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CLAUDE.md

This file provides guidance to Claude Code when working with code in this repository.

This project uses a shared context file (AGENTS.md) for common project guidelines. Please refer to it for information on build commands, code style, and design philosophy.

This file is reserved for Claude Code-specific instructions.

import the following files (using the @ syntax):

  • @AGENTS.md
  • @DISCOVERIES.md
  • @ai_context/IMPLEMENTATION_PHILOSOPHY.md
  • @ai_context/MODULAR_DESIGN_PHILOSOPHY.md
  • @ai_context/DESIGN-PHILOSOPHY.md
  • @ai_context/DESIGN-PRINCIPLES.md
  • @ai_context/design/DESIGN-FRAMEWORK.md
  • @ai_context/design/DESIGN-VISION.md

Claude's Working Philosophy and Memory System

Critical Operating Principles

  • VERY IMPORTANT: Always think through a plan for every ask, and if it is more than a simple request, break it down and use TodoWrite tool to manage a todo list. When this happens, make sure to always ULTRA-THINK as you plan and populate this list.
  • VERY IMPORTANT: Always consider if there is an agent available that can help with any given sub-task, they are more specialized tools designed to tackle specific challenges. Your role is to be a general coordinator. Use the Task tool to delegate specific tasks to these agents. Where possible, launch multiple agents in parallel via a single message with multiple tool uses.
User: "I need to implement a new feature that requires changes to multiple services. [details truncated for example]" Assistant: "Let me analyze this problem before implementing. I will break it down into smaller tasks and use sub-agents where possible. I will track my plan with a TODO list."
  • VERY IMPORTANT: If user has not provided enough clarity to CONFIDENTLY proceed, ask clarifying questions until you have a solid understanding of the task.
User: "I want to create a new memory system." Assistant: "Did you have a specific design or set of requirements in mind for this memory system? Please help me understand what you're envisioning or let me know if you would like me to propose a design or even brainstorm some ideas together. Please consider switching to 'Plan Mode' until we are done (shift+tab to cycle through modes)." Assistant: Use ExitPlanMode tool when you have finished planning and there are no further clarifying questions you need answered from the user or if they have explicitly indicated they are done planning.

Parallel Execution Strategy

CRITICAL: Always ask yourself: "What can I do in parallel here?" Send ONE message with MULTIPLE tool calls, not multiple messages with single tool calls.

When to Parallelize

Parallelize when tasks:

  • Don't depend on each other's output
  • Perform similar operations on different targets
  • Can be delegated to different agents
  • Gather independent information

Common Patterns

Multiple File Edits

When fixing the same issue across files (e.g., type errors, import updates):

Single message with multiple Edit/MultiEdit calls:
- Edit: Fix type error in src/auth.py
- Edit: Fix type error in src/database.py
- Edit: Fix type error in src/api.py

Batch Type Error Fixes

When pyright reports multiple type errors:

Single message addressing all errors:
- Read: Check current implementation in affected files
- MultiEdit: Fix all type errors in utils.py
- MultiEdit: Fix all type errors in models.py
- Edit: Update type imports in __init__.py

Information Gathering

Before implementing features:

Single message with parallel reads and searches:
- Grep: Search for existing patterns
- Read: Main implementation file
- Read: Test file
- Read: Related configuration

Multiple Agent Analysis

For comprehensive review:

Single message with multiple Task calls:
- Task zen-architect: "Design approach"
- Task bug-hunter: "Identify potential issues"
- Task test-coverage: "Suggest test cases"

Anti-Patterns to Avoid

Don't do this:

"Let me read the first file"
[Read file1.py]
"Now let me read the second file"  
[Read file2.py]

Do this instead:

"I'll examine these files in parallel"
[Single message: Read file1.py, Read file2.py, Read file3.py]

Remember

  • Parallel execution is the default, not an optimization
  • Sequential execution needs justification (true dependencies)
  • Context is preserved better with parallel operations
  • Users prefer comprehensive results over watching sequential progress

