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Knowledge was outdated Log to .learnings/LEARNINGS.md with category knowledge_gap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

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

• 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:

• Change Status: pending → Status: resolved

• 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

• Distill the learning into a concise rule or fact

• Add to appropriate section in target file (create file if needed)

• 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:

• Search first: grep -r "keyword" .learnings/

• Link entries: Add See Also: ERR-20250110-001 in Metadata

• Bump priority if issue keeps recurring

• 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

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

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

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

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

• If found:

• Increment Recurrence-Count

• Update Last-Seen

• Add See Also links to related entries/tasks

• 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

• Log immediately - context is freshest right after the issue

• Be specific - future agents need to understand quickly

• Include reproduction steps - especially for errors

• Link related files - makes fixes easier

• Suggest concrete fixes - not just "investigate"

• Use consistent categories - enables filtering

• Promote aggressively - if in doubt, add to CLAUDE.md or .github/copilot-instructions.md

• 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

• Identify candidate: Learning meets extraction criteria

• 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

• Customize SKILL.md: Fill in template with learning content

• Update learning: Set status to promoted_to_skill , add Skill-Path

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

Manual Extraction

If you prefer manual creation:

• Create skills/<skill-name>/SKILL.md

• Use template from assets/SKILL-TEMPLATE.md

• Follow Agent Skills spec:

• 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:

• Discover something non-obvious - solution wasn't immediate

• Correct yourself - initial approach was wrong

• Learn project conventions - discovered undocumented patterns

• Hit unexpected errors - especially if diagnosis was difficult

• 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"