Self-Improvement Skill

Quick Reference

.learnings/ERRORS.md

.learnings/LEARNINGS.md

correction

.learnings/FEATURE_REQUESTS.md

.learnings/ERRORS.md

.learnings/LEARNINGS.md

knowledge_gap

.learnings/LEARNINGS.md

best_practice

**See Also**

CLAUDE.md

AGENTS.md

.github/copilot-instructions.md

AGENTS.md

TOOLS.md

SOUL.md

Setup

.learnings/

mkdir -p .learnings

assets/

Logging Format

Learning Entry

.learnings/LEARNINGS.md

## [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)

---

Error Entry

.learnings/ERRORS.md

## [ERR-YYYYMMDD-XXX] skill_or_command_name

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

### Summary
Brief description of what failed

### Error

### 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
- See Also: ERR-20250110-001 (if recurring)

---

Feature Request Entry

.learnings/FEATURE_REQUESTS.md

## [FEAT-YYYYMMDD-XXX] capability_name

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

### Requested Capability
What the user wanted to do

### User Context
Why they needed it, what problem they're solving

### Complexity Estimate
simple | medium | complex

### Suggested Implementation
How this could be built, what it might extend

### Metadata
- Frequency: first_time | recurring
- Related Features: existing_feature_name

---

ID Generation

TYPE-YYYYMMDD-XXX

• TYPE:

  LRN

  (learning),

  ERR

  (error),

  FEAT

  (feature)
• YYYYMMDD: Current date
• XXX: Sequential number or random 3 chars (e.g.,

  001

  ,

  A7B

  )

LRN-20250115-001

ERR-20250115-A3F

FEAT-20250115-002

Resolving Entries

1. Change

   **Status**: pending

   →

   **Status**: resolved

2. Add resolution block after Metadata:

### Resolution
- **Resolved**: 2025-01-16T09:00:00Z
- **Commit/PR**: abc123 or #42
- **Notes**: Brief description of what was done

• in_progress

  - Actively being worked on

• wont_fix

  - Decided not to address (add reason in Resolution notes)

• promoted

  - Elevated to CLAUDE.md, AGENTS.md, or .github/copilot-instructions.md

Promoting to Project Memory

When to Promote

• Learning applies across multiple files/features
• Knowledge any contributor (human or AI) should know
• Prevents recurring mistakes
• Documents project-specific conventions

Promotion Targets

CLAUDE.md

AGENTS.md

.github/copilot-instructions.md

SOUL.md

TOOLS.md

How to Promote

1. Distill the learning into a concise rule or fact
2. Add to appropriate section in target file (create file if needed)
3. Update original entry:
   • Change

     **Status**: pending

     →

     **Status**: promoted

   • Add

     **Promoted**: CLAUDE.md

     ,

     AGENTS.md

     , or

     .github/copilot-instructions.md

Promotion Examples

Project uses pnpm workspaces. Attempted

npm install

but failed. Lock file is

pnpm-lock.yaml

. Must use

pnpm install

.

## Build & Dependencies
- Package manager: pnpm (not npm) - use `pnpm install`

When modifying API endpoints, must regenerate TypeScript client. Forgetting this causes type mismatches at runtime.

## After API Changes
1. Regenerate client: `pnpm run generate:api`
2. Check for type errors: `pnpm tsc --noEmit`

Recurring Pattern Detection

1. Search first:

   grep -r "keyword" .learnings/

2. Link entries: Add

   **See Also**: ERR-20250110-001

   in Metadata
3. Bump priority if issue keeps recurring
4. Consider systemic fix: Recurring issues often indicate:
   • Missing documentation (→ promote to CLAUDE.md or .github/copilot-instructions.md)
   • Missing automation (→ add to AGENTS.md)
   • Architectural problem (→ create tech debt ticket)

Periodic Review

.learnings/

When to Review

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

Quick Status Check

# Count pending items
grep -h "Status\*\*: pending" .learnings/*.md | wc -l

# List pending high-priority items
grep -B5 "Priority\*\*: high" .learnings/*.md | grep "^## \["

# Find learnings for a specific area
grep -l "Area\*\*: backend" .learnings/*.md

Review Actions

• Resolve fixed items
• Promote applicable learnings
• Link related entries
• Escalate recurring issues

Detection Triggers

correction

• "No, that's not right..."
• "Actually, it should be..."
• "You're wrong about..."
• "That's outdated..."

