self-improving-agent

Self-Improvement Skill

Log learnings and errors to markdown files for continuous improvement. Coding agents can later process these into fixes, and important learnings get promoted to project memory.

Quick Reference

| Situation | Action |

|-----------|--------|

| Command/operation fails | Log to .learnings/ERRORS.md |

| User corrects you | Log to .learnings/LEARNINGS.md with category correction |

| User wants missing feature | Log to .learnings/FEATURE_REQUESTS.md |

| API/external tool fails | Log to .learnings/ERRORS.md with integration details |

| Knowledge was outdated | Log to .learnings/LEARNINGS.md with category knowledge_gap |

| Found better approach | Log to .learnings/LEARNINGS.md with category best_practice |

| Simplify/Harden recurring patterns | Log/update .learnings/LEARNINGS.md with Source: simplify-and-harden and a stable Pattern-Key |

| Similar to existing entry | Link with See Also, consider priority bump |

| Broadly applicable learning | Promote to CLAUDE.md, AGENTS.md, and/or .github/copilot-instructions.md |

| Workflow improvements | Promote to AGENTS.md (OpenClaw workspace) |

| Tool gotchas | Promote to TOOLS.md (OpenClaw workspace) |

| Behavioral patterns | Promote to SOUL.md (OpenClaw workspace) |

OpenClaw Setup (Recommended)

OpenClaw is the primary platform for this skill. It uses workspace-based prompt injection with automatic skill loading.

Installation

Via ClawdHub (recommended):

clawdhub install self-improving-agent

Manual:

git clone https://github.com/peterskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent

Remade for openclaw from original repo : https://github.com/pskoett/pskoett-ai-skills - https://github.com/pskoett/pskoett-ai-skills/tree/main/skills/self-improvement

Workspace Structure

OpenClaw injects these files into every session:

~/.openclaw/workspace/
├── AGENTS.md          # Multi-agent workflows, delegation patterns
├── SOUL.md            # Behavioral guidelines, personality, principles
├── TOOLS.md           # Tool capabilities, integration gotchas
├── MEMORY.md          # Long-term memory (main session only)
├── memory/            # Daily memory files
│   └── YYYY-MM-DD.md
└── .learnings/        # This skill's log files
    ├── LEARNINGS.md
    ├── ERRORS.md
    └── FEATURE_REQUESTS.md

Create Learning Files

mkdir -p ~/.openclaw/workspace/.learnings

Then create the log files (or copy from assets/):

• -LEARNINGS.md — corrections, knowledge gaps, best practices
• -ERRORS.md — command failures, exceptions
• -FEATURE_REQUESTS.md — user-requested capabilities

Promotion Targets

When learnings prove broadly applicable, promote them to workspace files:

| Learning Type | Promote To | Example |

|---------------|------------|---------|

| Behavioral patterns | SOUL.md | "Be concise, avoid disclaimers" |

| Workflow improvements | AGENTS.md | "Spawn sub-agents for long tasks" |

| Tool gotchas | TOOLS.md | "Git push needs auth configured first" |

Inter-Session Communication

OpenClaw provides tools to share learnings across sessions:

• -sessions_list — View active/recent sessions
• -sessions_history — Read another session's transcript
• -sessions_send — Send a learning to another session
• -sessions_spawn — Spawn a sub-agent for background work

Optional: Enable Hook

For automatic reminders at session start:

Copy hook to OpenClaw hooks directory
cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement

Enable it
openclaw hooks enable self-improvement

See references/openclaw-integration.md for complete details.

---

Generic Setup (Other Agents)

For Claude Code, Codex, Copilot, or other agents, create .learnings/ in your project:

mkdir -p .learnings

Copy templates from assets/ or create files with headers.

Add reference to agent files AGENTS.md, CLAUDE.md, or .github/copilot-instructions.md to remind yourself to log learnings. (this is an alternative to hook-based reminders)

#### Self-Improvement Workflow

When errors or corrections occur:

1. Log to .learnings/ERRORS.md, LEARNINGS.md, or FEATURE_REQUESTS.md

2. Review and promote broadly applicable learnings to:

- CLAUDE.md - project facts and conventions

- AGENTS.md - workflows and automation

- .github/copilot-instructions.md - Copilot context

Logging Format

Learning Entry

Append to .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)
-Pattern-Key: simplify.dead_code | harden.input_validation (optional, for recurring-pattern tracking)
-Recurrence-Count: 1 (optional)
-First-Seen: 2025-01-15 (optional)
-Last-Seen: 2025-01-15 (optional)

---

Error Entry

Append to .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

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

---

Feature Request Entry

Append to .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

Format: TYPE-YYYYMMDD-XXX

• -TYPE: LRN (learning), ERR (error), FEAT (feature)
• -YYYYMMDD: Current date
• -XXX: Sequential number or random 3 chars (e.g., 001, A7B)

Examples: LRN-20250115-001, ERR-20250115-A3F, FEAT-20250115-002

Resolving Entries

When an issue is fixed, update the entry:

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

Other status values:

• -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 a learning is broadly applicable (not a one-off fix), promote it to permanent 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

| Target | What Belongs There |

|--------|-------------------|

| CLAUDE.md | Project facts, conventions, gotchas for all Claude interactions |

| AGENTS.md | Agent-specific workflows, tool usage patterns, automation rules |

| .github/copilot-instructions.md | Project context and conventions for GitHub Copilot |

| SOUL.md | Behavioral guidelines, communication style, principles (OpenClaw workspace) |

| TOOLS.md | Tool capabilities, usage patterns, integration gotchas (OpenClaw workspace) |

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

Learning (verbose):

> Project uses pnpm workspaces. Attempted npm install but failed.

