How should self-improving-agent be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Claim this agent

Agent DossierCLAWHUBSafety 84/100

Xpersona Agent

self-improving-agent

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...

77.6K downloadsTrust evidence available

View on ClawHub

clawhub skill install kn70cjr952qdec1nx70zs6wefn7ynq2t:self-improving-agent

Overall rank

#62

Adoption

77.6K downloads

Trust

Unknown

Freshness

Feb 28, 2026

Freshness

Last checked Feb 28, 2026

Best For

self-improving-agent is best for general automation workflows where documented compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

CLAWHUB, CLAWHUB, runtime-metrics, public facts pack

Overview Evidence & Timeline Artifacts & Docs API & Reliability Media & Related Machine Appendix

Overview

Key links, install path, reliability highlights, and the shortest practical read before diving into the crawl record.

Self-declaredCLAWHUB

Overview

Executive Summary

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau... Capability contract not published. No trust telemetry is available yet. 77.6K downloads reported by the source. Last updated 4/15/2026.

No verified compatibility signals77.6K downloads

Trust score

Unknown

Compatibility

Profile only

Freshness

Feb 28, 2026

Vendor

Clawhub

Artifacts

Benchmarks

Last release

1.0.11

Install & run

Setup Snapshot

clawhub skill install kn70cjr952qdec1nx70zs6wefn7ynq2t:self-improving-agent

1
Install using `clawhub skill install kn70cjr952qdec1nx70zs6wefn7ynq2t:self-improving-agent` in an isolated environment before connecting it to live workloads.
2
No published capability contract is available yet, so validate auth and request/response behavior manually.
3
Review the upstream CLAWHUB listing at https://clawhub.ai/pskoett/self-improving-agent before using production credentials.

Evidence & Timeline

Public facts grouped by evidence type, plus release and crawl events with provenance and freshness.

Self-declaredCLAWHUB

Public facts

Evidence Ledger

Vendor (1)

Vendor

Clawhub

profilemedium

Observed Apr 15, 2026Source link Provenance

Release (1)

Latest release

1.0.11

releasemedium

Observed Feb 22, 2026Source link Provenance

Adoption (1)

Adoption signal

77.6K downloads

profilemedium

Observed Apr 15, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Events

Release & Crawl Timeline

Release

Release 1.0.11

releasemedium

No functional or content changes; OpenClaw-specific environment metadata was removed. - Removed the OpenClaw `requires.env` metadata block from the skill definition. - All usage guidance, logging formats, and workflow instructions remain unchanged. - No new features or bug fixes included in this version. - This update does not require any action from users. - Ensures cleaner skill metadata and wider compatibility.

Observed Feb 22, 2026

Artifacts & Docs

Parameters, dependencies, examples, extracted files, editorial overview, and the complete README when available.

Self-declaredCLAWHUB

Captured outputs

Artifacts Archive

Extracted files

Examples

Snippets

Languages

Unknown

Executable Examples

bash

clawdhub install self-improving-agent

bash

git clone https://github.com/peterskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent

text

~/.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

bash

mkdir -p ~/.openclaw/workspace/.learnings

bash

# Copy hook to OpenClaw hooks directory
cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement

# Enable it
openclaw hooks enable self-improvement

bash

mkdir -p .learnings

Extracted Files

SKILL.md

---
name: self-improvement
description: "Captures 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."
metadata:
---

# 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):**
```bash
clawdhub install self-improving-agent
```

**Manual:**
```bash
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

```bash
mkdir -p ~/.openclaw/workspace/.learnings
```

Then create the

_meta.json

{
  "ownerId": "kn70cjr952qdec1nx70zs6wefn7ynq2t",
  "slug": "self-improving-agent",
  "version": "1.0.11",
  "publishedAt": 1771777713337
}

references/examples.md

# Entry Examples

Concrete examples of well-formatted entries with all fields.

## Learning: Correction

```markdown
## [LRN-20250115-001] correction

**Logged**: 2025-01-15T10:30:00Z
**Priority**: high
**Status**: pending
**Area**: tests

### Summary
Incorrectly assumed pytest fixtures are scoped to function by default

### Details
When writing test fixtures, I assumed all fixtures were function-scoped. 
User corrected that while function scope is the default, the codebase 
convention uses module-scoped fixtures for database connections to 
improve test performance.

### Suggested Action
When creating fixtures that involve expensive setup (DB, network), 
check existing fixtures for scope patterns before defaulting to function scope.

### Metadata
- Source: user_feedback
- Related Files: tests/conftest.py
- Tags: pytest, testing, fixtures

---
```

## Learning: Knowledge Gap (Resolved)

```markdown
## [LRN-20250115-002] knowledge_gap

**Logged**: 2025-01-15T14:22:00Z
**Priority**: medium
**Status**: resolved
**Area**: config

### Summary
Project uses pnpm not npm for package management

### Details
Attempted to run `npm install` but project uses pnpm workspaces.
Lock file is `pnpm-lock.yaml`, not `package-lock.json`.

### Suggested Action
Check for `pnpm-lock.yaml` or `pnpm-workspace.yaml` before assuming npm.
Use `pnpm install` for this project.

### Metadata
- Source: error
- Related Files: pnpm-lock.yaml, pnpm-workspace.yaml
- Tags: package-manager, pnpm, setup

### Resolution
- **Resolved**: 2025-01-15T14:30:00Z
- **Commit/PR**: N/A - knowledge update
- **Notes**: Added to CLAUDE.md for future reference

---
```

## Learning: Promoted to CLAUDE.md

```markdown
## [LRN-20250115-003] best_practice

**Logged**: 2025-01-15T16:00:00Z
**Priority**: high
**Status**: promoted
**Promoted**: CLAUDE.md
**Area**: backend

### Summary
API responses must include correlation ID from request headers

### Details
All API responses should echo back the X-Correlation-ID header from 
the request. This is required for distributed tracing. Responses 
without this header break the observability pipeline.

### Suggested Action
Always include correlation ID passthrough in API handlers.

### Metadata
- Source: user_feedback
- Related Files: src/middleware/correlation.ts
- Tags: api, observability, tracing

---
```

## Learning: Promoted to AGENTS.md

```markdown
## [LRN-20250116-001] best_practice

**Logged**: 2025-01-16T09:00:00Z
**Priority**: high
**Status**: promoted
**Promoted**: AGENTS.md
**Area**: backend

### Summary
Must regenerate API client after OpenAPI spec changes

### Details
When modifying API endpoints, the TypeScript client must be regenerated.
Forgetting this causes type mismatches that only appear at runtime.
The generate script also runs validation.

### Suggested Action
Add to agent workflow: after any API changes, run `pnpm run generate:api`.

### Metadata
- Source: error
- Related Files: openapi.yaml, src/client/api.ts
- Tags: api, codegen, type

references/hooks-setup.md

# Hook Setup Guide

Configure automatic self-improvement triggers for AI coding agents.

