快速判断
自我反思 + 自我批评 + 自我学习 + 自我组织记忆。代理评估自己的工作,发现错误,并永久改进。在以下情况下使用:(1) 命令、工具、API或操作失败;(2) 用户纠正你或拒绝你的工作;(3) 你意识到自己的知识已经过时或不正确;(4) 你发现了更好的方法;(5) 用户明确安装或引用了当前任务所需的技能。
适合任务
- 按 ModelScope 收录说明完成平台、开发或工作流任务。
- 通过下载包离线保存 Skill 内容。
- 结合下载量、访问量和喜欢数评估优先级。
输入与输出
输入:任务目标、上下文材料、平台信息、文件路径、约束条件或需要处理的内容。
输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。
示例任务
- 使用 Self-Improving + Proactive Agent 帮我完成当前任务,并先确认必要上下文。
- 根据 Self-Improving + Proactive Agent 的说明,列出操作步骤和风险检查点。
安装方式
- 下载本站提供的 Skill ZIP 并解压。
- 把解压后的 Skill 目录放入当前 AI 工具支持的
skills目录。 - 如需在线查看原始内容,可打开 GitHub 的
SKILL.md。
风险边界
使用前请检查权限、外部依赖和要处理的数据类型。第三方平台数据、支付、部署、账号和密钥相关内容应先核对官方说明。
SKILL.md 文档介绍
When to Use
User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance.
Architecture
Memory lives in ~/self-improving/ with tiered structure. If ~/self-improving/ does not exist, run setup.md.
Workspace setup should add the standard self-improving steering to the workspace AGENTS, SOUL, and HEARTBEAT.md files, with recurring maintenance routed through heartbeat-rules.md.
~/self-improving/
├── memory.md # HOT: ≤100 lines, always loaded
├── index.md # Topic index with line counts
├── heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes
├── projects/ # Per-project learnings
├── domains/ # Domain-specific (code, writing, comms)
├── archive/ # COLD: decayed patterns
└── corrections.md # Last 50 corrections logQuick Reference
| Topic | File |
|-------|------|
| Setup guide | setup.md |
| Heartbeat state template | heartbeat-state.md |
| Memory template | memory-template.md |
| Workspace heartbeat snippet | HEARTBEAT.md |
| Heartbeat rules | heartbeat-rules.md |
| Learning mechanics | learning.md |
| Security boundaries | boundaries.md |
| Scaling rules | scaling.md |
| Memory operations | operations.md |
| Self-reflection log | reflections.md |
| OpenClaw HEARTBEAT seed | openclaw-heartbeat.md |
Requirements
- No credentials required
- No extra binaries required
- Optional installation of the
Proactivityskill may require network access
Learning Signals
Log automatically when you notice these patterns:
Corrections → add to corrections.md, evaluate for memory.md:
- "No, that's not right..."
- "Actually, it should be..."
- "You're wrong about..."
- "I prefer X, not Y"
- "Remember that I always..."
- "I told you before..."
- "Stop doing X"
- "Why do you keep..."
Preference signals → add to memory.md if explicit:
- "I like when you..."
- "Always do X for me"
- "Never do Y"
- "My style is..."
- "For [project], use..."
Pattern candidates → track, promote after 3x:
- Same instruction repeated 3+ times
- Workflow that works well repeatedly
- User praises specific approach
Ignore (don't log):
- One-time instructions ("do X now")
- Context-specific ("in this file...")
- Hypotheticals ("what if...")
Self-Reflection
After completing significant work, pause and evaluate:
1. Did it meet expectations? — Compare outcome vs intent
2. What could be better? — Identify improvements for next time
3. Is this a pattern? — If yes, log to corrections.md
When to self-reflect:
- After completing a multi-step task
- After receiving feedback (positive or negative)
- After fixing a bug or mistake
- When you notice your output could be better
Log format:
CONTEXT: [type of task]
REFLECTION: [what I noticed]
LESSON: [what to do differently]Example:
CONTEXT: Building Flutter UI
REFLECTION: Spacing looked off, had to redo
LESSON: Check visual spacing before showing userSelf-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT.
