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Meta-cognitive self-learning system - Automated skill evolution based on predictive coding and value-driven mechanisms.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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| name | Self-Evolving Skill |
| description | Meta-cognitive self-learning system - Automated skill evolution based on predictive coding and value-driven mechanisms. |
| homepage | https://github.com/whtoo/self-evolving-bot |
| status | ⏸️ 待配置触发 |
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。
# 技能已安装到 ~/.openclaw/skills/self-evolving-skill
# 或使用ClawHub
clawhub install self-evolving-skill
self-evolving-skill/
├── core/ # Python核心
│ ├── residual_pyramid.py # 残差金字塔（SVD分解）
│ ├── reflection_trigger.py # 自适应触发器
│ ├── experience_replay.py # 经验回放缓存
│ ├── skill_engine.py # 核心引擎+ValueGate
│ ├── storage.py # 持久化
│ └── mcp_server.py # MCP服务器
├── src/ # TypeScript SDK
│ ├── index.ts # 主入口
│ ├── cli.ts # CLI
│ └── mcp-tools.ts # 工具定义
├── skills/ # OpenClaw Skill
│ └── self-evolving-skill/ # 技能封装
├── MCP_CONFIG.md # MCP配置
└── README.md # 文档
| 工具 | 描述 | 参数 |
|---|---|---|
skill_create | 创建Skill | name, description |
skill_execute | 执行并学习 | skill_id, context, success, value |
skill_analyze | 分析嵌入 | embedding |
skill_list | 列出Skills | - |
skill_stats | 系统统计 | - |
skill_save | 持久化保存 | skill_id |
skill_load | 加载 | skill_id |
# 列出所有Skill
openclaw skill self-evolving-skill list
# 创建Skill
openclaw skill self-evolving-skill create --name "MySkill"
# 执行
openclaw skill self-evolving-skill execute <id> --success
# 分析
openclaw skill self-evolving-skill analyze --embedding '[0.1,0.2,...]'
# 统计
openclaw skill self-evolving-skill stats
# 启动MCP服务器
cd ~/.openclaw/skills/self-evolving-skill
./run_mcp.sh
# 或使用适配器
python3 mcporter_adapter.py skill_list '{}'
import { SelfEvolvingSkillEngine } from 'self-evolving-skill';
const engine = new SelfEvolvingSkillEngine();
await engine.init();
const { skillId } = await engine.createSkill({ name: 'Analyzer' });
const stats = await engine.stats();
pyramid = ResidualPyramid(max_layers=5, use_pca=True)
decomposition = pyramid.decompose(embedding)
# 输出:
# - residual_ratio: 残差能量比率
# - suggested_abstraction: POLICY / SUB_SKILL / PREDICATE
# - novelty_score: 综合新颖性
| 覆盖率 | 抽象层级 | 操作 |
|---|---|---|
| >80% | POLICY | 调整策略权重 |
| 40-80% | SUB_SKILL | 生成子Skill |
| <40% | PREDICATE | 归纳新谓词 |
trigger = ReflectionTrigger(
min_energy_ratio=0.10, # 初始阈值
value_gain_threshold=0.20, # 触发阈值
target_trigger_rate=0.15 # 目标15%触发率
)
| 路径 | 说明 |
|---|---|
~/.openclaw/skills/self-evolving-skill | 技能根目录 |
~/.openclaw/mcp_servers/self-evolving-skill.json | MCP服务器配置 |
~/.openclaw/workspace/self-evolving-skill/storage | 数据存储 |