## Overview

Hooks enable proactive learning capture by injecting reminders at key moments:
- **UserPromptSubmit**: Reminder after each prompt to evaluate learnings
- **PostToolUse (Bash)**: Error detection when commands fail

## Claude Code Setup

### Option 1: Project-Level Configuration

Create `.claude/settings.json` in your project root:

```json
{
  "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"
          }
        ]
      }
    ]
  }
}
```

### Option 2: User-Level Configuration

Add to `~/.claude/settings.json` for global activation:

```json
{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}
```

### Minimal Setup (Activator Only)

For lower overhead, use only the UserPromptSubmit hook:

```json
{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "./skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}
```

## Codex CLI Setup

Codex uses the same hook system as Claude Code. Create `.codex/settings.json`:

```json
{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "./skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}
```

## GitHub Copilot Setup

Copilot doesn't support hooks directly. Instead, add guidance to `.github/copilot-instructions.md`:

```markdown
## Self-Improvement

After completing tasks that involved:
- Debugging non-obvious issues
- Discovering workarounds
- Learning project-specific patterns
- Resolving unexpected errors

Consider logging the learning to `.learnings/` using the format from the self-improvement skill.

For high-value learnings that would benefit other sessions, consider skill extraction.
```

## Verification

### Test Activator Hook

1. Enable the hook configuration
2. Start a new Claude Code session
3. Send any prompt
4. Verify you see `<self-improvement-reminder>` in the context

### Test Error Detector Hook

1. Enable PostToolUse hook for Bash
2. Run a command that fails: `ls /nonexistent/path`
3. Verify you see `<error-detected>` reminder

### Dry Run Extract Script

```bash
./skills/self-improvement/scripts/extract-skill.sh test-skill --dry-run
```

Expected

references/openclaw-integration.md

# OpenClaw Integration

Complete setup and usage guide for integrating the self-improvement skill with OpenClaw.

## Overview

OpenClaw uses workspace-based prompt injection combined with event-driven hooks. Context is injected from workspace files at session start, and hooks can trigger on lifecycle events.

## Workspace Structure

```
~/.openclaw/                      
├── workspace/                   # Working directory
│   ├── AGENTS.md               # Multi-agent coordination patterns
│   ├── SOUL.md                 # Behavioral guidelines and personality
│   ├── TOOLS.md                # Tool capabilities and gotchas
│   ├── MEMORY.md               # Long-term memory (main session only)
│   └── memory/                 # Daily memory files
│       └── YYYY-MM-DD.md
├── skills/                      # Installed skills
│   └── <skill-name>/
│       └── SKILL.md
└── hooks/                       # Custom hooks
    └── <hook-name>/
        ├── HOOK.md
        └── handler.ts
```

## Quick Setup

### 1. Install the Skill

```bash
clawdhub install self-improving-agent
```

Or copy manually:

```bash
cp -r self-improving-agent ~/.openclaw/skills/
```

### 2. Install the Hook (Optional)

Copy the hook to OpenClaw's hooks directory:

```bash
cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement
```

Enable the hook:

```bash
openclaw hooks enable self-improvement
```

### 3. Create Learning Files

Create the `.learnings/` directory in your workspace:

```bash
mkdir -p ~/.openclaw/workspace/.learnings
```

Or in the skill directory:

```bash
mkdir -p ~/.openclaw/skills/self-improving-agent/.learnings
```

## Injected Prompt Files

### AGENTS.md

Purpose: Multi-agent workflows and delegation patterns.

```markdown
# Agent Coordination

## Delegation Rules
- Use explore agent for open-ended codebase questions
- Spawn sub-agents for long-running tasks
- Use sessions_send for cross-session communication

## Session Handoff
When delegating to another session:
1. Provide full context in the handoff message
2. Include relevant file paths
3. Specify expected output format
```

### SOUL.md

Purpose: Behavioral guidelines and communication style.

```markdown
# Behavioral Guidelines

## Communication Style
- Be direct and concise
- Avoid unnecessary caveats and disclaimers
- Use technical language appropriate to context

## Error Handling
- Admit mistakes promptly
- Provide corrected information immediately
- Log significant errors to learnings
```

### TOOLS.md

Purpose: Tool capabilities, integration gotchas, local configuration.

```markdown
# Tool Knowledge

## Self-Improvement Skill
Log learnings to `.learnings/` for continuous improvement.

## Local Tools
- Document tool-specific gotchas here
- Note authentication requirements
- Track integration quirks
```

## Learning Workflow

### Capturing Learnings

1. **In-session**: Log to `.learnings/` as usual
2. **Cross-session**: Promote to workspace files