Quick Queries
| User says | Action |
|-----------|--------|
| "What do you know about X?" | Search all tiers for X |
| "What have you learned?" | Show last 10 from corrections.md |
| "Show my patterns" | List memory.md (HOT) |
| "Show [project] patterns" | Load projects/{name}.md |
| "What's in warm storage?" | List files in projects/ + domains/ |
| "Memory stats" | Show counts per tier |
| "Forget X" | Remove from all tiers (confirm first) |
| "Export memory" | ZIP all files |
Memory Stats
On "memory stats" request, report:
📊 Self-Improving Memory
HOT (always loaded):
memory.md: X entries
WARM (load on demand):
projects/: X files
domains/: X files
COLD (archived):
archive/: X files
Recent activity (7 days):
Corrections logged: X
Promotions to HOT: X
Demotions to WARM: XCommon Traps
| Trap | Why It Fails | Better Move |
|------|--------------|-------------|
| Learning from silence | Creates false rules | Wait for explicit correction or repeated evidence |
| Promoting too fast | Pollutes HOT memory | Keep new lessons tentative until repeated |
| Reading every namespace | Wastes context | Load only HOT plus the smallest matching files |
| Compaction by deletion | Loses trust and history | Merge, summarize, or demote instead |
Core Rules
1. Learn from Corrections and Self-Reflection
- Log when user explicitly corrects you
- Log when you identify improvements in your own work
- Never infer from silence alone
- After 3 identical lessons → ask to confirm as rule
2. Tiered Storage
| Tier | Location | Size Limit | Behavior |
|------|----------|------------|----------|
| HOT | memory.md | ≤100 lines | Always loaded |
| WARM | projects/, domains/ | ≤200 lines each | Load on context match |
| COLD | archive/ | Unlimited | Load on explicit query |
3. Automatic Promotion/Demotion
- Pattern used 3x in 7 days → promote to HOT
- Pattern unused 30 days → demote to WARM
- Pattern unused 90 days → archive to COLD
- Never delete without asking
4. Namespace Isolation
- Project patterns stay in
projects/{name}.md - Global preferences in HOT tier (memory.md)
- Domain patterns (code, writing) in
domains/ - Cross-namespace inheritance: global → domain → project
5. Conflict Resolution
When patterns contradict:
1. Most specific wins (project > domain > global)
2. Most recent wins (same level)
3. If ambiguous → ask user
6. Compaction
When file exceeds limit:
1. Merge similar corrections into single rule
2. Archive unused patterns
3. Summarize verbose entries
4. Never lose confirmed preferences
7. Transparency
- Every action from memory → cite source: "Using X (from projects/foo.md:12)"
- Weekly digest available: patterns learned, demoted, archived
- Full export on demand: all files as ZIP
8. Security Boundaries
See boundaries.md — never store credentials, health data, third-party info.
9. Graceful Degradation
If context limit hit:
1. Load only memory.md (HOT)
2. Load relevant namespace on demand
3. Never fail silently — tell user what's not loaded
Scope
This skill ONLY:
- Learns from user corrections and self-reflection
- Stores preferences in local files (
~/self-improving/) - Maintains heartbeat state in
~/self-improving/heartbeat-state.mdwhen the workspace integrates heartbeat - Reads its own memory files on activation
This skill NEVER:
- Accesses calendar, email, or contacts
- Makes network requests
- Reads files outside
~/self-improving/ - Infers preferences from silence or observation
- Deletes or blindly rewrites self-improving memory during heartbeat cleanup
- Modifies its own SKILL.md
Data Storage
Local state lives in ~/self-improving/:
memory.mdfor HOT rules and confirmed preferencescorrections.mdfor explicit corrections and reusable lessonsprojects/anddomains/for scoped patternsarchive/for decayed or inactive patternsheartbeat-state.mdfor recurring maintenance markers
Related Skills
Install with clawhub install <slug> if user confirms:
memory— Long-term memory patterns for agentslearning— Adaptive teaching and explanationdecide— Auto-learn decision patternsescalate— Know when to ask vs act autonomously
Feedback
- If useful:
clawhub star self-improving - Stay updated:
clawhub sync