• "Can you also..."
• "I wish you could..."
• "Is there a way to..."
• "Why can't you..."

knowledge_gap

• User provides information you didn't know
• Documentation you referenced is outdated
• API behavior differs from your understanding

• Command returns non-zero exit code
• Exception or stack trace
• Unexpected output or behavior
• Timeout or connection failure

Priority Guidelines

critical

high

medium

low

Area Tags

frontend

backend

infra

tests

docs

config

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

Gitignore Options

.learnings/

.learnings/*.md
!.learnings/.gitkeep

Hook Integration

Quick Setup (Claude Code / Codex)

.claude/settings.json

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/activator.sh"
      }]
    }]
  }
}

Full Setup (With Error Detection)

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": "",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/activator.sh"
      }]
    }],
    "PostToolUse": [{
      "matcher": "Bash",
      "hooks": [{
        "type": "command",
        "command": "./skills/self-improvement/scripts/error-detector.sh"
      }]
    }]
  }
}

Available Hook Scripts

scripts/activator.sh

scripts/error-detector.sh

references/hooks-setup.md

Automatic Skill Extraction

Skill Extraction Criteria

See Also

resolved

Extraction Workflow

1. Identify candidate: Learning meets extraction criteria
2. Run helper (or create manually):
   bash

   ./skills/self-improvement/scripts/extract-skill.sh skill-name --dry-run
   ./skills/self-improvement/scripts/extract-skill.sh skill-name

3. Customize SKILL.md: Fill in template with learning content
4. Update learning: Set status to

   promoted_to_skill

   , add

   Skill-Path

5. Verify: Read skill in fresh session to ensure it's self-contained

Manual Extraction

1. Create

   skills/<skill-name>/SKILL.md

2. Use template from

   assets/SKILL-TEMPLATE.md

3. Follow Agent Skills spec:
   • YAML frontmatter with

     name

     and

     description

   • Name must match folder name
   • No README.md inside skill folder

Extraction Detection Triggers

• "Save this as a skill"
• "I keep running into this"
• "This would be useful for other projects"
• "Remember this pattern"

• Multiple

  See Also

  links (recurring issue)
• High priority + resolved status
• Category:

  best_practice

  with broad applicability
• User feedback praising the solution

Skill Quality Gates

• Solution is tested and working
• Description is clear without original context
• Code examples are self-contained
• No project-specific hardcoded values
• Follows skill naming conventions (lowercase, hyphens)

Multi-Agent Support

Claude Code

.claude/settings.json

Codex CLI

.codex/settings.json

GitHub Copilot

.github/copilot-instructions.md

## Self-Improvement

After solving non-obvious issues, consider logging to `.learnings/`:
1. Use format from self-improvement skill
2. Link related entries with See Also
3. Promote high-value learnings to skills

Ask in chat: "Should I log this as a learning?"

Clawdbot

~/.clawdbot/clawdbot.json

AGENTS.md

SOUL.md

TOOLS.md

references/clawdbot-integration.md

Agent-Agnostic Guidance

1. Discover something non-obvious - solution wasn't immediate
2. Correct yourself - initial approach was wrong
3. Learn project conventions - discovered undocumented patterns
4. Hit unexpected errors - especially if diagnosis was difficult
5. Find better approaches - improved on your original solution

Copilot Chat Integration

After completing this task, evaluate if any learnings should be logged to

.learnings/

using the self-improvement skill format.

• "Log this to learnings"
• "Create a skill from this solution"
• "Check .learnings/ for related issues"

Clawdbot Integration

Workspace Structure

~/clawd/                    # Default workspace (configurable)
├── AGENTS.md              # Multi-agent workflows, delegation patterns
├── SOUL.md                # Behavioral guidelines, communication style
├── TOOLS.md               # Tool capabilities, MCP integrations
└── sessions/              # Session transcripts (auto-managed)

Clawdbot Promotion Targets

AGENTS.md

SOUL.md

TOOLS.md

CLAUDE.md

Inter-Agent Learning

• sessions_list - See active/recent sessions
• sessions_history - Read transcript from another session
• sessions_send - Send message to another session

Hybrid Setup (Claude Code + Clawdbot)

1. Keep

   .learnings/

   for project-specific learnings
2. Use clawdbot workspace files for cross-project patterns
3. Sync high-value learnings to both systems

references/clawdbot-integration.md