### Promotion Decision Tree

```
Is the learning projec

Editorial read

Docs & README

Docs source

CLAWHUB

Editorial quality

thin

Skill: self-improving-agent Owner: pskoett Summary: Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau... Tags: latest:1.0.11 Version history: v1.0.11 | 2026-02-22T16:28:33.337Z | user No functional or content changes; OpenClaw-specific environment metadata was removed. - Removed the OpenClaw requires.env metada

Full README

Skill: self-improving-agent

Owner: pskoett

Summary: Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Clau...

Tags: latest:1.0.11

Version history:

v1.0.11 | 2026-02-22T16:28:33.337Z | user

No functional or content changes; OpenClaw-specific environment metadata was removed.

Removed the OpenClaw requires.env metadata block from the skill definition.
All usage guidance, logging formats, and workflow instructions remain unchanged.
No new features or bug fixes included in this version.
This update does not require any action from users.
Ensures cleaner skill metadata and wider compatibility.

v1.0.10 | 2026-02-21T21:34:25.365Z | user

self-improving-agent v1.0.10

Added attribution: notes that this skill was remade for OpenClaw from the original repository (pskoett-ai-skills).
No functional or structural changes to the skill—documentation only update.
No code files were changed in this version.

v1.0.9 | 2026-02-21T20:43:11.283Z | user

Added OpenClaw integration metadata to SKILL.md (metadata: openclaw: requires: env: [CLAUDE_TOOL_OUTPUT])
No changes to general skill functionality or logging workflows
This update enables better compatibility and environment validation for OpenClaw users

v1.0.8 | 2026-02-21T20:36:06.961Z | user

self-improving-agent 1.0.8

Clarified that referencing agent files (AGENTS.md, CLAUDE.md, or .github/copilot-instructions.md) is an alternative to hook-based reminders in the generic setup section.
No code or file changes; documentation only.

v1.0.7 | 2026-02-21T18:12:28.113Z | user

Version 1.0.7

Added setup guidance: Now includes instructions to reference agent files (AGENTS.md, CLAUDE.md, or .github/copilot-instructions.md) to remind logging of learnings.
Introduced a new "Self-Improvement Workflow" section for logging and promoting learnings.
Clarified promotion steps for broadly applicable learnings, especially for non-OpenClaw environments.
No code or file changes; documentation only update.

v1.0.6 | 2026-02-21T17:22:45.619Z | user

self-improving-agent 1.0.6 changelog:

Added support for recurring pattern tracking: now supports logging and updating learnings with a stable Pattern-Key and new metadata fields like Recurrence-Count, First-Seen, and Last-Seen.
Introduced a "simplify-and-harden" source for learnings, enabling simplified/hardened patterns to be tracked and improved over time.
Updated Quick Reference and Learning Entry format to reflect new pattern tracking options.
No code or file structure changes; documentation-only update.

v1.0.5 | 2026-02-03T07:20:44.219Z | user

fixed hook sub-agent bug by removing hook for sub-agent processes

v1.0.4 | 2026-01-31T12:39:00.016Z | user

Added detailed OpenClaw integration instructions, including workspace structure, installation methods, and inter-session communication tools.
Introduced dedicated section for OpenClaw setup and workflow, separating generic and OpenClaw-specific usage.
Included instructions for enabling automatic prompts via OpenClaw session hooks.
Removed Clawdhub metadata file (.clawdhub/origin.json) from the repository.
Clarified file organization and promotion targets for learnings within the OpenClaw workspace.

v1.0.3 | 2026-01-31T11:04:05.160Z | auto

Initial OpenClaw integration: added OpenClaw hooks and documentation files.
Rebranded workspace references from "clawdbot" to "OpenClaw" throughout documentation.
Introduced new learnings directory structure and logging templates for errors, feature requests, and learnings.
Updated and extended instructions in SKILL.md regarding entry promotion, review, and recurring pattern detection.
Removed legacy reference to clawdbot integration.

v1.0.2 | 2026-01-26T09:42:32.012Z | auto

Added guidelines for promoting workflow improvements, tool gotchas, and behavioral patterns to new clawdbot workspace files (AGENTS.md, TOOLS.md, SOUL.md).
Updated promotion targets and instructions to include SOUL.md and TOOLS.md for better organization of learning types.
Included references to clawdbot integration throughout documentation.
No changes to entry format or basic logging workflow.

v1.0.1 | 2026-01-19T21:56:47.396Z | auto

self-improving-agent v1.0.1

Added 7 new files, including structured templates (assets/SKILL-TEMPLATE.md, assets/LEARNINGS.md), documentation for examples and hooks, and scripts for error detection and skill extraction.
Removed unscoped/old files (LEARNINGS.md, examples.md) in favor of new asset and reference structure.
SKILL.md updated: clarified promotion targets to include .github/copilot-instructions.md alongside CLAUDE.md and AGENTS.md, improved instructions for file promotion and creation.
Improved modularity and usability by providing templates and scripts to assist logging and review workflows.

v1.0.0 | 2026-01-05T17:03:18.365Z

Archive index:

Archive v1.0.11: 16 files, 24314 bytes

Files: .learnings/ERRORS.md (75b), .learnings/FEATURE_REQUESTS.md (84b), .learnings/LEARNINGS.md (99b), assets/LEARNINGS.md (1152b), assets/SKILL-TEMPLATE.md (3407b), hooks/openclaw/handler.js (1620b), hooks/openclaw/handler.ts (1872b), hooks/openclaw/HOOK.md (589b), references/examples.md (8291b), references/hooks-setup.md (4867b), references/openclaw-integration.md (5638b), scripts/activator.sh (680b), scripts/error-detector.sh (1317b), scripts/extract-skill.sh (5293b), SKILL.md (19704b), _meta.json (140b)

File v1.0.11:SKILL.md

name: self-improvement description: "Captures 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." metadata:

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.
Search .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/`:
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

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"

File v1.0.11:_meta.json

{ "ownerId": "kn70cjr952qdec1nx70zs6wefn7ynq2t", "slug": "self-improving-agent", "version": "1.0.11", "publishedAt": 1771777713337 }

File v1.0.11:references/examples.md

Entry Examples

Concrete examples of well-formatted entries with all fields.

Learning: Correction

## [LRN-20250115-001] correction

**Logged**: 2025-01-15T10:30:00Z
**Priority**: high
**Status**: pending
**Area**: tests

### Summary
Incorrectly assumed pytest fixtures are scoped to function by default

### Details
When writing test fixtures, I assumed all fixtures were function-scoped. 
User corrected that while function scope is the default, the codebase 
convention uses module-scoped fixtures for database connections to 
improve test performance.

### Suggested Action
When creating fixtures that involve expensive setup (DB, network), 
check existing fixtures for scope patterns before defaulting to function scope.

### Metadata
- Source: user_feedback
- Related Files: tests/conftest.py
- Tags: pytest, testing, fixtures

---

Learning: Knowledge Gap (Resolved)

## [LRN-20250115-002] knowledge_gap

**Logged**: 2025-01-15T14:22:00Z
**Priority**: medium
**Status**: resolved
**Area**: config

### Summary
Project uses pnpm not npm for package management

### Details
Attempted to run `npm install` but project uses pnpm workspaces.
Lock file is `pnpm-lock.yaml`, not `package-lock.json`.

### Suggested Action
Check for `pnpm-lock.yaml` or `pnpm-workspace.yaml` before assuming npm.
Use `pnpm install` for this project.

### Metadata
- Source: error
- Related Files: pnpm-lock.yaml, pnpm-workspace.yaml
- Tags: package-manager, pnpm, setup

### Resolution
- **Resolved**: 2025-01-15T14:30:00Z
- **Commit/PR**: N/A - knowledge update
- **Notes**: Added to CLAUDE.md for future reference

---

Learning: Promoted to CLAUDE.md

## [LRN-20250115-003] best_practice

**Logged**: 2025-01-15T16:00:00Z
**Priority**: high
**Status**: promoted
**Promoted**: CLAUDE.md
**Area**: backend

### Summary
API responses must include correlation ID from request headers

### Details
All API responses should echo back the X-Correlation-ID header from 
the request. This is required for distributed tracing. Responses 
without this header break the observability pipeline.

### Suggested Action
Always include correlation ID passthrough in API handlers.

### Metadata
- Source: user_feedback
- Related Files: src/middleware/correlation.ts
- Tags: api, observability, tracing

---

Learning: Promoted to AGENTS.md

## [LRN-20250116-001] best_practice

**Logged**: 2025-01-16T09:00:00Z
**Priority**: high
**Status**: promoted
**Promoted**: AGENTS.md
**Area**: backend

### Summary
Must regenerate API client after OpenAPI spec changes

