Self-Improving Plus

Enhanced self-improvement skill with automatic skill distillation, knowledge base integration, and GitHub high-star project learning.

Features

• Error/Correction Logging: Track learnings, errors, and feature requests
• Skill Distillation: Extract reusable skills from learnings
• Knowledge Base Integration: Connect with GitHub high-star project database
• Automatic Skill Extraction: Auto-create skills from recurring patterns
• Periodic Review: Regular review and promotion of learnings

Setup

mkdir -p .learnings

Quick Reference

Logging Format

Learning Entry

## [LRN-YYYYMMDD-XXX] category

**Logged**: ISO-8601 timestamp
**Priority**: low | medium | high | critical
**Status**: pending
**Area**: frontend | backend | infra | tests | docs | config

### Summary
One-line description of what was learned

### Details
Full context: what happened, what was wrong, what's correct

### Suggested Action
Specific fix or improvement to make

### Metadata
- Source: conversation | error | user_feedback
- Related Files: path/to/file.ext
- Tags: tag1, tag2
- See Also: LRN-20250110-001 (if related to existing entry)
- Pattern-Key: simplify.dead_code | harden.input_validation (optional)
- Recurrence-Count: 1 (optional)
- First-Seen: 2025-01-15 (optional)
- Last-Seen: 2025-01-15 (optional)

Error Entry

## [ERR-YYYYMMDD-XXX] skill_or_command_name

**Logged**: ISO-8601 timestamp
**Priority**: high
**Status**: pending

### Summary
Brief description of what failed

### Error
Actual error message or output

### Context
- Command/operation attempted
- Input or parameters used
- Environment details if relevant

### Suggested Fix
If identifiable, what might resolve this

### Metadata
- Reproducible: yes | no | unknown
- Related Files: path/to/file.ext

Skill Distillation Workflow

When a learning becomes valuable enough to be a reusable skill:

Extraction Criteria

Extraction Process

1. Identify candidate: Learning meets extraction criteria
2. Create skill directory: skills//
3. Write SKILL.md: Use standard skill format with YAML frontmatter
4. Update learning: Set status to promoted_to_skill, add Skill-Path
5. Publish to ClawHub: Share with community

Knowledge Base Integration

Connect with GitHub high-star project database for research:

### Research Query
- Topic: [subject to research]
- Related Projects: [list relevant projects]
- Search Strategy: [how to find solutions]

Periodic Review

Review .learnings/ at natural breakpoints:

• Before starting a new major task
• After completing a feature
• When working in an area with past learnings
• Weekly during active development

Review Actions

• Resolve fixed items
• Promote applicable learnings
• Link related entries
• Escalate recurring issues
• Extract recurring patterns as skills

Best Practices

1. Log immediately - context is freshest right after the issue
2. Be specific - future agents need to understand quickly
3. Include reproduction steps - especially for errors
4. Link related files - makes fixes easier
5. Suggest concrete fixes - not just "investigate"
6. Use consistent categories - enables filtering
7. Promote aggressively - if in doubt, add to CLAUDE.md or .github/copilot-instructions.md
8. Review regularly - stale learnings lose value
9. Extract skills - recurring patterns become reusable skills
10. Connect to knowledge base - research solutions from high-star projects