AI代理自我提升技能：记录错误与学习，实现持续改进的智能编码助手

self-improvement by pskoett/self-improving-agent

5,800 周安装量

7 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/pskoett/self-improving-agent --skill self-improvement

AI/机器学习开发生产力

🇨🇳中文介绍

自我提升技能

将学习和错误记录到 Markdown 文件中，以实现持续改进。编码代理稍后可以将这些处理成修复方案，重要的学习内容会被提升到项目记忆中。

快速参考

情境	操作
命令/操作失败	记录到 `.learnings/ERRORS.md`
用户纠正你	记录到 `.learnings/LEARNINGS.md`，类别为 `correction`
用户需要缺失的功能	记录到 `.learnings/FEATURE_REQUESTS.md`
API/外部工具失败	记录到 `.learnings/ERRORS.md`，包含集成详细信息
知识已过时

广告位招租

在这里展示您的产品或服务

触达数万 AI 开发者，精准高效

联系我们

OpenClaw 设置（推荐）

OpenClaw 是此技能的主要平台。它使用基于工作区的提示注入，并自动加载技能。

通过 ClawdHub（推荐）：

clawdhub install self-improving-agent

git clone https://github.com/peterskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent

OpenClaw 将以下文件注入到每个会话中：

~/.openclaw/workspace/
├── AGENTS.md          # 多代理工作流程、委托模式
├── SOUL.md            # 行为准则、个性、原则
├── TOOLS.md           # 工具能力、集成注意事项
├── MEMORY.md          # 长期记忆（仅主会话）
├── memory/            # 每日记忆文件
│   └── YYYY-MM-DD.md
└── .learnings/        # 此技能的日志文件
    ├── LEARNINGS.md
    ├── ERRORS.md
    └── FEATURE_REQUESTS.md

mkdir -p ~/.openclaw/workspace/.learnings

然后创建日志文件（或从 assets/ 复制）：

LEARNINGS.md — 纠正、知识差距、最佳实践
ERRORS.md — 命令失败、异常
FEATURE_REQUESTS.md — 用户请求的功能

当学习内容被证明广泛适用时，将其提升到工作区文件：

学习类型	提升到	示例
行为模式	`SOUL.md`	"保持简洁，避免免责声明"
工作流程改进	`AGENTS.md`	"为长任务生成子代理"
工具注意事项	`TOOLS.md`	"Git push 需要先配置认证"

OpenClaw 提供工具在会话间共享学习内容：

sessions_list — 查看活动/最近的会话
sessions_history — 读取另一个会话的转录
sessions_send — 向另一个会话发送学习内容
sessions_spawn — 生成子代理进行后台工作

可选：启用钩子

在会话开始时自动提醒：

# 将钩子复制到 OpenClaw 钩子目录
cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement

# 启用它
openclaw hooks enable self-improvement

完整详细信息请参阅 references/openclaw-integration.md。

通用设置（其他代理）

对于 Claude Code、Codex、Copilot 或其他代理，在项目中创建 .learnings/：

mkdir -p .learnings

从 assets/ 复制模板或创建带标题的文件。

追加到 .learnings/LEARNINGS.md：

## [LRN-YYYYMMDD-XXX] category

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
One-line description of what was learned

### Details
Full context: what happened, what was wrong, what's correct

### Suggested Action
Specific fix or improvement to make

### Metadata
- Source: conversation | error | user_feedback
- Related Files: path/to/file.ext
- Tags: tag1, tag2
- See Also: LRN-20250110-001 (if related to existing entry)

---

追加到 .learnings/ERRORS.md：

## [ERR-YYYYMMDD-XXX] skill_or_command_name

**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
Brief description of what failed

### Error

Actual error message or output

### Context
- Command/operation attempted
- Input or parameters used
- Environment details if relevant

### Suggested Fix
If identifiable, what might resolve this

### Metadata
- Reproducible: yes | no | unknown
- Related Files: path/to/file.ext
- See Also: ERR-20250110-001 (if recurring)

---

追加到 .learnings/FEATURE_REQUESTS.md：

## [FEAT-YYYYMMDD-XXX] capability_name

**Logged**: ISO-8601 timestamp
**Priority**: medium
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Requested Capability
What the user wanted to do