### Details
When modifying API endpoints, the TypeScript client must be regenerated.
Forgetting this causes type mismatches that only appear at runtime.
The generate script also runs validation.

### Suggested Action
Add to agent workflow: after any API changes, run `pnpm run generate:api`.

### Metadata
- Source: error
- Related Files: openapi.yaml, src/client/api.ts
- Tags: api, codegen, typescript

---

Error Entry

## [ERR-20250115-A3F] docker_build

**Logged**: 2025-01-15T09:15:00Z
**Priority**: high
**Status**: pending
**Area**: infra

### Summary
Docker build fails on M1 Mac due to platform mismatch

### Error

error: failed to solve: python:3.11-slim: no match for platform linux/arm64


### Context
- Command: `docker build -t myapp .`
- Dockerfile uses `FROM python:3.11-slim`
- Running on Apple Silicon (M1/M2)

### Suggested Fix
Add platform flag: `docker build --platform linux/amd64 -t myapp .`
Or update Dockerfile: `FROM --platform=linux/amd64 python:3.11-slim`

### Metadata
- Reproducible: yes
- Related Files: Dockerfile

---

Error Entry: Recurring Issue

## [ERR-20250120-B2C] api_timeout

**Logged**: 2025-01-20T11:30:00Z
**Priority**: critical
**Status**: pending
**Area**: backend

### Summary
Third-party payment API timeout during checkout

### Error

TimeoutError: Request to payments.example.com timed out after 30000ms


### Context
- Command: POST /api/checkout
- Timeout set to 30s
- Occurs during peak hours (lunch, evening)

### Suggested Fix
Implement retry with exponential backoff. Consider circuit breaker pattern.

### Metadata
- Reproducible: yes (during peak hours)
- Related Files: src/services/payment.ts
- See Also: ERR-20250115-X1Y, ERR-20250118-Z3W

---

Feature Request

## [FEAT-20250115-001] export_to_csv

**Logged**: 2025-01-15T16:45:00Z
**Priority**: medium
**Status**: pending
**Area**: backend

### Requested Capability
Export analysis results to CSV format

### User Context
User runs weekly reports and needs to share results with non-technical 
stakeholders in Excel. Currently copies output manually.

### Complexity Estimate
simple

### Suggested Implementation
Add `--output csv` flag to the analyze command. Use standard csv module.
Could extend existing `--output json` pattern.

### Metadata
- Frequency: recurring
- Related Features: analyze command, json output

---

Feature Request: Resolved

## [FEAT-20250110-002] dark_mode

**Logged**: 2025-01-10T14:00:00Z
**Priority**: low
**Status**: resolved
**Area**: frontend

### Requested Capability
Dark mode support for the dashboard

### User Context
User works late hours and finds the bright interface straining.
Several other users have mentioned this informally.

### Complexity Estimate
medium

### Suggested Implementation
Use CSS variables for colors. Add toggle in user settings.
Consider system preference detection.

### Metadata
- Frequency: recurring
- Related Features: user settings, theme system

### Resolution
- **Resolved**: 2025-01-18T16:00:00Z
- **Commit/PR**: #142
- **Notes**: Implemented with system preference detection and manual toggle

---

Learning: Promoted to Skill

## [LRN-20250118-001] best_practice

**Logged**: 2025-01-18T11:00:00Z
**Priority**: high
**Status**: promoted_to_skill
**Skill-Path**: skills/docker-m1-fixes
**Area**: infra

### Summary
Docker build fails on Apple Silicon due to platform mismatch

### Details
When building Docker images on M1/M2 Macs, the build fails because
the base image doesn't have an ARM64 variant. This is a common issue
that affects many developers.

### Suggested Action
Add `--platform linux/amd64` to docker build command, or use
`FROM --platform=linux/amd64` in Dockerfile.

### Metadata
- Source: error
- Related Files: Dockerfile
- Tags: docker, arm64, m1, apple-silicon
- See Also: ERR-20250115-A3F, ERR-20250117-B2D

---

Extracted Skill Example

When the above learning is extracted as a skill, it becomes:

File: skills/docker-m1-fixes/SKILL.md

---
name: docker-m1-fixes
description: "Fixes Docker build failures on Apple Silicon (M1/M2). Use when docker build fails with platform mismatch errors."
---

# Docker M1 Fixes

Solutions for Docker build issues on Apple Silicon Macs.

## Quick Reference

| Error | Fix |
|-------|-----|
| `no match for platform linux/arm64` | Add `--platform linux/amd64` to build |
| Image runs but crashes | Use emulation or find ARM-compatible base |

## The Problem

Many Docker base images don't have ARM64 variants. When building on
Apple Silicon (M1/M2/M3), Docker attempts to pull ARM64 images by
default, causing platform mismatch errors.

## Solutions

### Option 1: Build Flag (Recommended)

Add platform flag to your build command:

\`\`\`bash
docker build --platform linux/amd64 -t myapp .
\`\`\`

### Option 2: Dockerfile Modification

Specify platform in the FROM instruction:

\`\`\`dockerfile
FROM --platform=linux/amd64 python:3.11-slim
\`\`\`

### Option 3: Docker Compose

Add platform to your service:

\`\`\`yaml
services:
  app:
    platform: linux/amd64
    build: .
\`\`\`

## Trade-offs

| Approach | Pros | Cons |
|----------|------|------|
| Build flag | No file changes | Must remember flag |
| Dockerfile | Explicit, versioned | Affects all builds |
| Compose | Convenient for dev | Requires compose |

## Performance Note

Running AMD64 images on ARM64 uses Rosetta 2 emulation. This works
for development but may be slower. For production, find ARM-native
alternatives when possible.

## Source

- Learning ID: LRN-20250118-001
- Category: best_practice
- Extraction Date: 2025-01-18

File v1.0.11:references/hooks-setup.md

Hook Setup Guide

Configure automatic self-improvement triggers for AI coding agents.

Overview

Hooks enable proactive learning capture by injecting reminders at key moments:

UserPromptSubmit: Reminder after each prompt to evaluate learnings
PostToolUse (Bash): Error detection when commands fail

Claude Code Setup

Option 1: Project-Level Configuration

Create .claude/settings.json in your project root:

{
  "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"
          }
        ]
      }
    ]
  }
}

Option 2: User-Level Configuration

Add to ~/.claude/settings.json for global activation:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}

Minimal Setup (Activator Only)

For lower overhead, use only the UserPromptSubmit hook:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "./skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}

Codex CLI Setup

Codex uses the same hook system as Claude Code. Create .codex/settings.json:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "./skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}

GitHub Copilot Setup

Copilot doesn't support hooks directly. Instead, add guidance to .github/copilot-instructions.md:

## Self-Improvement

After completing tasks that involved:
- Debugging non-obvious issues
- Discovering workarounds
- Learning project-specific patterns
- Resolving unexpected errors

Consider logging the learning to `.learnings/` using the format from the self-improvement skill.

For high-value learnings that would benefit other sessions, consider skill extraction.

Verification

Test Activator Hook

Enable the hook configuration
Start a new Claude Code session
Send any prompt
Verify you see <self-improvement-reminder> in the context

Test Error Detector Hook

Enable PostToolUse hook for Bash
Run a command that fails: ls /nonexistent/path
Verify you see <error-detected> reminder

Dry Run Extract Script

./skills/self-improvement/scripts/extract-skill.sh test-skill --dry-run

Expected output shows the skill scaffold that would be created.

Troubleshooting

Hook Not Triggering

Check script permissions: chmod +x scripts/*.sh
Verify path: Use absolute paths or paths relative to project root
Check settings location: Project vs user-level settings
Restart session: Hooks are loaded at session start

Permission Denied

chmod +x ./skills/self-improvement/scripts/activator.sh
chmod +x ./skills/self-improvement/scripts/error-detector.sh
chmod +x ./skills/self-improvement/scripts/extract-skill.sh

Script Not Found

If using relative paths, ensure you're in the correct directory or use absolute paths:

{
  "command": "/absolute/path/to/skills/self-improvement/scripts/activator.sh"
}

Too Much Overhead

If the activator feels intrusive:

Use minimal setup: Only UserPromptSubmit, skip PostToolUse
Add matcher filter: Only trigger for certain prompts:

{
  "matcher": "fix|debug|error|issue",
  "hooks": [...]
}

Hook Output Budget

The activator is designed to be lightweight:

Target: ~50-100 tokens per activation
Content: Structured reminder, not verbose instructions
Format: XML tags for easy parsing

If you need to reduce overhead further, you can edit activator.sh to output less text.

Security Considerations

Hook scripts run with the same permissions as Claude Code
