self-improving
- Use it when
- you keep correcting the agent on the same things and want lessons to stick across sessions
- It does
- logs your corrections and its own reflections after big tasks, and turns lessons repeated three times into rules
- Keeps
- a memory folder in your home directory: a short file loaded every time, files per project and topic, and an archive
- Setup
- creates that folder and adds a few lines to your agent's instruction files, without removing what's there
- Runs
- on its own, when you correct it, a command fails or it finishes significant work
- Unlike
- skills that log lessons to a folder inside each project, its memory is shared across projects and sorted by how often each rule is used
TLDR
It makes the agent learn from mistakes. Explicit corrections ("no, that's wrong", "I prefer X", "stop doing Y") go to a corrections log, and after significant work the agent notes what could have gone better. A lesson seen three times becomes a candidate rule, confirmed with you, and moves into memory.md, a file of at most 100 lines loaded before every non-trivial task. Lessons for one project or topic, such as code or writing, get their own files, loaded only when relevant. A rule unused for 30 days leaves the always-loaded file, and after 90 days goes to an archive; nothing is deleted without asking. When rules conflict, the most specific wins, and the agent cites which file a choice came from. You can ask what it has learned, export everything or have it forget something. It never stores credentials or health data and makes no network requests. Setup assumes a workspace like OpenClaw's (an open-source personal AI agent): it adds lines to AGENTS.md, SOUL.md and HEARTBEAT.md, and a periodic check keeps the memory tidy. Setup also offers a separate Proactivity skill, installed only if you agree.
Summarizes the skill at aca4fea.
openclaw skills install @ivangdavila/self-improvingClawHub page ยท All skills from clawhub.ai/ivangdavila
- Downloads
- 209,818 on ClawHub, Sep 21, 2026
- Downloads
- 409,437 on Tencent SkillHub, Sep 21, 2026
| Category |
Build agents, orchestrate workers and give agents memory. Everything for building software: planning, coding, debugging, design, frameworks, cloud, agents and skills. |
|---|---|
| Type |
A multi-step process the agent follows: interview, debug, run a TDD loop, review. |
| Effect |
Its own steps create or change files, git state or local tools. |
| Needs |
Instructions only. Works in any agent that supports skills. |
| Publisher |
Everyone else. Unverified vendor claims default to community. |
| Topics |
SKILL.md
From ClawHub, version 1.2.16. Checked daily, last changed Oct 20, 2026.
Frontmatter
name: Self-Improving + Proactive Agent
slug: self-improving
version: 1.2.16
homepage: https://clawic.com/skills/self-improving
description: "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."
changelog: "Clarifies the setup flow for proactive follow-through and safer installation behavior."
metadata: {"clawdbot":{"emoji":"๐ง ","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/self-improving/"],"configPaths.optional":["./AGENTS.md","./SOUL.md","./HEARTBEAT.md"]}}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/
โโโ memory.md # HOT: โค100 lines, always loaded
โโโ index.md # Topic index with line counts
โโโ heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes
โโโ projects/ # Per-project learnings
โโโ domains/ # Domain-specific (code, writing, comms)
โโโ archive/ # COLD: decayed patterns
โโโ 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 โ 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 โ 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 โ 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? โ Compare outcome vs intent
- What could be better? โ Identify improvements for next time
- Is this a pattern? โ 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 โ 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:
๐ 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 โ ask to confirm as rule
2. Tiered Storage
| Tier | Location | Size Limit | Behavior |
|---|---|---|---|
| HOT | memory.md | โค100 lines | Always loaded |
| WARM | projects/, domains/ | โค200 lines each | Load on context match |
| COLD | archive/ | Unlimited | Load on explicit query |
3. Automatic Promotion/Demotion
- Pattern used 3x in 7 days โ promote to HOT
- Pattern unused 30 days โ demote to WARM
- Pattern unused 90 days โ 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 โ domain โ project
5. Conflict Resolution
When patterns contradict:
- Most specific wins (project > domain > global)
- Most recent wins (same level)
- If ambiguous โ 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 โ 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 โ 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 โ 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:
memoryโ Long-term memory patterns for agentslearningโ Adaptive teaching and explanationdecideโ Auto-learn decision patternsescalateโ Know when to ask vs act autonomously
Feedback
- If useful:
clawhub star self-improving - Stay updated:
clawhub sync