How do I install the Self Evolver skill?

Run npx skillmds@latest add penghang1223/self-evolver in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Self Evolver skill do?

年年自我进化引擎 — 自我监控、Agent匹配、任务分解、决策追踪。让年年持续变强的元技能。 It is listed under AI & ML on SkillMD.

Is Self Evolver safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Self Evolver?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Self Evolver free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Self Evolver?

penghang1223 (@penghang1223) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Self Evolver by penghang1223 — Agent Skill

Name: Self Evolver
Author: penghang1223

File contents

Self-Evolver — 年年自我进化引擎

核心模块

1. 自我监控 (`self_monitor.py`)

每次心跳自动检查年年的状态：

python3 <SKILL_DIR>/scripts/self_monitor.py

检查项：

📚 学习连续性 — 今日是否有学习记录
📁 文件健康度 — 关键文件是否完整
🔄 错误模式 — 是否重复犯错
🤝 协调效率 — 学习汇报数量

2. Agent匹配器 (`agent_matcher.py`)

自动分析任务类型，匹配最适合的Agent：

python3 <SKILL_DIR>/scripts/agent_matcher.py

from scripts.agent_matcher import match_agent
result = match_agent("写React组件")
# Returns: {"agent_id": "frontend_dev", "name": "霓裳", "score": 9.2}

3. 任务分解器 (`task_decomposer.py`)

自动分析任务依赖，生成波次执行计划：

python3 <SKILL_DIR>/scripts/task_decomposer.py "开发登录系统"

输出：

waves:
  - wave: 1 (并行)
    - 数据库模型设计 → 玄机
    - JWT配置方案 → 玄机
    - 测试用例框架 → 鉴微
  - wave: 2 (依赖Wave 1)
    - 认证API开发 → 玄机
    - 登录页UI → 霓裳
  - wave: 3 (依赖Wave 2)
    - 集成测试 → 鉴微

4. 决策追踪器 (`decision_tracker.py`)

记录决策依据，支持复盘：

python3 <SKILL_DIR>/scripts/decision_tracker.py record "选择React而非Vue" "团队熟悉度+生态"
python3 <SKILL_DIR>/scripts/decision_tracker.py review

使用场景

心跳时：运行自我监控，发现问题自动修复
分配任务时：运行Agent匹配器，找到最佳Agent
复杂项目时：运行任务分解器，生成波次计划
做决策时：运行决策追踪器，记录依据备复盘

文件

scripts/self_monitor.py — 自我监控
scripts/agent_matcher.py — Agent匹配
scripts/task_decomposer.py — 任务分解
scripts/decision_tracker.py — 决策追踪

---name: self-evolverdescription: 年年自我进化引擎 — 自我监控、Agent匹配、任务分解、决策追踪。让年年持续变强的元技能。---# Self-Evolver — 年年自我进化引擎## 核心模块### 1. 自我监控 (`self_monitor.py`)每次心跳自动检查年年的状态：```bashpython3 <SKILL_DIR>/scripts/self_monitor.py```检查项：- 📚 学习连续性 — 今日是否有学习记录- 📁 文件健康度 — 关键文件是否完整- 🔄 错误模式 — 是否重复犯错- 🤝 协调效率 — 学习汇报数量### 2. Agent匹配器 (`agent_matcher.py`)自动分析任务类型，匹配最适合的Agent：```bashpython3 <SKILL_DIR>/scripts/agent_matcher.py``````pythonfrom scripts.agent_matcher import match_agentresult = match_agent("写React组件")# Returns: {"agent_id": "frontend_dev", "name": "霓裳", "score": 9.2}```### 3. 任务分解器 (`task_decomposer.py`)自动分析任务依赖，生成波次执行计划：```bashpython3 <SKILL_DIR>/scripts/task_decomposer.py "开发登录系统"```输出：```yamlwaves: - wave: 1 (并行) - 数据库模型设计 → 玄机 - JWT配置方案 → 玄机 - 测试用例框架 → 鉴微 - wave: 2 (依赖Wave 1) - 认证API开发 → 玄机 - 登录页UI → 霓裳 - wave: 3 (依赖Wave 2) - 集成测试 → 鉴微```### 4. 决策追踪器 (`decision_tracker.py`)记录决策依据，支持复盘：```bashpython3 <SKILL_DIR>/scripts/decision_tracker.py record "选择React而非Vue" "团队熟悉度+生态"python3 <SKILL_DIR>/scripts/decision_tracker.py review```## 使用场景- **心跳时**：运行自我监控，发现问题自动修复- **分配任务时**：运行Agent匹配器，找到最佳Agent- **复杂项目时**：运行任务分解器，生成波次计划- **做决策时**：运行决策追踪器，记录依据备复盘## 文件- `scripts/self_monitor.py` — 自我监控- `scripts/agent_matcher.py` — Agent匹配- `scripts/task_decomposer.py` — 任务分解- `scripts/decision_tracker.py` — 决策追踪

Frequently asked questions

Self Evolver