Scripts only output text; they don't modify files or run commands
Error detector reads CLAUDE_TOOL_OUTPUT environment variable
All scripts are opt-in (you must configure them explicitly)

Disabling Hooks

To temporarily disable without removing configuration:

Comment out in settings:

{
  "hooks": {
    // "UserPromptSubmit": [...]
  }
}

Or delete the settings file: Hooks won't run without configuration

File v1.0.11:references/openclaw-integration.md

OpenClaw Integration

Complete setup and usage guide for integrating the self-improvement skill with OpenClaw.

Overview

OpenClaw uses workspace-based prompt injection combined with event-driven hooks. Context is injected from workspace files at session start, and hooks can trigger on lifecycle events.

Workspace Structure

~/.openclaw/                      
├── workspace/                   # Working directory
│   ├── AGENTS.md               # Multi-agent coordination patterns
│   ├── SOUL.md                 # Behavioral guidelines and personality
│   ├── TOOLS.md                # Tool capabilities and gotchas
│   ├── MEMORY.md               # Long-term memory (main session only)
│   └── memory/                 # Daily memory files
│       └── YYYY-MM-DD.md
├── skills/                      # Installed skills
│   └── <skill-name>/
│       └── SKILL.md
└── hooks/                       # Custom hooks
    └── <hook-name>/
        ├── HOOK.md
        └── handler.ts

Quick Setup

1. Install the Skill

clawdhub install self-improving-agent

Or copy manually:

cp -r self-improving-agent ~/.openclaw/skills/

2. Install the Hook (Optional)

Copy the hook to OpenClaw's hooks directory:

cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement

Enable the hook:

openclaw hooks enable self-improvement

3. Create Learning Files

Create the .learnings/ directory in your workspace:

mkdir -p ~/.openclaw/workspace/.learnings

Or in the skill directory:

mkdir -p ~/.openclaw/skills/self-improving-agent/.learnings

Injected Prompt Files

AGENTS.md

Purpose: Multi-agent workflows and delegation patterns.

# Agent Coordination

## Delegation Rules
- Use explore agent for open-ended codebase questions
- Spawn sub-agents for long-running tasks
- Use sessions_send for cross-session communication

## Session Handoff
When delegating to another session:
1. Provide full context in the handoff message
2. Include relevant file paths
3. Specify expected output format

SOUL.md

Purpose: Behavioral guidelines and communication style.

# Behavioral Guidelines

## Communication Style
- Be direct and concise
- Avoid unnecessary caveats and disclaimers
- Use technical language appropriate to context

## Error Handling
- Admit mistakes promptly
- Provide corrected information immediately
- Log significant errors to learnings

TOOLS.md

Purpose: Tool capabilities, integration gotchas, local configuration.

# Tool Knowledge

## Self-Improvement Skill
Log learnings to `.learnings/` for continuous improvement.

## Local Tools
- Document tool-specific gotchas here
- Note authentication requirements
- Track integration quirks

Learning Workflow

Capturing Learnings

In-session: Log to .learnings/ as usual
Cross-session: Promote to workspace files

Promotion Decision Tree

Is the learning project-specific?
├── Yes → Keep in .learnings/
└── No → Is it behavioral/style-related?
    ├── Yes → Promote to SOUL.md
    └── No → Is it tool-related?
        ├── Yes → Promote to TOOLS.md
        └── No → Promote to AGENTS.md (workflow)

Promotion Format Examples

From learning:

Git push to GitHub fails without auth configured - triggers desktop prompt

To TOOLS.md:

## Git
- Don't push without confirming auth is configured
- Use `gh auth status` to check GitHub CLI auth

Inter-Agent Communication

OpenClaw provides tools for cross-session communication:

sessions_list

View active and recent sessions:

sessions_list(activeMinutes=30, messageLimit=3)

sessions_history

Read transcript from another session:

sessions_history(sessionKey="session-id", limit=50)

sessions_send

Send message to another session:

sessions_send(sessionKey="session-id", message="Learning: API requires X-Custom-Header")

sessions_spawn

Spawn a background sub-agent:

sessions_spawn(task="Research X and report back", label="research")

Available Hook Events

| Event | When It Fires | |-------|---------------| | agent:bootstrap | Before workspace files inject | | command:new | When /new command issued | | command:reset | When /reset command issued | | command:stop | When /stop command issued | | gateway:startup | When gateway starts |

Detection Triggers

Standard Triggers

User corrections ("No, that's wrong...")
Command failures (non-zero exit codes)
API errors
Knowledge gaps

OpenClaw-Specific Triggers

| Trigger | Action | |---------|--------| | Tool call error | Log to TOOLS.md with tool name | | Session handoff confusion | Log to AGENTS.md with delegation pattern | | Model behavior surprise | Log to SOUL.md with expected vs actual | | Skill issue | Log to .learnings/ or report upstream |

Verification

Check hook is registered:

openclaw hooks list

Check skill is loaded:

openclaw status

Troubleshooting

Hook not firing

Ensure hooks enabled in config
Restart gateway after config changes
Check gateway logs for errors

Learnings not persisting

Verify .learnings/ directory exists
Check file permissions
Ensure workspace path is configured correctly

Skill not loading

Check skill is in skills directory
Verify SKILL.md has correct frontmatter
Run openclaw status to see loaded skills

File v1.0.11:.learnings/ERRORS.md

Errors Log

Command failures, exceptions, and unexpected behaviors.

File v1.0.11:.learnings/FEATURE_REQUESTS.md

Feature Requests

Capabilities requested by user that don't currently exist.

File v1.0.11:.learnings/LEARNINGS.md

Learnings Log

Captured learnings, corrections, and discoveries. Review before major tasks.

File v1.0.11:assets/LEARNINGS.md

Learnings

Corrections, insights, and knowledge gaps captured during development.

Status Definitions

| Status | Meaning | |--------|---------| | pending | Not yet addressed | | in_progress | Actively being worked on | | resolved | Issue fixed or knowledge integrated | | wont_fix | Decided not to address (reason in Resolution) | | promoted | Elevated to CLAUDE.md, AGENTS.md, or copilot-instructions.md | | promoted_to_skill | Extracted as a reusable skill |

Skill Extraction Fields

When a learning is promoted to a skill, add these fields:

**Status**: promoted_to_skill
**Skill-Path**: skills/skill-name

Example:

## [LRN-20250115-001] best_practice

**Logged**: 2025-01-15T10:00:00Z
**Priority**: high
**Status**: promoted_to_skill
**Skill-Path**: skills/docker-m1-fixes
**Area**: infra

### Summary
Docker build fails on Apple Silicon due to platform mismatch
...

File v1.0.11:assets/SKILL-TEMPLATE.md

Skill Template

Template for creating skills extracted from learnings. Copy and customize.

SKILL.md Template

---
name: skill-name-here
description: "Concise description of when and why to use this skill. Include trigger conditions."
---

# Skill Name

Brief introduction explaining the problem this skill solves and its origin.

## Quick Reference

| Situation | Action |
|-----------|--------|
| [Trigger 1] | [Action 1] |
| [Trigger 2] | [Action 2] |

## Background

Why this knowledge matters. What problems it prevents. Context from the original learning.

## Solution

### Step-by-Step

1. First step with code or command
2. Second step
3. Verification step

### Code Example

\`\`\`language
// Example code demonstrating the solution
\`\`\`

## Common Variations

- **Variation A**: Description and how to handle
- **Variation B**: Description and how to handle

## Gotchas

- Warning or common mistake #1
- Warning or common mistake #2

## Related

- Link to related documentation
- Link to related skill

## Source

Extracted from learning entry.
- **Learning ID**: LRN-YYYYMMDD-XXX
- **Original Category**: correction | insight | knowledge_gap | best_practice
- **Extraction Date**: YYYY-MM-DD

Minimal Template

For simple skills that don't need all sections:

---
name: skill-name-here
description: "What this skill does and when to use it."
---

# Skill Name

[Problem statement in one sentence]

## Solution

[Direct solution with code/commands]

## Source

- Learning ID: LRN-YYYYMMDD-XXX

Template with Scripts

For skills that include executable helpers:

---
name: skill-name-here
description: "What this skill does and when to use it."
---

# Skill Name

[Introduction]

## Quick Reference

| Command | Purpose |
|---------|---------|
| `./scripts/helper.sh` | [What it does] |
| `./scripts/validate.sh` | [What it does] |

## Usage

### Automated (Recommended)

\`\`\`bash
./skills/skill-name/scripts/helper.sh [args]
\`\`\`

### Manual Steps

1. Step one
2. Step two