### User Context
Why they needed it, what problem they're solving

### Complexity Estimate
simple | medium | complex

### Suggested Implementation
How this could be built, what it might extend

### Metadata
- Frequency: first_time | recurring
- Related Features: existing_feature_name

---

格式：TYPE-YYYYMMDD-XXX

TYPE: LRN（学习）、ERR（错误）、FEAT（功能）
YYYYMMDD: 当前日期
XXX: 顺序号或随机 3 个字符（例如 001、A7B）

示例：LRN-20250115-001、ERR-20250115-A3F、FEAT-20250115-002

当问题被修复时，更新条目：

将 **Status**: pending 改为 **Status**: resolved
在元数据后添加解决块：

- **Resolved**: 2025-01-16T09:00:00Z
- **Commit/PR**: abc123 or #42
- **Notes**: Brief description of what was done

in_progress - 正在积极处理中
wont_fix - 决定不解决（在解决说明中添加原因）
promoted - 已提升到 CLAUDE.md、AGENTS.md 或 .github/copilot-instructions.md

提升到项目记忆

当学习内容广泛适用（不是一次性修复）时，将其提升到永久的项目记忆中。

学习内容适用于多个文件/功能
任何贡献者（人类或 AI）都应该知道的知识
防止重复出现的错误
记录项目特定的约定

目标	应包含的内容
`CLAUDE.md`	项目事实、约定、所有 Claude 交互的注意事项
`AGENTS.md`	代理特定的工作流程、工具使用模式、自动化规则
`.github/copilot-instructions.md`	GitHub Copilot 的项目上下文和约定
`SOUL.md`	行为准则、沟通风格、原则（OpenClaw 工作区）
`TOOLS.md`	工具能力、使用模式、集成注意事项（OpenClaw 工作区）

提炼学习内容为简洁的规则或事实
添加到目标文件的适当部分（如果需要则创建文件）
更新原始条目：
- 将 **Status**: pending 改为 **Status**: promoted
- 添加 **Promoted**: CLAUDE.md、AGENTS.md 或 .github/copilot-instructions.md

学习内容（详细）：

项目使用 pnpm 工作区。尝试了 npm install 但失败了。锁文件是 pnpm-lock.yaml。必须使用 pnpm install。

在 CLAUDE.md 中（简洁）：

## Build & Dependencies
- Package manager: pnpm (not npm) - use `pnpm install`

学习内容（详细）：

修改 API 端点时，必须重新生成 TypeScript 客户端。忘记这一点会导致运行时的类型不匹配。

在 AGENTS.md 中（可操作）：

## After API Changes
1. Regenerate client: `pnpm run generate:api`
2. Check for type errors: `pnpm tsc --noEmit`

如果记录的内容与现有条目类似：

先搜索：grep -r "keyword" .learnings/
链接条目：在元数据中添加 **See Also**: ERR-20250110-001
提升优先级 如果问题持续出现
考虑系统性修复：重复出现的问题通常表明：
- 缺少文档（→ 提升到 CLAUDE.md 或 .github/copilot-instructions.md）
- 缺少自动化（→ 添加到 AGENTS.md）
- 架构问题（→ 创建技术债务工单）

在自然的断点处审查 .learnings/：

开始新的主要任务之前
完成一个功能之后
在处理有过去学习内容的领域时
在活跃开发期间每周

# Count pending items
grep -h "Status\*\*: pending" .learnings/*.md | wc -l

# List pending high-priority items
grep -B5 "Priority\*\*: high" .learnings/*.md | grep "^## \["

# Find learnings for a specific area
grep -l "Area\*\*: backend" .learnings/*.md

解决已修复的项目
提升适用的学习内容
链接相关条目
上报重复出现的问题

当你注意到以下情况时自动记录：

纠正（→ 类别为 correction 的学习）：

"不，那不对..."
"实际上，应该是..."
"你关于...是错误的"
"那个已经过时了..."

功能请求（→ 功能请求）：

"你还能..."
"我希望你能..."
"有没有办法..."
"为什么你不能..."