> Lock file is pnpm-lock.yaml. Must use pnpm install.

In CLAUDE.md (concise):

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

Learning (verbose):

> When modifying API endpoints, must regenerate TypeScript client.

> Forgetting this causes type mismatches at runtime.

In AGENTS.md (actionable):

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

Recurring Pattern Detection

If logging something similar to an existing entry:

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)

Simplify & Harden Feed

Use this workflow to ingest recurring patterns from the simplify-and-harden

skill and turn them into durable prompt guidance.

Ingestion Workflow

1. Read simplify_and_harden.learning_loop.candidates from the task summary.

2. For each candidate, use pattern_key as the stable dedupe key.

3. Search .learnings/LEARNINGS.md for an existing entry with that key:

- grep -n "Pattern-Key: <pattern_key>" .learnings/LEARNINGS.md

4. If found:

- Increment Recurrence-Count

- Update Last-Seen

- Add See Also links to related entries/tasks

5. If not found:

- Create a new LRN-... entry

- Set Source: simplify-and-harden

- Set Pattern-Key, Recurrence-Count: 1, and First-Seen/Last-Seen

Promotion Rule (System Prompt Feedback)

Promote recurring patterns into agent context/system prompt files when all are true:

• -Recurrence-Count >= 3
• -Seen across at least 2 distinct tasks
• -Occurred within a 30-day window

Promotion targets:

• -CLAUDE.md
• -AGENTS.md
• -.github/copilot-instructions.md
• -SOUL.md / TOOLS.md for OpenClaw workspace-level guidance when applicable

Write promoted rules as short prevention rules (what to do before/while coding),

not long incident write-ups.

Periodic Review

Review .learnings/ at natural breakpoints:

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

Automatically log when you notice:

Corrections (→ learning with correction category):

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

Feature Requests (→ feature request):

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

Knowledge Gaps (→ learning with knowledge_gap category):

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

Errors (→ error entry):

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

Priority Guidelines

| Priority | When to Use |

|----------|-------------|

| critical | Blocks core functionality, data loss risk, security issue |

| high | Significant impact, affects common workflows, recurring issue |

| medium | Moderate impact, workaround exists |

| low | Minor inconvenience, edge case, nice-to-have |

Area Tags

Use to filter learnings by codebase region:

| Area | Scope |

|------|-------|

| frontend | UI, components, client-side code |

| backend | API, services, server-side code |

| infra | CI/CD, deployment, Docker, cloud |

| tests | Test files, testing utilities, coverage |

| docs | Documentation, comments, READMEs |

| config | Configuration files, environment, settings |

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

Keep learnings local (per-developer):

.learnings/

Track learnings in repo (team-wide):

Don't add to .gitignore - learnings become shared knowledge.

Hybrid (track templates, ignore entries):

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

Hook Integration

Enable automatic reminders through agent hooks. This is opt-in - you must explicitly configure hooks.

Quick Setup (Claude Code / Codex)

Create .claude/settings.json in your project:

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

This injects a learning evaluation reminder after each prompt (~50-100 tokens overhead).

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

| Script | Hook Type | Purpose |

|--------|-----------|---------|

| scripts/activator.sh | UserPromptSubmit | Reminds to evaluate learnings after tasks |

| scripts/error-detector.sh | PostToolUse (Bash) | Triggers on command errors |

See references/hooks-setup.md for detailed configuration and troubleshooting.

Automatic Skill Extraction

When a learning is valuable enough to become a reusable skill, extract it using the provided helper.

Skill Extraction Criteria

A learning qualifies for skill extraction when ANY of these apply:

| Criterion | Description |

|-----------|-------------|

| Recurring | Has See Also links to 2+ similar issues |

| Verified | Status is resolved with working fix |

| Non-obvious | Required actual debugging/investigation to discover |

| Broadly applicable | Not project-specific; useful across codebases |

| User-flagged | User says "save this as a skill" or similar |

Extraction Workflow

1. Identify candidate: Learning meets extraction criteria

2. Run helper (or create manually):

   ./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

If you prefer manual creation:

1. Create skills/<skill-name>/SKILL.md

2. Use template from assets/SKILL-TEMPLATE.md

3. Follow [Agent Skills spec](https://agentskills.io/specification):

- YAML frontmatter with name and description

- Name must match folder name

- No README.md inside skill folder

Extraction Detection Triggers

Watch for these signals that a learning should become a skill:

In conversation:

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

In learning entries:

• -Multiple See Also links (recurring issue)
• -High priority + resolved status
• -Category: best_practice with broad applicability
• -User feedback praising the solution

Skill Quality Gates

Before extraction, verify:

• -[ ] 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

This skill works across different AI coding agents with agent-specific activation.

Claude Code

Activation: Hooks (UserPromptSubmit, PostToolUse) Setup: .claude/settings.json with hook configuration Detection: Automatic via hook scripts

Codex CLI

Activation: Hooks (same pattern as Claude Code) Setup: .codex/settings.json with hook configuration Detection: Automatic via hook scripts

GitHub Copilot

Activation: Manual (no hook support) Setup: Add to .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?"

Detection: Manual review at session end

OpenClaw

Activation: Workspace injection + inter-agent messaging Setup: See "OpenClaw Setup" section above Detection: Via session tools and workspace files

Agent-Agnostic Guidance

Regardless of agent, apply self-improvement when you:

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

For Copilot users, add this to your prompts when relevant:

> After completing this task, evaluate if any learnings should be logged to .learnings/ using the self-improvement skill format.

Or use quick prompts:

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