## Scripts

| Script | Description |
|--------|-------------|
| `scripts/helper.sh` | Main utility |
| `scripts/validate.sh` | Validation checker |

## Source

- Learning ID: LRN-YYYYMMDD-XXX

Naming Conventions

Skill name: lowercase, hyphens for spaces
- Good: docker-m1-fixes, api-timeout-patterns
- Bad: Docker_M1_Fixes, APITimeoutPatterns
Description: Start with action verb, mention trigger
- Good: "Handles Docker build failures on Apple Silicon. Use when builds fail with platform mismatch."
- Bad: "Docker stuff"
Files:
- SKILL.md - Required, main documentation
- scripts/ - Optional, executable code
- references/ - Optional, detailed docs
- assets/ - Optional, templates

Extraction Checklist

Before creating a skill from a learning:

[ ] Learning is verified (status: resolved)
[ ] Solution is broadly applicable (not one-off)
[ ] Content is complete (has all needed context)
[ ] Name follows conventions
[ ] Description is concise but informative
[ ] Quick Reference table is actionable
[ ] Code examples are tested
[ ] Source learning ID is recorded

After creating:

[ ] Update original learning with promoted_to_skill status
[ ] Add Skill-Path: skills/skill-name to learning metadata
[ ] Test skill by reading it in a fresh session

File v1.0.11:hooks/openclaw/HOOK.md

name: self-improvement description: "Injects self-improvement reminder during agent bootstrap" metadata: {"openclaw":{"emoji":"🧠","events":["agent:bootstrap"]}}

Self-Improvement Hook

Injects a reminder to evaluate learnings during agent bootstrap.

What It Does

Fires on agent:bootstrap (before workspace files are injected)
Adds a reminder block to check .learnings/ for relevant entries
Prompts the agent to log corrections, errors, and discoveries

Configuration

No configuration needed. Enable with:

openclaw hooks enable self-improvement

Archive v1.0.10: 16 files, 24352 bytes

File v1.0.10:SKILL.md

name: self-improvement description: "Captures 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." metadata: openclaw: requires: env: - CLAUDE_TOOL_OUTPUT

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

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

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

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.
Search .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

Area Tags

Use to filter learnings by codebase region:

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

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:

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/`:
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

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"

File v1.0.10:_meta.json

{ "ownerId": "kn70cjr952qdec1nx70zs6wefn7ynq2t", "slug": "self-improving-agent", "version": "1.0.10", "publishedAt": 1771709665365 }

File v1.0.10:references/examples.md

Entry Examples

Concrete examples of well-formatted entries with all fields.

Learning: Correction

## [LRN-20250115-001] correction

**Logged**: 2025-01-15T10:30:00Z
**Priority**: high
**Status**: pending
**Area**: tests

### Summary
Incorrectly assumed pytest fixtures are scoped to function by default

### Details
When writing test fixtures, I assumed all fixtures were function-scoped. 
User corrected that while function scope is the default, the codebase 
convention uses module-scoped fixtures for database connections to 
improve test performance.

### Suggested Action
When creating fixtures that involve expensive setup (DB, network), 
check existing fixtures for scope patterns before defaulting to function scope.

### Metadata
- Source: user_feedback
- Related Files: tests/conftest.py
- Tags: pytest, testing, fixtures

---

Learning: Knowledge Gap (Resolved)

## [LRN-20250115-002] knowledge_gap

**Logged**: 2025-01-15T14:22:00Z
**Priority**: medium
**Status**: resolved
**Area**: config

### Summary
Project uses pnpm not npm for package management

### Details
Attempted to run `npm install` but project uses pnpm workspaces.
Lock file is `pnpm-lock.yaml`, not `package-lock.json`.

### Suggested Action
Check for `pnpm-lock.yaml` or `pnpm-workspace.yaml` before assuming npm.
Use `pnpm install` for this project.

### Metadata
- Source: error
- Related Files: pnpm-lock.yaml, pnpm-workspace.yaml
- Tags: package-manager, pnpm, setup

### Resolution
- **Resolved**: 2025-01-15T14:30:00Z
- **Commit/PR**: N/A - knowledge update
- **Notes**: Added to CLAUDE.md for future reference

---

Learning: Promoted to CLAUDE.md

## [LRN-20250115-003] best_practice

**Logged**: 2025-01-15T16:00:00Z
**Priority**: high
**Status**: promoted
**Promoted**: CLAUDE.md
**Area**: backend

### Summary
API responses must include correlation ID from request headers

### Details
All API responses should echo back the X-Correlation-ID header from 
the request. This is required for distributed tracing. Responses 
without this header break the observability pipeline.

### Suggested Action
Always include correlation ID passthrough in API handlers.

### Metadata
- Source: user_feedback
- Related Files: src/middleware/correlation.ts
- Tags: api, observability, tracing

---

Learning: Promoted to AGENTS.md

## [LRN-20250116-001] best_practice

**Logged**: 2025-01-16T09:00:00Z
**Priority**: high
**Status**: promoted
**Promoted**: AGENTS.md
**Area**: backend

### Summary
Must regenerate API client after OpenAPI spec changes

### Details
When modifying API endpoints, the TypeScript client must be regenerated.
Forgetting this causes type mismatches that only appear at runtime.
The generate script also runs validation.

### Suggested Action
Add to agent workflow: after any API changes, run `pnpm run generate:api`.

### Metadata
- Source: error
- Related Files: openapi.yaml, src/client/api.ts
- Tags: api, codegen, typescript

---

Error Entry

## [ERR-20250115-A3F] docker_build

**Logged**: 2025-01-15T09:15:00Z
**Priority**: high
**Status**: pending
**Area**: infra

### Summary
Docker build fails on M1 Mac due to platform mismatch

### Error

error: failed to solve: python:3.11-slim: no match for platform linux/arm64


### Context
- Command: `docker build -t myapp .`
- Dockerfile uses `FROM python:3.11-slim`
- Running on Apple Silicon (M1/M2)

### Suggested Fix
Add platform flag: `docker build --platform linux/amd64 -t myapp .`
Or update Dockerfile: `FROM --platform=linux/amd64 python:3.11-slim`

### Metadata
- Reproducible: yes
- Related Files: Dockerfile

---

Error Entry: Recurring Issue

## [ERR-20250120-B2C] api_timeout

**Logged**: 2025-01-20T11:30:00Z
**Priority**: critical
**Status**: pending
**Area**: backend

### Summary
Third-party payment API timeout during checkout

### Error

TimeoutError: Request to payments.example.com timed out after 30000ms


### Context
- Command: POST /api/checkout
- Timeout set to 30s
- Occurs during peak hours (lunch, evening)

### Suggested Fix
Implement retry with exponential backoff. Consider circuit breaker pattern.

### Metadata
- Reproducible: yes (during peak hours)
- Related Files: src/services/payment.ts
- See Also: ERR-20250115-X1Y, ERR-20250118-Z3W

---

Feature Request

## [FEAT-20250115-001] export_to_csv

**Logged**: 2025-01-15T16:45:00Z
**Priority**: medium
**Status**: pending
**Area**: backend

### Requested Capability
Export analysis results to CSV format

### User Context
User runs weekly reports and needs to share results with non-technical 
stakeholders in Excel. Currently copies output manually.

### Complexity Estimate
simple

### Suggested Implementation
Add `--output csv` flag to the analyze command. Use standard csv module.
Could extend existing `--output json` pattern.

### Metadata
- Frequency: recurring
- Related Features: analyze command, json output

---

Feature Request: Resolved

## [FEAT-20250110-002] dark_mode

**Logged**: 2025-01-10T14:00:00Z
**Priority**: low
**Status**: resolved
**Area**: frontend

### Requested Capability
Dark mode support for the dashboard

### User Context
User works late hours and finds the bright interface straining.
Several other users have mentioned this informally.

### Complexity Estimate
medium

### Suggested Implementation
Use CSS variables for colors. Add toggle in user settings.
Consider system preference detection.

### Metadata
- Frequency: recurring
- Related Features: user settings, theme system

### Resolution
- **Resolved**: 2025-01-18T16:00:00Z
- **Commit/PR**: #142
- **Notes**: Implemented with system preference detection and manual toggle

---

Learning: Promoted to Skill

## [LRN-20250118-001] best_practice

**Logged**: 2025-01-18T11:00:00Z
**Priority**: high
**Status**: promoted_to_skill
**Skill-Path**: skills/docker-m1-fixes
**Area**: infra

### Summary
Docker build fails on Apple Silicon due to platform mismatch

### Details
When building Docker images on M1/M2 Macs, the build fails because
the base image doesn't have an ARM64 variant. This is a common issue
