add all files

Author: qdriven

.github/workflows/deploy-static.yml

@@ -0,0 +1,57 @@
+name: Deploy Static Site to GitHub Pages
+
+on:
+  push:
+    branches: [main]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v4
+        with:
+          version: 10
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version: "20"
+
+      - name: Install dependencies
+        working-directory: playground
+        run: pnpm install
+
+      - name: Generate skills manifest
+        working-directory: playground/apps/desktop
+        run: node scripts/generate-skills-manifest.mjs
+
+      - name: Build static site
+        working-directory: playground/apps/desktop
+        run: pnpm build
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: playground/apps/desktop/out
+
+  deploy:
+    needs: build
+    runs-on: ubuntu-latest
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4

playground/apps/desktop/.git-workflow.state.json

@@ -0,0 +1,7 @@
+{
+  "issue": 15,
+  "repo": "variableway/innate-executable",
+  "first_comment_id": 4367777053,
+  "description": "1. \u4fee\u590d\u591a\u64cd\u4f5c\u7cfb\u7edf\u811a\u672c\u5207\u6362\u4e0d\u663e\u793a\u95ee\u9898\n2. \u6539\u5584 Markdown \u4e3b\u9898\u4e3a\u66f4\u6e29\u6696\u660e\u4eae\u7684\u98ce\u683c\n3. \u8bbe\u7f6e GitHub Actions \u90e8\u7f72\u9759\u6001\u7ad9\u70b9",
+  "title": "Task 18: Fix PlatformTabs + Theme + GitHub Actions"
+}

playground/apps/desktop/package.json

@@ -3,11 +3,14 @@
   "version": "0.1.0",
   "private": true,
   "scripts": {
+    "predev": "node scripts/generate-skills-manifest.mjs",
+    "prebuild": "node scripts/generate-skills-manifest.mjs",
     "dev": "next dev",
     "build": "next build",
     "start": "next start",
     "lint": "eslint",
-    "tauri": "tauri"
+    "tauri": "tauri",
+    "manifest": "node scripts/generate-skills-manifest.mjs"
   },
   "dependencies": {
     "@innate/ui": "workspace:*",

playground/apps/desktop/public/skills-manifest.json

@@ -1,5 +1,5 @@
 {
-  "generatedAt": "2026-05-04T01:39:08.447Z",
+  "generatedAt": "2026-05-04T03:49:20.647Z",
   "count": 14,
   "skills": [
     {
@@ -235,5 +235,90 @@
       "source": "builtin",
       "localPath": "/skills/install-nodejs.mdx"
     }
+  ],
+  "courses": [
+    {
+      "id": "ai-assistant-basics",
+      "title": "AI 助手入门系列",
+      "description": "从零开始学习使用 AI 助手，掌握搜索总结、邮件管理、日程安排和自定义技能",
+      "icon": "🤖",
+      "color": "#6366f1",
+      "skills": [
+        {
+          "slug": "openclaw-search-summarize",
+          "order": 1
+        },
+        {
+          "slug": "openclaw-email-calendar",
+          "order": 2
+        },
+        {
+          "slug": "openclaw-custom-skills",
+          "order": 3
+        },
+        {
+          "slug": "openclaw-5min-experience",
+          "order": 4
+        }
+      ]
+    },
+    {
+      "id": "ai-fundamentals",
+      "title": "AI 基础知识系列",
+      "description": "系统学习 AI 的跨学科基础：数学、认知科学、控制论和因果推断",
+      "icon": "🧠",
+      "color": "#8b5cf6",
+      "skills": [
+        {
+          "slug": "ai-cognitive-science",
+          "order": 1
+        },
+        {
+          "slug": "ai-math-basics-part1",
+          "order": 2
+        },
+        {
+          "slug": "ai-math-basics-part2",
+          "order": 3
+        },
+        {
+          "slug": "ai-interdisciplinary-journey",
+          "order": 4
+        },
+        {
+          "slug": "ai-cybernetics-complexity",
+          "order": 5
+        },
+        {
+          "slug": "ai-causal-inference",
+          "order": 6
+        }
+      ]
+    },
+    {
+      "id": "dev-tools-setup",
+      "title": "开发环境搭建系列",
+      "description": "一站式安装和配置 AI 编程工具：Claude Code、Node.js、Git、Kimi CLI",
+      "icon": "🛠️",
+      "color": "#f59e0b",
+      "skills": [
+        {
+          "slug": "install-claude-code-glm5-deepseek-v4",
+          "order": 1
+        },
+        {
+          "slug": "install-git",
+          "order": 2
+        },
+        {
+          "slug": "install-kimi-cli",
+          "order": 3
+        },
+        {
+          "slug": "install-nodejs",
+          "order": 4
+        }
+      ]
+    }
   ]
 }

