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\nCategories: 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. Prefer short summaries or redacted excerpts over raw command output or full transcripts.

If you want automatic reminders or setup assistance, use the opt-in hook workflow described in Hook Integration.

Quick Reference

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

Remade for openclaw from original repo : https://github.com/pskoett/pskoett-ai-skills - https://github.com/pskoett/pskoett-ai-skills/tree/main/skills/self-improvement

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:

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

Use these only in trusted environments and only when the user explicitly wants cross-session sharing. Prefer sending a short sanitized summary and relevant file paths, not raw transcripts, secrets, or full command output.

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.

Add reference to agent files AGENTS.md, CLAUDE.md, or .github/copilot-instructions.md to remind yourself to log learnings. (this is an alternative to hook-based reminders)

Self-Improvement Workflow

When errors or corrections occur:

• Log to .learnings/ERRORS.md, LEARNINGS.md, or FEATURE_REQUESTS.md
• Review and promote broadly applicable learnings to:

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)
Pattern-Key: simplify.dead_code | harden.input_validation (optional, for recurring-pattern tracking)
Recurrence-Count: 1 (optional)
First-Seen: 2025-01-15 (optional)
Last-Seen: 2025-01-15 (optional)

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

Context
Command/operation attempted
Input or parameters used
Environment details if relevant
Summary or redacted excerpt of relevant output (avoid full transcripts and secret-bearing data by default)
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

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:

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
Regenerate client: pnpm run generate:api
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:

Simplify & Harden Feed

Use this workflow to ingest recurring patterns from the simplify-and-harden skill and turn them into durable prompt guidance.

Ingestion Workflow

• Read simplify_and_harden.learning_loop.candidates from the task summary.
• For each candidate, use pattern_key as the stable dedupe key.
• Search .learnings/LEARNINGS.md for an existing entry with that key:

• If found:

• If not found:

Promotion Rule (System Prompt Feedback)

Promote recurring patterns into agent context/system prompt files when all are true:

• Recurrence-Count >= 3
• Seen across at least 2 distinct tasks
• Occurred within a 30-day window

Promotion targets:

• CLAUDE.md
• AGENTS.md
• .github/copilot-instructions.md
• SOUL.md / TOOLS.md for OpenClaw workspace-level guidance when applicable

Write promoted rules as short prevention rules (what to do before/while coding), not long incident write-ups.

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

.learnings/

.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).

Advanced 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"
      }]
    }]
  }
}

This is optional. The recommended default is activator-only setup; enable PostToolUse only if you are comfortable with hook scripts inspecting command output for error patterns.

Available Hook Scripts

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:

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:

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/:
Use format from self-improvement skill
Link related entries with See Also
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"