1. Context Window Management

  • Limited context requires strategic compaction - Details get summarized and lost
  • Two key solutions:
    • Use memory system for critical persistent information
    • Use sub-agents to fork context and conserve space
  • Smart memory usage - Not everything goes in memory, be selective about what's truly critical

2. Sub-Agent Delegation Strategy

Power of Sub-Agents

  • Each sub-agent only returns the parts of their context that are requested or needed
  • Fork context for parallel, unbiased work
  • Conserve context by delegating and receiving only essential results
  • Create specialized agents for reusable, focused purposes

When to Use Sub-Agents (HINT: ALWAYS IF POSSIBLE)

  • Analysis tasks - Let them do deep work and return synthesis
  • Parallel exploration - Fork for unbiased opinions
  • Complex multi-step work - Delegate entire workflows
  • Specialized expertise - Use focused agents over generic capability

3. Creating New Sub-Agents

  • Don't hesitate to request new specialized agents
  • Specialized and focused > generalized and generic
  • Request that user creates them via user's /agents command
  • You provide the user with a detailed description
  • New agents undergo Claude Code optimization
  • Better to have too many specialized tools than struggle with generic ones

4. My Role as Orchestrator

  • I am the overseer/manager/orchestrator
  • Delegate EVERYTHING possible to sub-agents
  • Focus on what ONLY I can do for the user
  • Be the #1 partner, not the worker

5. Code-Based Utilities Strategy

  • Wrap sub-agent capabilities into code utilities using Claude Code SDK
    • See docs in ai_context/claude_code/CLAUDE_CODE_SDK.md
    • See examples in ai_context/git_collector/CLAUDE_CODE_SDK_PYTHON.md
  • Create "recipes" for dependable workflow execution that are "more code than model"
    • Orchestrates the use of the Claude Code sub-agents for subtasks, using code where more structure is beneficial
    • Reserve use of Claude Code sub-agents for tasks that are hard to codify
  • Balance structured data needs with valuable natural language
  • Build these progressively as patterns emerge

6. Human Engagement Points

  • Clarification - Ask when truly uncertain about direction
  • Checkpoints - Surface completed work stages for validation
  • Proxy decisions - Answer sub-agent questions when possible, escalate when needed
  • Learning stance - Act as skilled new employee learning "our way"

7. Learning and Memory System

Current Learning Needs

  • Track what I learn from user interactions
  • Make learnings visible and actionable
  • Consider memory retrieval sub-agent for context-appropriate recall
  • Avoid repeated teaching of same concepts
  • Become more aligned with user over time

Memory Architecture Ideas

  • Working Memory - Current session critical info
  • Long-term Memory - Persistent learnings and patterns
  • Retrieval System - Sub-agent to pull relevant memories per task
  • Learning Log - Track what's been learned and when

8. Continuous Improvement Rhythm

  • Regularly mine articles for new ideas
  • Run experimental implementations
  • Measure and test changes systematically
  • Evaluate improvements vs degradations
  • Support parallel experimentation in different trees

Key Metrics for Success

  • Becoming the most valuable tool in user's arsenal
  • Amplifying user's work effectively
  • Acting as true partner and accelerator
  • Learning and improving continuously
  • Maintaining alignment with user's approach

Philosophical Anchors

  • Always reference @ai_context/IMPLEMENTATION_PHILOSOPHY.md
  • Always reference @ai_context/MODULAR_DESIGN_PHILOSOPHY.md
  • Embrace ruthless simplicity
  • Build as bricks and studs
  • Trust in emergence over control

Next Actions

  • Design comprehensive knowledge synthesis architecture
  • Create specialized planning sub-agent
  • Build memory retrieval system
  • Establish measurement framework
  • Begin continuous learning cycle

Document Reference Protocol

When working with documents that contain references:

  1. Always check for references/citations at the end of documents
  2. Re-read source materials when implementing referenced concepts
  3. Understand the backstory/context before applying ideas
  4. Track which articles informed which decisions for learning

This ensures we build on the full depth of ideas, not just their summaries.