playground/apps/desktop/public/skills/ai-causal-inference.mdx

@@ -162,7 +162,31 @@ AI 拒绝了贷款申请
 
 ## 用代码体验因果关系
 
-<RunnableCodeBlock code="python3 -c \"# 简单的因果 vs 相关演示\nimport random\nrandom.seed(42)\n\nn = 1000\ntemperature = [random.gauss(30, 5) for _ in range(n)]  # 温度\nice_cream = [t * 2 + random.gauss(0, 5) for t in temperature]  # 冰淇淋销量\nswimming = [t * 1.5 + random.gauss(0, 3) for t in temperature]  # 游泳人数\n\n# 计算相关系数\ndef corr(x, y):\n    n = len(x)\n    mx, my = sum(x)/n, sum(y)/n\n    cov = sum((a-mx)*(b-my) for a,b in zip(x,y)) / n\n    sx = (sum((a-mx)**2 for a in x) / n) ** 0.5\n    sy = (sum((b-my)**2 for b in y) / n) ** 0.5\n    return cov / (sx * sy)\n\nprint('相关系数：')\nprint(f'  冰淇淋 vs 游泳: {corr(ice_cream, swimming):.3f} (高度相关！)')\nprint(f'  但这不是因果关系 — 共同原因是温度')\nprint(f'  温度 vs 冰淇淋: {corr(temperature, ice_cream):.3f}')\nprint(f'  温度 vs 游泳:   {corr(temperature, swimming):.3f}')\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "
+# 简单的因果 vs 相关演示
+import random
+random.seed(42)
+
+n = 1000
+temperature = [random.gauss(30, 5) for _ in range(n)]  # 温度
+ice_cream = [t * 2 + random.gauss(0, 5) for t in temperature]  # 冰淇淋销量
+swimming = [t * 1.5 + random.gauss(0, 3) for t in temperature]  # 游泳人数
+
+# 计算相关系数
+def corr(x, y):
+    n = len(x)
+    mx, my = sum(x)/n, sum(y)/n
+    cov = sum((a-mx)*(b-my) for a,b in zip(x,y)) / n
+    sx = (sum((a-mx)**2 for a in x) / n) ** 0.5
+    sy = (sum((b-my)**2 for b in y) / n) ** 0.5
+    return cov / (sx * sy)
+
+print('相关系数：')
+print(f'  冰淇淋 vs 游泳: {corr(ice_cream, swimming):.3f} (高度相关！)')
+print(f'  但这不是因果关系 — 共同原因是温度')
+print(f'  温度 vs 冰淇淋: {corr(temperature, ice_cream):.3f}')
+print(f'  温度 vs 游泳:   {corr(temperature, swimming):.3f}')
+"`} language="python" />
 
 ---
 

playground/apps/desktop/public/skills/ai-cognitive-science.mdx

@@ -143,7 +143,18 @@ tags: ["path2agi", "认知科学", "神经科学", "入门"]
 