知识差距（→ 类别为 knowledge_gap 的学习）：

用户提供你不知道的信息
你参考的文档已过时
API 行为与你的理解不同

错误（→ 错误条目）：

命令返回非零退出码
异常或堆栈跟踪
意外的输出或行为
超时或连接失败

优先级	何时使用
`critical`	阻塞核心功能、数据丢失风险、安全问题
`high`	重大影响、影响常见工作流程、重复出现的问题
`medium`	中等影响、存在变通方法
`low`	轻微不便、边缘情况、锦上添花

用于按代码库区域筛选学习内容：

区域	范围
`frontend`	UI、组件、客户端代码
`backend`	API、服务、服务器端代码
`infra`	CI/CD、部署、Docker、云
`tests`	测试文件、测试工具、覆盖率
`docs`	文档、注释、README
`config`	配置文件、环境、设置

立即记录 - 问题发生后上下文最新鲜
具体明确 - 未来的代理需要快速理解
包含重现步骤 - 特别是对于错误
链接相关文件 - 使修复更容易
建议具体修复 - 不仅仅是"调查"
使用一致的类别 - 支持筛选
积极提升 - 如果有疑问，添加到 CLAUDE.md 或 .github/copilot-instructions.md
定期审查 - 过时的学习内容会失去价值

保持学习内容本地化（每个开发者）：

在仓库中跟踪学习内容（团队范围）：不要添加到 .gitignore - 学习内容成为共享知识。

混合模式（跟踪模板，忽略条目）：

.learnings/*.md
!.learnings/.gitkeep

通过代理钩子启用自动提醒。这是选择加入的 - 你必须明确配置钩子。

快速设置（Claude Code / Codex）

在项目中创建 .claude/settings.json：

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/activator.sh"
      }]
    }]
  }
}

这会在每个提示后注入学习评估提醒（约 50-100 个令牌的开销）。

完整设置（带错误检测）

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/activator.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "Bash",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/error-detector.sh"
      }]
    }]
  }
}

可用的钩子脚本

脚本	钩子类型	目的
`scripts/activator.sh`	UserPromptSubmit	提醒在任务后评估学习内容
`scripts/error-detector.sh`	PostToolUse (Bash)	在命令错误时触发

详细配置和故障排除请参阅 references/hooks-setup.md。

当学习内容足够有价值，可以成为可重用技能时，使用提供的助手进行提取。

当满足以下任何条件时，学习内容有资格进行技能提取：

标准	描述
重复出现	有 `See Also` 链接指向 2 个以上类似问题
已验证	状态为 `resolved` 且有有效的修复方案
非显而易见	需要实际调试/调查才能发现
广泛适用	不特定于项目；跨代码库有用
用户标记	用户说"将此保存为技能"或类似内容

识别候选：学习内容满足提取标准

运行助手（或手动创建）：

./skills/self-improvement/scripts/extract-skill.sh skill-name --dry-run
./skills/self-improvement/scripts/extract-skill.sh skill-name

自定义 SKILL.md：用学习内容填充模板
更新学习内容：将状态设置为 promoted_to_skill，添加 Skill-Path
验证：在新会话中阅读技能以确保其自包含

如果你更喜欢手动创建：

创建 skills/<skill-name>/SKILL.md
使用 assets/SKILL-TEMPLATE.md 中的模板
遵循 Agent Skills 规范：
- 带有 name 和 description 的 YAML 前置元数据
- 名称必须与文件夹名称匹配
- 技能文件夹内没有 README.md

提取检测触发器

注意这些信号，表明学习内容应该成为技能：

"将此保存为技能"
"我经常遇到这个问题"
"这对其他项目会有用"
"记住这个模式"

在学习条目中：

多个 See Also 链接（重复出现的问题）
高优先级 + 已解决状态
类别：具有广泛适用性的 best_practice
用户反馈赞扬解决方案

在提取之前，验证：

解决方案经过测试且有效
描述清晰，无需原始上下文
代码示例自包含
没有项目特定的硬编码值
遵循技能命名约定（小写，连字符）

此技能通过代理特定的激活方式，在不同的 AI 编码代理中工作。

激活：钩子（UserPromptSubmit、PostToolUse）设置：带有钩子配置的 .claude/settings.json 检测：通过钩子脚本自动

激活：钩子（与 Claude Code 相同的模式）设置：带有钩子配置的 .codex/settings.json 检测：通过钩子脚本自动

激活：手动（不支持钩子）设置：添加到 .github/copilot-instructions.md：

## Self-Improvement

After solving non-obvious issues, consider logging to `.learnings/`:
1. Use format from self-improvement skill
2. Link related entries with See Also
3. Promote high-value learnings to skills

Ask in chat: "Should I log this as a learning?"

