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Knowledge was outdated	Log to `.learnings/LEARNINGS.md` with category `knowledge_gap`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`
Simplify/Harden recurring patterns	Log/update `.learnings/LEARNINGS.md` with `Source: simplify-and-harden` and a stable `Pattern-Key`
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

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:

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 your project:

mkdir -p .learnings

Copy templates from

assets/

or create files with headers.

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:
- ```
CLAUDE.md
```
  - project facts and conventions
- ```
AGENTS.md
```
  - workflows and automation
- ```
.github/copilot-instructions.md
```
  - Copilot context

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

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 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

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

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
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-20250110-001
```
in 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)

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.

.learnings/LEARNINGS.md

for an existing entry with that key:

grep -n "Pattern-Key: <pattern_key>" .learnings/LEARNINGS.md

If found:
- Increment
```
Recurrence-Count
```
- Update
```
Last-Seen
```
- Add
```
See Also
```
  links to related entries/tasks

If not found:

Create a new
```
LRN-...
```
entry
Set
```
Source: simplify-and-harden
```

Set

Pattern-Key

Recurrence-Count: 1

, and

First-Seen

Last-Seen

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

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

Log immediately - context is freshest right after the issue
Be specific - future agents need to understand quickly
Include reproduction steps - especially for errors
Link related files - makes fixes easier
Suggest concrete fixes - not just "investigate"
Use consistent categories - enables filtering
Promote aggressively - if in doubt, add to CLAUDE.md or .github/copilot-instructions.md
Review regularly - stale learnings lose value

Gitignore Options

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

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).

Full 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"
      }]
    }]
  }
}

Available Hook Scripts

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.

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:

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

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-name>/SKILL.md
```
Use template from
```
assets/SKILL-TEMPLATE.md
```
Follow Agent Skills spec:
- YAML frontmatter with
```
name
```
  and
```
description
```
- Name must match folder name
- No README.md inside skill folder

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"

Self Improving Agent

AI Skill Market Insights

Be Part of the 0+ Developer Community

Self-Improvement Skill

Quick Reference

OpenClaw Setup (Recommended)

Installation

Workspace Structure

Create Learning Files

Promotion Targets

Inter-Session Communication

Optional: Enable Hook

Enable it

Generic Setup (Other Agents)

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

Logging Format

Learning Entry

Summary

Details

Suggested Action

Metadata

Error Entry

Summary

Error

Suggested Fix

Metadata

Feature Request Entry

Requested Capability

User Context

Complexity Estimate

Suggested Implementation

Metadata

ID Generation

Resolving Entries

Promoting to Project Memory

When to Promote

Promotion Targets

How to Promote

Promotion Examples

Recurring Pattern Detection

Simplify & Harden Feed

Ingestion Workflow

Promotion Rule (System Prompt Feedback)

Periodic Review

When to Review

Quick Status Check

List pending high-priority items

Find learnings for a specific area

Review Actions

Detection Triggers

Priority Guidelines

Area Tags

Best Practices

Gitignore Options

Hook Integration

Quick Setup (Claude Code / Codex)

Full Setup (With Error Detection)

Available Hook Scripts

Automatic Skill Extraction

Skill Extraction Criteria

Extraction Workflow

Manual Extraction

Extraction Detection Triggers

Skill Quality Gates

Multi-Agent Support

Claude Code

Codex CLI

GitHub Copilot

OpenClaw

Agent-Agnostic Guidance

Copilot Chat Integration

Quick Start

Manual Installation

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