self-improving-agent-1-0-1Captures 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User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task.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.
Install via ClawdBot CLI:
clawdbot install pntrivedy/self-improving-agent-1-0-1Log 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.
| 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| Knowledge was outdated | Log to .learnings/LEARNINGS.md with category knowledge_gap |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 |
Create .learnings/ directory in project root if it doesn't exist:
mkdir -p .learnings
Copy templates from assets/ or create files with headers.
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)
---
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)
---
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
---
Format: TYPE-YYYYMMDD-XXX
LRN (learning), ERR (error), FEAT (feature)001, A7B)Examples: LRN-20250115-001, ERR-20250115-A3F, FEAT-20250115-002
When an issue is fixed, update the entry:
Status: pending → Status: resolved### 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 onwont_fix - Decided not to address (add reason in Resolution notes)promoted - Elevated to CLAUDE.md, AGENTS.md, or .github/copilot-instructions.mdWhen a learning is broadly applicable (not a one-off fix), promote it to permanent project memory.
| 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 |
Status: pending → Status: promotedPromoted: CLAUDE.md, AGENTS.md, or .github/copilot-instructions.mdLearning (verbose):
Project uses pnpm workspaces. Attempted npm install but failed.
Lock file ispnpm-lock.yaml. Must usepnpm 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`
If logging something similar to an existing entry:
grep -r "keyword" .learnings/See Also: ERR-20250110-001 in MetadataReview .learnings/ at natural breakpoints:
# 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
Automatically log when you notice:
Corrections (→ learning with correction category):
Feature Requests (→ feature request):
Knowledge Gaps (→ learning with knowledge_gap category):
Errors (→ error entry):
| 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 |
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 |
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
Enable automatic reminders through agent hooks. This is opt-in - you must explicitly configure hooks.
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).
{
"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"
}]
}]
}
}
| 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.
When a learning is valuable enough to become a reusable skill, extract it using the provided helper.
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 |
./skills/self-improvement/scripts/extract-skill.sh skill-name --dry-run
./skills/self-improvement/scripts/extract-skill.sh skill-name
promoted_to_skill, add Skill-PathIf you prefer manual creation:
skills//SKILL.md assets/SKILL-TEMPLATE.mdname and descriptionWatch for these signals that a learning should become a skill:
In conversation:
In learning entries:
See Also links (recurring issue)best_practice with broad applicabilityBefore extraction, verify:
This skill works across different AI coding agents with agent-specific activation.
Activation: Hooks (UserPromptSubmit, PostToolUse)
Setup: .claude/settings.json with hook configuration
Detection: Automatic via hook scripts
Activation: Hooks (same pattern as Claude Code)
Setup: .codex/settings.json with hook configuration
Detection: Automatic via hook scripts
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
Regardless of agent, apply self-improvement when you:
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:
Generated Mar 1, 2026
A development team uses this skill to log coding errors, API failures, and user feedback during agile sprints. It helps capture bugs and knowledge gaps for post-mortem analysis, enabling continuous improvement in code quality and team workflows.
An AI-powered customer support agent employs this skill to record incorrect responses and feature requests from users. This data is used to refine response accuracy, update knowledge bases, and prioritize new feature development based on recurring issues.
An online tutoring platform integrates this skill to track student corrections and outdated content in learning materials. It allows for systematic updates to course modules and improves instructional strategies by analyzing common misunderstandings.
A medical AI assistant uses this skill to log diagnostic errors and new medical guidelines. This supports compliance updates, reduces misdiagnoses, and enhances decision-making protocols by promoting learnings to institutional memory.
An e-commerce platform's recommendation AI applies this skill to capture failed product suggestions and user feedback. It helps optimize algorithms, address integration failures with inventory APIs, and improve personalization over time.
Offer this skill as part of a premium AI agent platform with monthly or annual subscriptions. Revenue comes from tiered pricing based on usage volume, advanced analytics, and integration support, targeting enterprises seeking continuous optimization.
Provide consulting to implement and customize this skill for clients, including setup, training, and ongoing analysis of logged data. Revenue is generated through project-based fees and retainer agreements for maintenance and improvement cycles.
Release the core skill as open source to build community adoption, then monetize through premium features like automated analytics, priority support, and cloud storage for logs. Revenue streams include one-time purchases for add-ons and enterprise licenses.
💬 Integration Tip
Start by integrating this skill in a sandbox environment to test logging workflows before deploying in production, ensuring it aligns with existing project structures and tools.
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