self-improvement

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API/external tool fails Log to .learnings/ERRORS.md with integration details Knowledge was outdated Log to .learnings/LEARNINGS.md with category knowledge_gap Found better approach Log to .learnings/LEARNINGS.md with category best_practice Similar to existing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 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. 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) --- 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) 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.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" Weekly Installs4.6KRepositorypskoett/self-im…ng-agentGitHub Stars6First SeenFeb 20, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled onopencode4.5Kcodex4.5Kgemini-cli4.5Kgithub-copilot4.5Kcursor4.5Kkimi-cli4.5K