---

name: self-improving-agent description: Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects the agent's behavior, (3) Agent completes a complex task successfully, (4) Periodic review of past learnings.

Self-Improving Agent

Overview

This skill enables OpenClaw to continuously improve by capturing learnings from:

• Failed operations and errors
• User corrections and feedback
• Successful complex task completions
• Periodic review and consolidation

When to Use

1. Error Recovery: When a command or operation fails unexpectedly
2. User Correction: When the user corrects the agent's behavior or output
3. Success Capture: After completing a complex task successfully
4. Learning Review: Periodic review of past learnings to consolidate knowledge

Core Workflow

1. Capture Learning

When something noteworthy happens:

Type: error | correction | success | insight
Context: What was being attempted
Issue: What went wrong (for errors)
Correction: What should be done differently
Lesson: Generalizable takeaway
Tags: Relevant topics/skills

2. Store Learning

Learnings are stored in memory/learnings.jsonl with:

• Timestamp
• Type and severity
• Full context and details
• Tags for searchability

3. Retrieve Relevant Learnings

Before starting a task, search past learnings:

• Match by task type
• Match by tags
• Match by error patterns

4. Apply Learnings

Use retrieved learnings to:

• Avoid past mistakes
• Apply successful patterns
• Adjust approach based on corrections

Memory Structure

Learnings are stored in JSONL format:

{
  "timestamp": "2026-03-06T10:30:00Z",
  "type": "error",
  "severity": "high",
  "context": "Installing npm package globally",
  "issue": "Permission denied without sudo",
  "correction": "Use sudo for global installs or configure npm prefix",
  "lesson": "Always check if operation requires elevated privileges",
  "tags": ["npm", "permissions", "installation"],
  "taskSlug": "npm-global-install"
}

Learning Types

Type	When to Use	Example
error	Operation failed	Command returned non-zero exit code
correction	User corrected behavior	"Don't use rm, use trash instead"
success	Complex task completed	Successfully deployed to production
insight	Discovered optimization	"This API is faster than alternatives"

Severity Levels

• critical: System-breaking errors, data loss risk
• high: Task-blocking errors, significant issues
• medium: Minor issues, workarounds available
• low: Optimization opportunities, nice-to-know

Commands

Capture a Learning

# Manual capture (for user corrections)
openclaw memory add-learning --type correction --context "..." --lesson "..."

Search Learnings

# Search by keyword
openclaw memory search-learnings "npm permissions"

# Search by tag
openclaw memory search-learnings --tag npm

# Search by type
openclaw memory search-learnings --type error

Review Learnings

# Review recent learnings
openclaw memory review-learnings --days 7

# Review by category
openclaw memory review-learnings --tag deployment

Best Practices

1. Capture Immediately: Record learnings while context is fresh
2. Be Specific: Include full error messages and exact commands
3. Generalize Lessons: Extract principles that apply beyond this instance
4. Tag Thoughtfully: Use consistent tags for easy retrieval
5. Review Regularly: Weekly review helps consolidate knowledge
6. Avoid Duplicates: Check existing learnings before adding new ones

Integration Points

• Error Handlers: Automatically capture command failures
• User Feedback: Listen for correction patterns in conversation
• Task Completion: Prompt for learning capture after complex tasks
• Heartbeat: Include learning review in periodic checks

Example Scenarios

Scenario 1: Command Failure

Context: Running `npm install -g package`
Issue: EACCES permission error
Correction: Run with sudo or configure npm prefix
Lesson: Check if global install requires elevated privileges
Tags: npm, permissions, installation

Scenario 2: User Correction

Context: Suggested using `rm -rf` for cleanup
Correction: User prefers `trash` for safety
Lesson: Default to safe, reversible operations
Tags: safety, file-operations, user-preference

Scenario 3: Success Pattern

Context: Deploying to VPS via SSH
Success: Used rsync with specific flags for reliability
Lesson: rsync -avz --delete is reliable for deployments
Tags: deployment, ssh, rsync, success

Safety Rules

• Never store sensitive data (passwords, API keys, tokens)
• Sanitize error messages that might contain secrets
• Require user approval before storing corrections
• Allow users to delete or edit learnings
• Respect user privacy preferences