检测：会话结束时手动审查

激活：工作区注入 + 代理间消息传递设置：参见上面的"OpenClaw 设置"部分检测：通过会话工具和工作区文件

无论使用何种代理，在以下情况下应用自我提升：

发现非显而易见的内容 - 解决方案不是立即显而易见的
纠正自己 - 初始方法是错误的
学习项目约定 - 发现未记录的模式
遇到意外错误 - 特别是如果诊断很困难
找到更好的方法 - 改进了原始解决方案

Copilot 聊天集成

对于 Copilot 用户，在相关时将此添加到你的提示中：

完成此任务后，评估是否有任何学习内容应使用自我提升技能格式记录到 .learnings/。

或使用快速提示：

"将此记录到学习内容"
"从此解决方案创建技能"
"检查 .learnings/ 中的相关问题"

🇺🇸English

Self-Improvement Skill

Log learnings and errors to markdown files for continuous improvement. Coding agents can later process these into fixes, and important learnings get promoted to project memory.

Quick Reference

Situation	Action
Command/operation fails	Log to `.learnings/ERRORS.md`
User corrects you	Log to `.learnings/LEARNINGS.md` with category `correction`
User wants missing feature	Log to `.learnings/FEATURE_REQUESTS.md`
API/external tool fails	Log to `.learnings/ERRORS.md` with integration details
Knowledge was outdated	Log to `.learnings/LEARNINGS.md` with category `knowledge_gap`
Found better approach	Log to `.learnings/LEARNINGS.md` with category `best_practice`
Similar to existing entry	Link with `See Also`, consider priority bump
Broadly applicable learning	Promote to `CLAUDE.md`, `AGENTS.md`, and/or `.github/copilot-instructions.md`
Workflow improvements	Promote to `AGENTS.md` (OpenClaw workspace)
Tool gotchas	Promote to `TOOLS.md` (OpenClaw workspace)
Behavioral patterns	Promote to `SOUL.md` (OpenClaw workspace)

OpenClaw Setup (Recommended)

OpenClaw is the primary platform for this skill. It uses workspace-based prompt injection with automatic skill loading.

Installation

Via ClawdHub (recommended):

clawdhub install self-improving-agent

Manual:

git clone https://github.com/peterskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent

Workspace Structure

OpenClaw injects these files into every session:

~/.openclaw/workspace/
├── AGENTS.md          # Multi-agent workflows, delegation patterns
├── SOUL.md            # Behavioral guidelines, personality, principles
├── TOOLS.md           # Tool capabilities, integration gotchas
├── MEMORY.md          # Long-term memory (main session only)
├── memory/            # Daily memory files
│   └── YYYY-MM-DD.md
└── .learnings/        # This skill's log files
    ├── LEARNINGS.md
    ├── ERRORS.md
    └── FEATURE_REQUESTS.md

Create Learning Files

mkdir -p ~/.openclaw/workspace/.learnings

Then create the log files (or copy from assets/):

LEARNINGS.md — corrections, knowledge gaps, best practices
ERRORS.md — command failures, exceptions
FEATURE_REQUESTS.md — user-requested capabilities

Promotion Targets

When learnings prove broadly applicable, promote them to workspace files:

Learning Type	Promote To	Example
Behavioral patterns	`SOUL.md`	"Be concise, avoid disclaimers"
Workflow improvements	`AGENTS.md`	"Spawn sub-agents for long tasks"
Tool gotchas	`TOOLS.md`	"Git push needs auth configured first"

Inter-Session Communication

OpenClaw provides tools to share learnings across sessions:

sessions_list — View active/recent sessions
sessions_history — Read another session's transcript
sessions_send — Send a learning to another session
sessions_spawn — Spawn a sub-agent for background work

Optional: Enable Hook

For automatic reminders at session start:

# Copy hook to OpenClaw hooks directory
cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement

# Enable it
openclaw hooks enable self-improvement

See references/openclaw-integration.md for complete details.