 一个最简单的人工神经元：
 
-<RunnableCodeBlock code="python3 -c \"# 简单的人工神经元：加权求和 + 激活函数\ndef neuron(inputs, weights, threshold=0.5):\n    total = sum(i * w for i, w in zip(inputs, weights))\n    return 1 if total > threshold else 0\n\n# 模拟 AND 逻辑门（两个输入都为1才输出1）\nprint('AND 逻辑门（模拟）：')\nfor a, b in [(0,0), (0,1), (1,0), (1,1)]:\n    result = neuron([a, b], [0.5, 0.5], threshold=0.75)\n    print(f'  输入: {a},{b} → 输出: {result}')\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "
+# 简单的人工神经元：加权求和 + 激活函数
+def neuron(inputs, weights, threshold=0.5):
+    total = sum(i * w for i, w in zip(inputs, weights))
+    return 1 if total > threshold else 0
+
+# 模拟 AND 逻辑门（两个输入都为1才输出1）
+print('AND 逻辑门（模拟）：')
+for a, b in [(0,0), (0,1), (1,0), (1,1)]:
+    result = neuron([a, b], [0.5, 0.5], threshold=0.75)
+    print(f'  输入: {a},{b} → 输出: {result}')
+"`} language="python" />
 
 ---
 

playground/apps/desktop/public/skills/ai-cybernetics-complexity.mdx

@@ -168,7 +168,28 @@ AI 处理的一切数据本质上都是**信号**：
 
 ## 用代码体验反馈系统
 
-<RunnableCodeBlock code="python3 -c \"# 简单的反馈控制系统：温度调节器\nclass Thermostat:\n    def __init__(self, target=25):\n        self.target = target\n        self.current = 15  # 初始温度\n        self.history = []\n\n    def step(self):\n        error = self.target - self.current\n        adjustment = error * 0.3  # 比例控制\n        noise = (hash(str(self.current)) % 10 - 5) * 0.1  # 环境噪声\n        self.current += adjustment + noise\n        self.history.append(round(self.current, 2))\n\nthermo = Thermostat(target=25)\nprint('温度调节器反馈过程：')\nfor i in range(15):\n    thermo.step()\n    status = '✓ 到达目标' if abs(thermo.current - 25) < 0.5 else '...'\n    print(f'  第{i+1}步: {thermo.current:.1f}°C {status}')\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "
+# 简单的反馈控制系统：温度调节器
+class Thermostat:
+    def __init__(self, target=25):
+        self.target = target
+        self.current = 15  # 初始温度
+        self.history = []
+
+    def step(self):
+        error = self.target - self.current
+        adjustment = error * 0.3  # 比例控制
+        noise = (hash(str(self.current)) % 10 - 5) * 0.1  # 环境噪声
+        self.current += adjustment + noise
+        self.history.append(round(self.current, 2))
+
+thermo = Thermostat(target=25)
+print('温度调节器反馈过程：')
+for i in range(15):
+    thermo.step()
+    status = '✓ 到达目标' if abs(thermo.current - 25) < 0.5 else '...'
+    print(f'  第{i+1}步: {thermo.current:.1f}°C {status}')
+"`} language="python" />
 
 ---
 

playground/apps/desktop/public/skills/ai-interdisciplinary-journey.mdx

@@ -156,7 +156,7 @@ AI 的 29 个基础学科可以归纳为四大能力区：
 
 用 Python 快速体验一下 AI 的"表示能力" — 把文字变成数字向量：
 
-<RunnableCodeBlock code="python3 -c \"print('AI 的本质：把世界变成数字')\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "print('AI 的本质：把世界变成数字')"`} language="python" />
 