that affects many developers.

### Suggested Action
Add `--platform linux/amd64` to docker build command, or use
`FROM --platform=linux/amd64` in Dockerfile.

### Metadata
- Source: error
- Related Files: Dockerfile
- Tags: docker, arm64, m1, apple-silicon
- See Also: ERR-20250115-A3F, ERR-20250117-B2D

---

Extracted Skill Example

When the above learning is extracted as a skill, it becomes:

File: skills/docker-m1-fixes/SKILL.md

---
name: docker-m1-fixes
description: "Fixes Docker build failures on Apple Silicon (M1/M2). Use when docker build fails with platform mismatch errors."
---

# Docker M1 Fixes

Solutions for Docker build issues on Apple Silicon Macs.

## Quick Reference

| Error | Fix |
|-------|-----|
| `no match for platform linux/arm64` | Add `--platform linux/amd64` to build |
| Image runs but crashes | Use emulation or find ARM-compatible base |

## The Problem

Many Docker base images don't have ARM64 variants. When building on
Apple Silicon (M1/M2/M3), Docker attempts to pull ARM64 images by
default, causing platform mismatch errors.

## Solutions

### Option 1: Build Flag (Recommended)

Add platform flag to your build command:

\`\`\`bash
docker build --platform linux/amd64 -t myapp .
\`\`\`

### Option 2: Dockerfile Modification

Specify platform in the FROM instruction:

\`\`\`dockerfile
FROM --platform=linux/amd64 python:3.11-slim
\`\`\`

### Option 3: Docker Compose

Add platform to your service:

\`\`\`yaml
services:
  app:
    platform: linux/amd64
    build: .
\`\`\`

## Trade-offs

| Approach | Pros | Cons |
|----------|------|------|
| Build flag | No file changes | Must remember flag |
| Dockerfile | Explicit, versioned | Affects all builds |
| Compose | Convenient for dev | Requires compose |

## Performance Note

Running AMD64 images on ARM64 uses Rosetta 2 emulation. This works
for development but may be slower. For production, find ARM-native
alternatives when possible.

## Source

- Learning ID: LRN-20250118-001
- Category: best_practice
- Extraction Date: 2025-01-18

File v1.0.10:references/hooks-setup.md

Hook Setup Guide

Configure automatic self-improvement triggers for AI coding agents.

Overview

Hooks enable proactive learning capture by injecting reminders at key moments:

UserPromptSubmit: Reminder after each prompt to evaluate learnings
PostToolUse (Bash): Error detection when commands fail

Claude Code Setup

Option 1: Project-Level Configuration

Create .claude/settings.json in your project root:

{
  "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"
          }
        ]
      }
    ]
  }
}

Option 2: User-Level Configuration

Add to ~/.claude/settings.json for global activation:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}

Minimal Setup (Activator Only)

For lower overhead, use only the UserPromptSubmit hook:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "./skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}

Codex CLI Setup

Codex uses the same hook system as Claude Code. Create .codex/settings.json:

{
  "hooks": {
    "UserPromptSubmit": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "./skills/self-improvement/scripts/activator.sh"
          }
        ]
      }
    ]
  }
}

GitHub Copilot Setup

Copilot doesn't support hooks directly. Instead, add guidance to .github/copilot-instructions.md:

## Self-Improvement

After completing tasks that involved:
- Debugging non-obvious issues
- Discovering workarounds
- Learning project-specific patterns
- Resolving unexpected errors

Consider logging the learning to `.learnings/` using the format from the self-improvement skill.

For high-value learnings that would benefit other sessions, consider skill extraction.

Verification

Test Activator Hook

Enable the hook configuration
Start a new Claude Code session
Send any prompt
Verify you see <self-improvement-reminder> in the context

Test Error Detector Hook

Enable PostToolUse hook for Bash
Run a command that fails: ls /nonexistent/path
Verify you see <error-detected> reminder

Dry Run Extract Script

./skills/self-improvement/scripts/extract-skill.sh test-skill --dry-run

Expected output shows the skill scaffold that would be created.

Troubleshooting

Hook Not Triggering

Check script permissions: chmod +x scripts/*.sh
Verify path: Use absolute paths or paths relative to project root
Check settings location: Project vs user-level settings
Restart session: Hooks are loaded at session start

Permission Denied

chmod +x ./skills/self-improvement/scripts/activator.sh
chmod +x ./skills/self-improvement/scripts/error-detector.sh
chmod +x ./skills/self-improvement/scripts/extract-skill.sh

Script Not Found

If using relative paths, ensure you're in the correct directory or use absolute paths:

{
  "command": "/absolute/path/to/skills/self-improvement/scripts/activator.sh"
}

Too Much Overhead

If the activator feels intrusive:

Use minimal setup: Only UserPromptSubmit, skip PostToolUse
Add matcher filter: Only trigger for certain prompts:

{
  "matcher": "fix|debug|error|issue",
  "hooks": [...]
}

Hook Output Budget

The activator is designed to be lightweight:

Target: ~50-100 tokens per activation
Content: Structured reminder, not verbose instructions
Format: XML tags for easy parsing

If you need to reduce overhead further, you can edit activator.sh to output less text.

Security Considerations

Hook scripts run with the same permissions as Claude Code
Scripts only output text; they don't modify files or run commands
Error detector reads CLAUDE_TOOL_OUTPUT environment variable
All scripts are opt-in (you must configure them explicitly)

Disabling Hooks

To temporarily disable without removing configuration:

Comment out in settings:

{
  "hooks": {
    // "UserPromptSubmit": [...]
  }
}

Or delete the settings file: Hooks won't run without configuration

File v1.0.10:references/openclaw-integration.md

OpenClaw Integration

Complete setup and usage guide for integrating the self-improvement skill with OpenClaw.

Overview

OpenClaw uses workspace-based prompt injection combined with event-driven hooks. Context is injected from workspace files at session start, and hooks can trigger on lifecycle events.

Workspace Structure

~/.openclaw/                      
├── workspace/                   # Working directory
│   ├── AGENTS.md               # Multi-agent coordination patterns
│   ├── SOUL.md                 # Behavioral guidelines and personality
│   ├── TOOLS.md                # Tool capabilities and gotchas
│   ├── MEMORY.md               # Long-term memory (main session only)
│   └── memory/                 # Daily memory files
│       └── YYYY-MM-DD.md
├── skills/                      # Installed skills
│   └── <skill-name>/
│       └── SKILL.md
└── hooks/                       # Custom hooks
    └── <hook-name>/
        ├── HOOK.md
        └── handler.ts

Quick Setup

1. Install the Skill

clawdhub install self-improving-agent

Or copy manually:

cp -r self-improving-agent ~/.openclaw/skills/

2. Install the Hook (Optional)

Copy the hook to OpenClaw's hooks directory:

cp -r hooks/openclaw ~/.openclaw/hooks/self-improvement

Enable the hook:

openclaw hooks enable self-improvement

3. Create Learning Files

Create the .learnings/ directory in your workspace:

mkdir -p ~/.openclaw/workspace/.learnings

Or in the skill directory:

mkdir -p ~/.openclaw/skills/self-improving-agent/.learnings

Injected Prompt Files

AGENTS.md

Purpose: Multi-agent workflows and delegation patterns.

# Agent Coordination

## Delegation Rules
- Use explore agent for open-ended codebase questions
- Spawn sub-agents for long-running tasks
- Use sessions_send for cross-session communication

## Session Handoff
When delegating to another session:
1. Provide full context in the handoff message
2. Include relevant file paths
3. Specify expected output format

SOUL.md

Purpose: Behavioral guidelines and communication style.

# Behavioral Guidelines

## Communication Style
- Be direct and concise
- Avoid unnecessary caveats and disclaimers
- Use technical language appropriate to context

## Error Handling
- Admit mistakes promptly
- Provide corrected information immediately
- Log significant errors to learnings

TOOLS.md

Purpose: Tool capabilities, integration gotchas, local configuration.

# Tool Knowledge

## Self-Improvement Skill
Log learnings to `.learnings/` for continuous improvement.

## Local Tools
- Document tool-specific gotchas here
- Note authentication requirements
- Track integration quirks

Learning Workflow

Capturing Learnings

In-session: Log to .learnings/ as usual
Cross-session: Promote to workspace files

Promotion Decision Tree

Is the learning project-specific?
├── Yes → Keep in .learnings/
└── No → Is it behavioral/style-related?
    ├── Yes → Promote to SOUL.md
    └── No → Is it tool-related?
        ├── Yes → Promote to TOOLS.md