Generic Setup (Other Agents)

For Claude Code, Codex, Copilot, or other agents, create .learnings/ in your project:

mkdir -p .learnings

Copy templates from assets/ or create files with headers.

Logging Format

Learning Entry

Append to .learnings/LEARNINGS.md:

## [LRN-YYYYMMDD-XXX] category

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
One-line description of what was learned

### Details
Full context: what happened, what was wrong, what's correct

### Suggested Action
Specific fix or improvement to make

### Metadata
- Source: conversation | error | user_feedback
- Related Files: path/to/file.ext
- Tags: tag1, tag2
- See Also: LRN-20250110-001 (if related to existing entry)

---

Error Entry

Append to .learnings/ERRORS.md:

## [ERR-YYYYMMDD-XXX] skill_or_command_name

**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
Brief description of what failed

### Error

Actual error message or output

### Context
- Command/operation attempted
- Input or parameters used
- Environment details if relevant

### Suggested Fix
If identifiable, what might resolve this

### Metadata
- Reproducible: yes | no | unknown
- Related Files: path/to/file.ext
- See Also: ERR-20250110-001 (if recurring)

---

Feature Request Entry

Append to .learnings/FEATURE_REQUESTS.md:

## [FEAT-YYYYMMDD-XXX] capability_name

**Logged**: ISO-8601 timestamp
**Priority**: medium
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Requested Capability
What the user wanted to do

### User Context
Why they needed it, what problem they're solving

### Complexity Estimate
simple | medium | complex

### Suggested Implementation
How this could be built, what it might extend

### Metadata
- Frequency: first_time | recurring
- Related Features: existing_feature_name

---

ID Generation

Format: TYPE-YYYYMMDD-XXX

TYPE: LRN (learning), ERR (error), FEAT (feature)
YYYYMMDD: Current date
XXX: Sequential number or random 3 chars (e.g., 001, A7B)

Examples: LRN-20250115-001, ERR-20250115-A3F, FEAT-20250115-002

Resolving Entries

When an issue is fixed, update the entry:

Change **Status**: pending → **Status**: resolved
Add resolution block after Metadata:

### Resolution

- **Resolved**: 2025-01-16T09:00:00Z
- **Commit/PR**: abc123 or #42
- **Notes**: Brief description of what was done

Other status values:

in_progress - Actively being worked on
wont_fix - Decided not to address (add reason in Resolution notes)
promoted - Elevated to CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md

Promoting to Project Memory

When a learning is broadly applicable (not a one-off fix), promote it to permanent project memory.

When to Promote

Learning applies across multiple files/features
Knowledge any contributor (human or AI) should know
Prevents recurring mistakes
Documents project-specific conventions

Promotion Targets

Target	What Belongs There
`CLAUDE.md`	Project facts, conventions, gotchas for all Claude interactions
`AGENTS.md`	Agent-specific workflows, tool usage patterns, automation rules
`.github/copilot-instructions.md`	Project context and conventions for GitHub Copilot
`SOUL.md`	Behavioral guidelines, communication style, principles (OpenClaw workspace)
`TOOLS.md`	Tool capabilities, usage patterns, integration gotchas (OpenClaw workspace)

How to Promote

Distill the learning into a concise rule or fact
Add to appropriate section in target file (create file if needed)
Update original entry:
- Change **Status**: pending → **Status**: promoted
- Add **Promoted**: CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md

Promotion Examples

Learning (verbose):

Project uses pnpm workspaces. Attempted npm install but failed. Lock file is pnpm-lock.yaml. Must use pnpm install.

In CLAUDE.md (concise):

## Build & Dependencies
- Package manager: pnpm (not npm) - use `pnpm install`

Learning (verbose):

When modifying API endpoints, must regenerate TypeScript client. Forgetting this causes type mismatches at runtime.

