self-improvement
Captures learnings, errors, and corrections to enable continuous improvement across agent sessions.
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What it does
Logs failures, user corrections, knowledge gaps, and API errors to structured markdown files ( .learnings/LEARNINGS.md , ERRORS.md , FEATURE_REQUESTS.md ) with consistent ID, priority, and status tracking
Supports promotion of broadly applicable learnings to project memory files ( CLAUDE.md , AGENTS.md , .github/copilot-instructions.md ) and workspace files in OpenClaw ( SOUL.md , TOOLS.m
Installation Guide
How to use self-improvement on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
self-improvement
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches self-improvement from pskoett/self-improving-agent and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate self-improvement. Access via /self-improvement in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
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.
First-Use Initialisation
Before logging anything, ensure the .learnings/ directory and files exist in the project or workspace root. If any are missing, create them:
mkdir -p .learnings
[ -f .learnings/LEARNINGS.md ] || printf "# Learnings\n\nCorrections, insights, and knowledge gaps captured during development.\n\n**Categories**: correction | insight | knowledge_gap | best_practice\n\n---\n" > .learnings/LEARNINGS.md
[ -f .learnings/ERRORS.md ] || printf "# Errors\n\nCommand failures and integration errors.\n\n---\n" > .learnings/ERRORS.md
[ -f .learnings/FEATURE_REQUESTS.md ] || printf "# Feature Requests\n\nCapabilities requested by the user.\n\n---\n" > .learnings/FEATURE_REQUESTS.md
Never overwrite existing files. This is a no-op if .learnings/ is already initialised.
Do not log secrets, tokens, private keys, environment variables, or full source/config files unless the user explicitly asks for that level of detail.
If you want automatic reminders or setup assistance, use the opt-in hook workflow described in Hook Integration.
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 practicesERRORS.md— command failures, exceptionsFEATURE_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 the project or workspace root:
mkdir -p .learnings
Create the files inline using the headers shown above. Avoid reading templates from the current repo or workspace unless you explicitly trust that path.
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 onwont_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
- Change
Promotion Examples
Learning (verbose):
Project uses pnpm workspaces. Attempted
npm installbut 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`
Recurring Pattern Detection
If logging something similar to an existing entry:
- Search first:
grep -r "keyword" .learnings/ - Link entries: Add
**See Also**: ERR-20250110-001in 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 "^## \["
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Get started →Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- NNeel Sanchez★★★★★Dec 24, 2024
We added self-improvement from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAnaya Liu★★★★★Dec 12, 2024
self-improvement reduced setup friction for our internal harness; good balance of opinion and flexibility.
- AArjun Okafor★★★★★Nov 15, 2024
Solid pick for teams standardizing on skills: self-improvement is focused, and the summary matches what you get after install.
- HHassan Singh★★★★★Oct 6, 2024
self-improvement has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAnika Ndlovu★★★★★Sep 25, 2024
Keeps context tight: self-improvement is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AArjun Perez★★★★★Sep 13, 2024
Useful defaults in self-improvement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- JJin White★★★★★Sep 9, 2024
I recommend self-improvement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- RRahul Santra★★★★★Sep 1, 2024
I recommend self-improvement for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- JJin Kim★★★★★Aug 28, 2024
Useful defaults in self-improvement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- PPratham Ware★★★★★Aug 20, 2024
Useful defaults in self-improvement — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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