151 lines
3.9 KiB
Markdown
151 lines
3.9 KiB
Markdown
---
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name: Self-Evolving Skill
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description: "元认知自我学习系统——自动技能进化。"
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homepage: https://github.com/whtoo/self-evolving-bot
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---
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# Self-Evolving Skill
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元认知自学习系统 - 基于预测编码和价值驱动的Skill自动演化。
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## 功能
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- **ResidualPyramid金字塔分解，量化认知缺口
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-**: 残差 **自适应反思触发**: 基于残差能量自动判断何时需要学习
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- **经验回放**: 缓存已学模式，降低重复触发
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- **价值门控**: 只有提升长期价值才接受变异
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- **持久化**: 经验自动保存/加载
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## 安装
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```bash
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# 技能已安装到 ~/.openclaw/skills/self-evolving-skill
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# 或使用ClawHub
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clawhub install self-evolving-skill
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```
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## 架构
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```
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self-evolving-skill/
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├── core/ # Python核心
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│ ├── residual_pyramid.py # 残差金字塔（SVD分解）
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│ ├── reflection_trigger.py # 自适应触发器
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│ ├── experience_replay.py # 经验回放缓存
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│ ├── skill_engine.py # 核心引擎+ValueGate
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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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│ ├── index.ts # 主入口
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│ ├── cli.ts # CLI
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│ └── mcp-tools.ts # 工具定义
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├── skills/ # OpenClaw Skill
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│ └── self-evolving-skill/ # 技能封装
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├── MCP_CONFIG.md # MCP配置
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└── README.md # 文档
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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Skill | `name`, `description` |
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| `skill_execute` | 执行并学习 | `skill_id`, `context`, `success`, `value` |
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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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## 使用方式
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### CLI
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```bash
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# 列出所有Skill
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openclaw skill self-evolving-skill list
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# 创建Skill
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openclaw skill self-evolving-skill create --name "MySkill"
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# 执行
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openclaw skill self-evolving-skill execute <id> --success
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# 分析
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openclaw skill self-evolving-skill analyze --embedding '[0.1,0.2,...]'
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# 统计
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openclaw skill self-evolving-skill stats
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```
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### MCP服务器
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```bash
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# 启动MCP服务器
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cd ~/.openclaw/skills/self-evolving-skill
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./run_mcp.sh
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# 或使用适配器
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python3 mcporter_adapter.py skill_list '{}'
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```
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### 编程
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```typescript
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import { SelfEvolvingSkillEngine } from 'self-evolving-skill';
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const engine = new SelfEvolvingSkillEngine();
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await engine.init();
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const { skillId } = await engine.createSkill({ name: 'Analyzer' });
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const stats = await engine.stats();
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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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## 文件位置
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| 路径 | 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/mcp_servers/self-evolving-skill.json` | MCP服务器配置 |
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| `~/.openclaw/workspace/self-evolving-skill/storage` | 数据存储 |
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## 相关文档
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- [README.md](./README.md) - 完整文档
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- [MCP_CONFIG.md](./MCP_CONFIG.md) - MCP配置说明
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- [MEMORY.md](../MEMORY.md) - 研究笔记
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