 如果你还没安装 Python，不用担心，后续教程会教你安装。
 

playground/apps/desktop/public/skills/ai-math-basics-part1.mdx

@@ -58,7 +58,7 @@ AI 就是这样工作的：每看到一个新的数据点，就更新它的判
 
 让我们用 Python 模拟一个简单的概率计算：
 
-<RunnableCodeBlock code="python3 -c \"import random; results = [random.choice(['正面','反面']) for _ in range(10)]; print('抛10次硬币:', results); print('正面次数:', results.count('正面'))\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "import random; results = [random.choice(['正面','反面']) for _ in range(10)]; print('抛10次硬币:', results); print('正面次数:', results.count('正面'))"`} language="python" />
 
 ---
 
@@ -119,7 +119,14 @@ AI 判断两张图片是否相似，本质上是判断两个向量是否"接近"
 
 ### 用代码体验向量
 
-<RunnableCodeBlock code="python3 -c \"# 用列表模拟向量\nperson_a = [175, 70, 25]  # 身高, 体重, 年龄\nperson_b = [180, 75, 30]\nprint('人物A:', person_a)\nprint('人物B:', person_b)\nprint('差异:', [b-a for a,b in zip(person_a, person_b)])\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "
+# 用列表模拟向量
+person_a = [175, 70, 25]  # 身高, 体重, 年龄
+person_b = [180, 75, 30]
+print('人物A:', person_a)
+print('人物B:', person_b)
+print('差异:', [b-a for a,b in zip(person_a, person_b)])
+"`} language="python" />
 
 ---
 

playground/apps/desktop/public/skills/ai-math-basics-part2.mdx

@@ -63,7 +63,18 @@ AI 学习的过程，本质上就是**蒙着眼睛下山**：
 
 ### 用代码体验梯度下降
 
-<RunnableCodeBlock code="python3 -c \"# 简单的梯度下降演示\n# 找到函数 f(x) = x^2 的最小值（答案是 x=0）\nx = 5.0  # 起点\nlr = 0.1  # 学习率\nprint('梯度下降过程：')\nfor i in range(10):\n    grad = 2 * x  # f(x)=x^2 的导数是 2x\n    x = x - lr * grad\n    print(f'  第{i+1}步: x = {x:.4f}')\nprint(f'最终结果: x = {x:.4f} (理论最优解: x = 0)')\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "
+# 简单的梯度下降演示
+# 找到函数 f(x) = x^2 的最小值（答案是 x=0）
+x = 5.0  # 起点
+lr = 0.1  # 学习率
+print('梯度下降过程：')
+for i in range(10):
+    grad = 2 * x  # f(x)=x^2 的导数是 2x
+    x = x - lr * grad
+    print(f'  第{i+1}步: x = {x:.4f}')
+print(f'最终结果: x = {x:.4f} (理论最优解: x = 0)')
+"`} language="python" />
 
 ---
 
@@ -103,7 +114,20 @@ AI 学习的过程，本质上就是**蒙着眼睛下山**：
 
 ### 用代码体验信息熵
 
-<RunnableCodeBlock code="python3 -c \"import math\n# 计算抛硬币的熵\ndef entropy(p):\n    if p == 0 or p == 1:\n        return 0\n    return -p * math.log2(p) - (1-p) * math.log2(1-p)\n\nprint('不同概率下的信息熵：')\nfor p in [0.5, 0.7, 0.9, 0.99, 1.0]:\n    print(f'  正面概率={p:.0%} → 熵={entropy(p):.3f} bit')\nprint()\nprint('结论：越不确定，熵越高！')\"" language="python" />
+<RunnableCodeBlock code={`python3 -c "
+import math
+# 计算抛硬币的熵
+def entropy(p):
+    if p == 0 or p == 1:
+        return 0
+    return -p * math.log2(p) - (1-p) * math.log2(1-p)
+
+print('不同概率下的信息熵：')
+for p in [0.5, 0.7, 0.9, 0.99, 1.0]:
+    print(f'  正面概率={p:.0%} → 熵={entropy(p):.3f} bit')
+print()
+print('结论：越不确定，熵越高！')
+"`} language="python" />
 
 ---