self-improving-agent-1-0-2Captures 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User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task.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 TXMERLXN/self-improving-agent-1-0-2Log 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| Knowledge was outdated | Log to .learnings/LEARNINGS.md with category knowledge_gap |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 |
| Workflow improvements | Promote to AGENTS.md (clawdbot workspace) |
| Tool gotchas | Promote to TOOLS.md (clawdbot workspace) |
| Behavioral patterns | Promote to SOUL.md (clawdbot workspace) |
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 ā 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 |
| SOUL.md | Behavioral guidelines, communication style, principles (clawdbot) |
| TOOLS.md | Tool capabilities, usage patterns, integration gotchas (clawdbot) |
Status: pending ā 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 (ā learning with correction category):
Feature Requests (ā feature request):
Knowledge Gaps (ā learning with knowledge_gap category):
Errors (ā 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
Activation: Workspace injection + inter-agent messaging
Setup: Configure workspace path in ~/.clawdbot/clawdbot.json
Detection: Via session tools and workspace files (AGENTS.md, SOUL.md, TOOLS.md)
Clawdbot uses a workspace-based model with injected prompt files. See references/clawdbot-integration.md for detailed setup.
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:
Clawdbot uses workspace-based prompt injection with specialized files for different concerns.
~/clawd/ # Default workspace (configurable)
āāā AGENTS.md # Multi-agent workflows, delegation patterns
āāā SOUL.md # Behavioral guidelines, communication style
āāā TOOLS.md # Tool capabilities, MCP integrations
āāā sessions/ # Session transcripts (auto-managed)
| Learning Type | Promote To | Example |
|--------------|------------|---------|
| Agent coordination | AGENTS.md | "Delegate file searches to explore agent" |
| Communication style | SOUL.md | "Be concise, avoid disclaimers" |
| Tool gotchas | TOOLS.md | "MCP server X requires auth refresh" |
| Project facts | CLAUDE.md | Standard project conventions |
Clawdbot supports session-based communication:
When using both:
.learnings/ for project-specific learningsSee references/clawdbot-integration.md for complete setup, promotion formats, and troubleshooting.
Generated Mar 1, 2026
A development team uses this skill to log coding errors, user feedback, and API failures during agile sprints. It helps maintain a knowledge base for onboarding new developers and AI assistants, reducing repeated mistakes and improving code quality over time.
An AI-powered customer support agent employs this skill to capture user corrections and feature requests from chat interactions. This enables continuous improvement of response accuracy and identifies gaps in knowledge bases or tool integrations for better service.
DevOps engineers integrate this skill to log infrastructure failures, outdated configurations, and best practices discovered during deployments. It aids in creating runbooks and automating fixes, enhancing system reliability and team collaboration.
Content creators and editors use this skill to track corrections in drafts, outdated information, and user requests for new content types. It streamlines workflow improvements and ensures consistency across marketing materials or documentation.
Offer this skill as part of a premium AI agent platform with monthly or annual subscriptions. Revenue comes from tiered pricing based on usage limits, advanced analytics, and integration support, targeting businesses seeking continuous AI improvement.
Provide consulting services to implement and customize this skill for specific client needs, such as integrating with existing tools or training teams. Revenue is generated through project-based fees and ongoing maintenance contracts.
Release the core skill as open source to build community adoption, then monetize through premium features like advanced analytics, automated promotion to project memory, or enterprise support. Revenue streams include paid add-ons and support packages.
š¬ Integration Tip
Start by setting up the .learnings directory and logging a few test entries to familiarize with the format; then integrate into existing workflows like code reviews or support tickets for seamless adoption.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.
Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection