self-improving
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.
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Install
Works with every agent the skills CLI supports.
npx skills add https://modelscope.cn --skill self-improving- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads self-improving automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 log
Quick 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:
- Did it meet expectations? — Compare outcome vs intent
- What could be better? — Identify improvements for next time
- 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 user
Self-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: X
Common 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:
- Most specific wins (project > domain > global)
- Most recent wins (same level)
- If ambiguous → ask user
6. Compaction
When file exceeds limit:
- Merge similar corrections into single rule
- Archive unused patterns
- Summarize verbose entries
- 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:
- Load only memory.md (HOT)
- Load relevant namespace on demand
- 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
FAQ
What does the self-improving skill do?
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.
How do I install self-improving?
Run npx skills add https://modelscope.cn --skill self-improving in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/self-improving/ (personal) or .claude/skills/self-improving/ (project).
Which AI agents can use self-improving?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is self-improving safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains self-improving?
It is published by modelscope.cn in modelscope.cn. It has 234 recorded installs.
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