103 lines
2.5 KiB
Markdown
103 lines
2.5 KiB
Markdown
# Self-Evolving Skill - OpenClaw集成
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## 项目结构
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```
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self-evolving-skill/
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├── core/ # Python核心模块
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│ ├── residual_pyramid.py # 残差金字塔分解
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│ ├── reflection_trigger.py # 自适应触发器
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│ ├── experience_replay.py # 经验回放
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│ ├── skill_engine.py # 核心引擎
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│ ├── storage.py # 持久化
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│ └── mcp_server.py # MCP服务器
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├── src/ # TypeScript封装
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│ ├── index.ts # 主入口
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│ ├── cli.ts # CLI
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│ └── mcp-tools.ts # MCP工具定义
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├── skills/ # 供OpenClaw调用
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│ └── self-evolving-skill/ # OpenClaw Skill
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├── SKILL.md # 技能文档
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├── package.json
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└── README.md
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```
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## 安装到OpenClaw
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```bash
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# 方式1: 链接到OpenClaw skills目录
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cd skills/self-evolving-skill
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npm install
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npm run build
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# 链接
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ln -s $(pwd)/skills/self-evolving-skill ~/.openclaw/skills/self-evolving-skill
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# 方式2: 通过ClawHub
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clawhub install self-evolving-skill
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```
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## OpenClaw中调用
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```typescript
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// 直接调用MCP工具
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const result = await useTool('skill_create', {
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name: 'ProblemSolver'
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});
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const analysis = await useTool('skill_analyze', {
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embedding: [0.1, 0.2, 0.3, ...]
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});
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```
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## MCP工具列表
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| 工具 | 描述 | 参数 |
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|------|------|------|
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| `skill_create` | 创建Skill | `name`, `description` |
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| `skill_execute` | 执行并学习 | `skill_id`, `context`, `success` |
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| `skill_analyze` | 分析嵌入 | `embedding` |
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| `skill_list` | 列出Skills | - |
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| `skill_stats` | 系统统计 | - |
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| `skill_save` | 持久化保存 | `skill_id` |
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| `skill_load` | 加载 | `skill_id` |
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## 示例
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```typescript
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// 1. 创建Skill
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const skill = await useTool('skill_create', {
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name: 'TextAnalyzer',
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description: '文本分析自学习Skill'
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});
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// 2. 执行并观察学习
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const result = await useTool('skill_execute', {
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skill_id: skill.skill_id,
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context: { task: 'sentiment' },
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success: true,
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value: 1.0
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});
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console.log('反思触发:', result.reflection_triggered);
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// 3. 分析新输入
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const analysis = await useTool('skill_analyze', {
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embedding: generateEmbedding(text)
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});
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```
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## 配置
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在OpenClaw配置文件中：
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```yaml
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skills:
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self-evolving-skill:
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max_layers: 5
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energy_threshold: 0.1
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similarity_threshold: 0.85
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target_trigger_rate: 0.15
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storage_dir: ~/.openclaw/self-evolving
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```
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