self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
Best use case
self-improvement is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
Teams using self-improvement should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/self-improving-agent-1-0-0/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How self-improvement Compares
| Feature / Agent | self-improvement | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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` and/or `AGENTS.md` | ## Setup Create `.learnings/` directory in project root if it doesn't exist: ```bash mkdir -p .learnings ``` Copy templates from `assets/` or create files with headers. ## Logging Format ### Learning Entry Append to `.learnings/LEARNINGS.md`: ```markdown ## [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`: ```markdown ## [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`: ```markdown ## [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: ```markdown ### 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 or AGENTS.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 | ### How to Promote 1. **Distill** the learning into a concise rule or fact 2. **Add** to appropriate section in target file 3. **Update** original entry: - Change `**Status**: pending` → `**Status**: promoted` - Add `**Promoted**: CLAUDE.md` or `**Promoted**: AGENTS.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): ```markdown ## 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): ```markdown ## 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) - 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 ```bash # 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 8. **Review regularly** - stale learnings lose value ## Gitignore Options **Keep learnings local** (per-developer): ```gitignore .learnings/ ``` **Track learnings in repo** (team-wide): Don't add to .gitignore - learnings become shared knowledge. **Hybrid** (track templates, ignore entries): ```gitignore .learnings/*.md !.learnings/.gitkeep ```
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