In AGENTS.md (actionable):

## After API Changes
1. Regenerate client: `pnpm run generate:api`
2. Check for type errors: `pnpm tsc --noEmit`

Recurring Pattern Detection

If logging something similar to an existing entry:

Search first : grep -r "keyword" .learnings/
Link entries : Add **See Also**: ERR-20250110-001 in Metadata
Bump priority if issue keeps recurring
Consider systemic fix : Recurring issues often indicate:
- Missing documentation (→ promote to CLAUDE.md or .github/copilot-instructions.md)
- Missing automation (→ add to AGENTS.md)
- Architectural problem (→ create tech debt ticket)

Periodic Review

Review .learnings/ at natural breakpoints:

When to Review

Before starting a new major task
After completing a feature
When working in an area with past learnings
Weekly during active development

Quick Status Check

# Count pending items
grep -h "Status\*\*: pending" .learnings/*.md | wc -l

# List pending high-priority items
grep -B5 "Priority\*\*: high" .learnings/*.md | grep "^## \["

# Find learnings for a specific area
grep -l "Area\*\*: backend" .learnings/*.md

Review Actions

Resolve fixed items
Promote applicable learnings
Link related entries
Escalate recurring issues

Detection Triggers

Automatically log when you notice:

Corrections (→ learning with correction category):

"No, that's not right..."
"Actually, it should be..."
"You're wrong about..."
"That's outdated..."

Feature Requests (→ feature request):

"Can you also..."
"I wish you could..."
"Is there a way to..."
"Why can't you..."

Knowledge Gaps (→ learning with knowledge_gap category):

User provides information you didn't know
Documentation you referenced is outdated
API behavior differs from your understanding

Errors (→ error entry):

Command returns non-zero exit code
Exception or stack trace
Unexpected output or behavior
Timeout or connection failure

Priority Guidelines

Priority	When to Use
`critical`	Blocks core functionality, data loss risk, security issue
`high`	Significant impact, affects common workflows, recurring issue
`medium`	Moderate impact, workaround exists
`low`	Minor inconvenience, edge case, nice-to-have

Area Tags

Use to filter learnings by codebase region:

Area	Scope
`frontend`	UI, components, client-side code
`backend`	API, services, server-side code
`infra`	CI/CD, deployment, Docker, cloud
`tests`	Test files, testing utilities, coverage
`docs`	Documentation, comments, READMEs
`config`	Configuration files, environment, settings

Best Practices

Log immediately - context is freshest right after the issue
Be specific - future agents need to understand quickly
Include reproduction steps - especially for errors
Link related files - makes fixes easier
Suggest concrete fixes - not just "investigate"
Use consistent categories - enables filtering
Promote aggressively - if in doubt, add to CLAUDE.md or .github/copilot-instructions.md
Review regularly - stale learnings lose value

Gitignore Options

Keep learnings local (per-developer):

.learnings/

Track learnings in repo (team-wide): Don't add to .gitignore - learnings become shared knowledge.

Hybrid (track templates, ignore entries):

.learnings/*.md
!.learnings/.gitkeep

Hook Integration

Enable automatic reminders through agent hooks. This is opt-in - you must explicitly configure hooks.

Quick Setup (Claude Code / Codex)

Create .claude/settings.json in your project:

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/activator.sh"
      }]
    }]
  }
}

This injects a learning evaluation reminder after each prompt (~50-100 tokens overhead).

Full Setup (With Error Detection)

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/activator.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "Bash",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/error-detector.sh"
      }]
    }]
  }
}

Available Hook Scripts

Script	Hook Type	Purpose
`scripts/activator.sh`	UserPromptSubmit	Reminds to evaluate learnings after tasks
`scripts/error-detector.sh`	PostToolUse (Bash)	Triggers on command errors

See references/hooks-setup.md for detailed configuration and troubleshooting.

Automatic Skill Extraction

When a learning is valuable enough to become a reusable skill, extract it using the provided helper.

Skill Extraction Criteria

A learning qualifies for skill extraction when ANY of these apply:

Criterion	Description
Recurring	Has `See Also` links to 2+ similar issues
Verified	Status is `resolved` with working fix
Non-obvious	Required actual debugging/investigation to discover
Broadly applicable	Not project-specific; useful across codebases
User-flagged	User says "save this as a skill" or similar

Extraction Workflow

Identify candidate : Learning meets extraction criteria

Run helper (or create manually):

./skills/self-improvement/scripts/extract-skill.sh skill-name --dry-run
./skills/self-improvement/scripts/extract-skill.sh skill-name

Customize SKILL.md : Fill in template with learning content
Update learning : Set status to promoted_to_skill, add Skill-Path
Verify : Read skill in fresh session to ensure it's self-contained

Manual Extraction

If you prefer manual creation:

