Self-Learning Skill
Gives any AI agent persistent memory and self-improvement. The agent writes and refines its own memory files — capturing failures, extracting patterns, generating fixes, and validating them before committing.
Triggers
Use when:
- Starting a new session with the agent
- After a task fails or produces unexpected output
- When the agent makes the same mistake twice
- Regular review of memory files for stale or incorrect entries
- Improving the agent's performance based on past experience
How It Works
The self-learning loop:
- Capture — After each session or failure, log: what was attempted, what went wrong, system state
- Analyse — Identify the root cause pattern, not just the symptom
- Generate — Create a fix or prevention rule based on the pattern
- Validate — Test the fix before committing it to memory
- Commit — Update the agent's memory files only when validation succeeds
Files Created
~/.openclaw/workspace/memory/failures/— Log of failed tasks with full context~/.openclaw/workspace/memory/lessons/— Generated fixes and prevention rules~/.openclaw/workspace/MEMORY.md— Consolidated memory (auto-updated on commit)
What Gets Captured
- Task failures — What the agent tried, why it failed, what it would do differently
- User corrections — Explicit feedback that changes how the agent approaches tasks
- Error patterns — Repeated mistakes suggesting a systematic fix is needed
- Preference shifts — Changes in user requirements that should update future behaviour
Why File-Based
No external dependencies — just the filesystem. Zero latency, full transparency, human-readable. The agent doesn't just store facts — it stores actionable lessons.
Installation
openclaw skills install https://github.com/TheSolAI/openclaw-self-learning-skill