170 lines
4.3 KiB
Markdown
170 lines
4.3 KiB
Markdown
# Self-Evolving Skill - OpenClaw集成指南
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## 安装完成 ✅
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### 文件位置
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| 位置 | 说明 |
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|------|------|
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| `~/.openclaw/skills/self-evolving-skill/` | 技能根目录 |
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| `~/.openclaw/agents/main/agent/mcp_servers.json` | MCP服务器配置 |
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| `~/.openclaw/skills/self-evolving-skill/storage/` | 数据存储 |
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### 项目结构
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```
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~/.openclaw/skills/self-evolving-skill/
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├── core/ # Python核心
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│ ├── residual_pyramid.py # SVD分解
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│ ├── reflection_trigger.py # 自适应触发
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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 SDK
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├── SKILL.md # 技能文档
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├── package.json # npm配置
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├── mcporter_adapter.py # mcporter适配器
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└── venv/ # Python虚拟环境
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```
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## 使用方式
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### 1. 直接调用（推荐）
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```bash
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# 激活虚拟环境
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source ~/.openclaw/skills/self-evolving-skill/venv/bin/activate
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# 列出所有Skill
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python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_list '{}'
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# 创建新Skill
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python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_create '{"name":"MySkill"}'
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# 分析嵌入
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python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_analyze '{"embedding":[0.1,0.2,0.3]}'
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# 系统统计
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python3 ~/.openclaw/skills/self-evolving-skill/mcporter_adapter.py skill_stats '{}'
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```
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### 2. OpenClaw MCP调用
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在OpenClaw中可直接调用MCP工具：
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```json
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{
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"tool": "skill_create",
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"arguments": {
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"name": "ProblemSolver",
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"description": "问题解决技能"
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}
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}
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```
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### 3. 启动MCP服务器
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```bash
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# 前台运行
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source ~/.openclaw/skills/self-evolving-skill/venv/bin/activate
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python3 ~/.openclaw/skills/self-evolving-skill/mcp_server.py --storage ~/.openclaw/skills/self-evolving-skill/storage
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# 或通过配置自动启动（已在mcp_servers.json中配置）
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```
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## MCP工具
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| 工具 | 描述 | 参数 |
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|------|------|------|
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| `skill_create` | 创建新的自演化Skill | `name`, `description` |
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| `skill_execute` | 执行Skill并触发学习 | `skill_id`, `context`, `success`, `value` |
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| `skill_analyze` | 分析嵌入向量（不触发学习） | `embedding` |
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| `skill_list` | 列出所有已保存的Skill | - |
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| `skill_stats` | 获取系统统计信息 | - |
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| `skill_save` | 持久化保存Skill | `skill_id` |
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| `skill_load` | 加载已保存的Skill | `skill_id` |
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| `skill_clear` | 清空所有数据和缓存 | - |
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## 测试结果
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```
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=== skill_list ===
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Skills: 20
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=== skill_create ===
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{"skill_id":"1ac4a2cb3f79347f","name":"TestOpenClaw"}
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=== skill_analyze ===
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{
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"total_energy": 0.55,
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"residual_ratio": 0.086,
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"suggested_abstraction": "POLICY",
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"novelty_score": 0.657
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}
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```
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## 核心算法
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### 1. 残差金字塔分解
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```python
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pyramid = ResidualPyramid(max_layers=5, use_pca=True)
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decomposition = pyramid.decompose(embedding)
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# 输出:
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# - residual_ratio: 残差能量比率
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# - suggested_abstraction: POLICY / SUB_SKILL / PREDICATE
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# - novelty_score: 综合新颖性
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```
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### 2. 三层跃迁规则
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| 覆盖率 | 抽象层级 | 操作 |
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|--------|---------|------|
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| >80% | POLICY | 调整策略权重 |
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| 40-80% | SUB_SKILL | 生成子Skill |
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| <40% | PREDICATE | 归纳新谓词 |
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### 3. 自适应阈值
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```python
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trigger = ReflectionTrigger(
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min_energy_ratio=0.10, # 初始阈值
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value_gain_threshold=0.20, # 触发阈值
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target_trigger_rate=0.15 # 目标15%触发率
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)
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```
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## OpenClaw配置
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MCP服务器已配置在：
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```json
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// ~/.openclaw/agents/main/agent/mcp_servers.json
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{
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"servers": {
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"self-evolving-skill": {
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"name": "self-evolving-skill",
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"type": "stdio",
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"command": "/bin/bash",
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"args": [
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"-c",
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"source ~/.openclaw/skills/self-evolving-skill/venv/bin/activate && python3 ~/.openclaw/skills/self-evolving-skill/mcp_server.py --storage ~/.openclaw/skills/self-evolving-skill/storage"
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]
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}
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}
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}
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```
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## 下一步
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- [ ] 在OpenClaw中测试MCP工具调用
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- [ ] 集成到Agent执行流程
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- [ ] 添加强化学习策略优化
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## 相关文档
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- [SKILL.md](SKILL.md) - 完整技能文档
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- [MEMORY.md](../../workspace/MEMORY.md) - 研究笔记
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