Create skills/<skill-name>/SKILL.md
Use template from assets/SKILL-TEMPLATE.md
Follow Agent Skills spec:
- YAML frontmatter with name and description
- Name must match folder name
- No README.md inside skill folder

Extraction Detection Triggers

Watch for these signals that a learning should become a skill:

In conversation:

"Save this as a skill"
"I keep running into this"
"This would be useful for other projects"
"Remember this pattern"

In learning entries:

Multiple See Also links (recurring issue)
High priority + resolved status
Category: best_practice with broad applicability
User feedback praising the solution

Skill Quality Gates

Before extraction, verify:

Solution is tested and working
Description is clear without original context
Code examples are self-contained
No project-specific hardcoded values
Follows skill naming conventions (lowercase, hyphens)

Multi-Agent Support

This skill works across different AI coding agents with agent-specific activation.

Claude Code

Activation : Hooks (UserPromptSubmit, PostToolUse) Setup : .claude/settings.json with hook configuration Detection : Automatic via hook scripts

Codex CLI

Activation : Hooks (same pattern as Claude Code) Setup : .codex/settings.json with hook configuration Detection : Automatic via hook scripts

GitHub Copilot

Activation : Manual (no hook support) Setup : Add to .github/copilot-instructions.md:

## Self-Improvement

After solving non-obvious issues, consider logging to `.learnings/`:
1. Use format from self-improvement skill
2. Link related entries with See Also
3. Promote high-value learnings to skills

Ask in chat: "Should I log this as a learning?"

Detection : Manual review at session end

OpenClaw

Activation : Workspace injection + inter-agent messaging Setup : See "OpenClaw Setup" section above Detection : Via session tools and workspace files

Agent-Agnostic Guidance

Regardless of agent, apply self-improvement when you:

Discover something non-obvious - solution wasn't immediate
Correct yourself - initial approach was wrong
Learn project conventions - discovered undocumented patterns
Hit unexpected errors - especially if diagnosis was difficult
Find better approaches - improved on your original solution

Copilot Chat Integration

For Copilot users, add this to your prompts when relevant:

After completing this task, evaluate if any learnings should be logged to .learnings/ using the self-improvement skill format.

Or use quick prompts:

"Log this to learnings"
"Create a skill from this solution"
"Check .learnings/ for related issues"

Weekly Installs

5.8K

Repository

pskoett/self-im…ng-agent

GitHub Stars

First Seen

Feb 20, 2026

Security Audits

Gen Agent Trust HubPass SocketPass SnykPass

Installed on

opencode5.7K

codex5.7K

gemini-cli5.7K

github-copilot5.7K

cursor5.7K

kimi-cli5.7K

AI代理自我提升技能：记录错误与学习，实现持续改进的智能编码助手

🇨🇳中文介绍

自我提升技能

快速参考

相关 Skills

OpenClaw 设置（推荐）

安装

工作区结构

创建学习文件

提升目标

会话间通信

可选：启用钩子

通用设置（其他代理）

日志记录格式

学习条目

错误条目

功能请求条目

ID 生成

解决条目

提升到项目记忆

何时提升

提升目标

如何提升

提升示例

重复模式检测

定期审查

何时审查

快速状态检查

审查操作

检测触发器

优先级指南

区域标签

最佳实践

Gitignore 选项

钩子集成

快速设置（Claude Code / Codex）

完整设置（带错误检测）

可用的钩子脚本

自动技能提取

技能提取标准

提取工作流程

手动提取

提取检测触发器

技能质量门控

多代理支持

Claude Code

Codex CLI

GitHub Copilot

OpenClaw

代理无关指导

Copilot 聊天集成

🇺🇸English

Self-Improvement Skill

Quick Reference

OpenClaw Setup (Recommended)

Installation

Workspace Structure

Create Learning Files

Promotion Targets

Inter-Session Communication

Optional: Enable Hook

Generic Setup (Other Agents)

Logging Format

Learning Entry

Error Entry

Feature Request Entry

ID Generation

Resolving Entries

Promoting to Project Memory

When to Promote

Promotion Targets

How to Promote

Promotion Examples

Recurring Pattern Detection

Periodic Review

When to Review

Quick Status Check

Review Actions

Detection Triggers

Priority Guidelines