        └── No → Promote to AGENTS.md (workflow)

Promotion Format Examples

From learning:

Git push to GitHub fails without auth configured - triggers desktop prompt

To TOOLS.md:

## Git
- Don't push without confirming auth is configured
- Use `gh auth status` to check GitHub CLI auth

Inter-Agent Communication

OpenClaw provides tools for cross-session communication:

sessions_list

View active and recent sessions:

sessions_list(activeMinutes=30, messageLimit=3)

sessions_history

Read transcript from another session:

sessions_history(sessionKey="session-id", limit=50)

sessions_send

Send message to another session:

sessions_send(sessionKey="session-id", message="Learning: API requires X-Custom-Header")

sessions_spawn

Spawn a background sub-agent:

sessions_spawn(task="Research X and report back", label="research")

Available Hook Events

Detection Triggers

Standard Triggers

User corrections ("No, that's wrong...")
Command failures (non-zero exit codes)
API errors
Knowledge gaps

OpenClaw-Specific Triggers

Verification

Check hook is registered:

openclaw hooks list

Check skill is loaded:

openclaw status

Troubleshooting

Hook not firing

Ensure hooks enabled in config
Restart gateway after config changes
Check gateway logs for errors

Learnings not persisting

Verify .learnings/ directory exists
Check file permissions
Ensure workspace path is configured correctly

Skill not loading

Check skill is in skills directory
Verify SKILL.md has correct frontmatter
Run openclaw status to see loaded skills

File v1.0.10:.learnings/ERRORS.md

Errors Log

Command failures, exceptions, and unexpected behaviors.

File v1.0.10:.learnings/FEATURE_REQUESTS.md

Feature Requests

Capabilities requested by user that don't currently exist.

File v1.0.10:.learnings/LEARNINGS.md

Learnings Log

Captured learnings, corrections, and discoveries. Review before major tasks.

File v1.0.10:assets/LEARNINGS.md

Learnings

Corrections, insights, and knowledge gaps captured during development.

Status Definitions

Skill Extraction Fields

When a learning is promoted to a skill, add these fields:

**Status**: promoted_to_skill
**Skill-Path**: skills/skill-name

Example:

## [LRN-20250115-001] best_practice

**Logged**: 2025-01-15T10:00:00Z
**Priority**: high
**Status**: promoted_to_skill
**Skill-Path**: skills/docker-m1-fixes
**Area**: infra

### Summary
Docker build fails on Apple Silicon due to platform mismatch
...

File v1.0.10:assets/SKILL-TEMPLATE.md

Skill Template

Template for creating skills extracted from learnings. Copy and customize.

SKILL.md Template

---
name: skill-name-here
description: "Concise description of when and why to use this skill. Include trigger conditions."
---

# Skill Name

Brief introduction explaining the problem this skill solves and its origin.

## Quick Reference

| Situation | Action |
|-----------|--------|
| [Trigger 1] | [Action 1] |
| [Trigger 2] | [Action 2] |

## Background

Why this knowledge matters. What problems it prevents. Context from the original learning.

## Solution

### Step-by-Step

1. First step with code or command
2. Second step
3. Verification step

### Code Example

\`\`\`language
// Example code demonstrating the solution
\`\`\`

## Common Variations

- **Variation A**: Description and how to handle
- **Variation B**: Description and how to handle

## Gotchas

- Warning or common mistake #1
- Warning or common mistake #2

## Related

- Link to related documentation
- Link to related skill

## Source

Extracted from learning entry.
- **Learning ID**: LRN-YYYYMMDD-XXX
- **Original Category**: correction | insight | knowledge_gap | best_practice
- **Extraction Date**: YYYY-MM-DD

Minimal Template

For simple skills that don't need all sections:

---
name: skill-name-here
description: "What this skill does and when to use it."
---

# Skill Name

[Problem statement in one sentence]

## Solution

[Direct solution with code/commands]

## Source

- Learning ID: LRN-YYYYMMDD-XXX

Template with Scripts

For skills that include executable helpers:

---
name: skill-name-here
description: "What this skill does and when to use it."
---

# Skill Name

[Introduction]

## Quick Reference

| Command | Purpose |
|---------|---------|
| `./scripts/helper.sh` | [What it does] |
| `./scripts/validate.sh` | [What it does] |

## Usage

### Automated (Recommended)

\`\`\`bash
./skills/skill-name/scripts/helper.sh [args]
\`\`\`

### Manual Steps

1. Step one
2. Step two

## Scripts

| Script | Description |
|--------|-------------|
| `scripts/helper.sh` | Main utility |
| `scripts/validate.sh` | Validation checker |

## Source

- Learning ID: LRN-YYYYMMDD-XXX

Naming Conventions

Skill name: lowercase, hyphens for spaces
- Good: docker-m1-fixes, api-timeout-patterns
- Bad: Docker_M1_Fixes, APITimeoutPatterns
Description: Start with action verb, mention trigger
- Good: "Handles Docker build failures on Apple Silicon. Use when builds fail with platform mismatch."
- Bad: "Docker stuff"
Files:
- SKILL.md - Required, main documentation
- scripts/ - Optional, executable code
- references/ - Optional, detailed docs
- assets/ - Optional, templates

Extraction Checklist

Before creating a skill from a learning:

[ ] Learning is verified (status: resolved)
[ ] Solution is broadly applicable (not one-off)
[ ] Content is complete (has all needed context)
[ ] Name follows conventions
[ ] Description is concise but informative
[ ] Quick Reference table is actionable
[ ] Code examples are tested
[ ] Source learning ID is recorded

After creating:

[ ] Update original learning with promoted_to_skill status
[ ] Add Skill-Path: skills/skill-name to learning metadata
[ ] Test skill by reading it in a fresh session

File v1.0.10:hooks/openclaw/HOOK.md

name: self-improvement description: "Injects self-improvement reminder during agent bootstrap" metadata: {"openclaw":{"emoji":"🧠","events":["agent:bootstrap"]}}

Self-Improvement Hook

Injects a reminder to evaluate learnings during agent bootstrap.

What It Does

Fires on agent:bootstrap (before workspace files are injected)
Adds a reminder block to check .learnings/ for relevant entries
Prompts the agent to log corrections, errors, and discoveries

Configuration

No configuration needed. Enable with:

openclaw hooks enable self-improvement

API & Reliability

Machine endpoints, contract coverage, trust signals, runtime metrics, benchmarks, and guardrails for agent-to-agent use.

MissingCLAWHUB

Machine interfaces

Contract & API

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

No protocol metadata captured.

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/snapshot"

curl -s "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/contract"

curl -s "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/trust"

Operational fit

Reliability & Benchmarks

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Machine Appendix

Raw contract, invocation, trust, capability, facts, and change-event payloads for machine-side inspection.

MissingCLAWHUB

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": []
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "CLAWHUB",
      "generatedAt": "2026-04-17T02:04:22.433Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [],
  "flattenedTokens": ""
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Clawhub",
    "href": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceUrl": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "77.6K downloads",
    "href": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceUrl": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-04-15T00:45:39.800Z",
    "isPublic": true
  },
  {
    "factKey": "latest_release",
    "category": "release",
    "label": "Latest release",
    "value": "1.0.11",
    "href": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceUrl": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceType": "release",
    "confidence": "medium",
    "observedAt": "2026-02-22T16:28:33.337Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/clawhub-pskoett-self-improving-agent/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "release",
    "title": "Release 1.0.11",
    "description": "No functional or content changes; OpenClaw-specific environment metadata was removed. - Removed the OpenClaw `requires.env` metadata block from the skill definition. - All usage guidance, logging formats, and workflow instructions remain unchanged. - No new features or bug fixes included in this version. - This update does not require any action from users. - Ensures cleaner skill metadata and wider compatibility.",
    "href": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceUrl": "https://clawhub.ai/pskoett/self-improving-agent",
    "sourceType": "release",
    "confidence": "medium",
    "observedAt": "2026-02-22T16:28:33.337Z",
    "isPublic": true
  }
]