Self-Improvement Skill for Coding Agents for Openclaw

An autonomous learning skill that allows AI coding agents to log, analyze, and resolve their errors while promoting key insights into long-term project memory.

subaru0573
v1.0.0
Jun 30, 2026
0
289
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install super-self-improving-agent

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install super-self-improving-agent using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Self-Improvement Skill for Coding Agents?

The Self-Improvement skill enables AI coding agents to continuously adapt, learn, and refine their processes by systematically logging errors, user corrections, and knowledge gaps into structured Markdown files. By keeping a local record of execution failures and developer feedback, coding agents can automatically correct recurring issues, adapt to outdated context, and refine their overall approach over time. This makes it a foundational capability among Openclaw Skills.

Designed primarily for OpenClaw but compatible with other developer environments like Claude Code and GitHub Copilot, this skill ensures your AI developer agent grows smarter with every terminal execution. It moves AI interaction from temporary chat sessions to a persistent cycle of software engineering refinement.

Self-Improvement Skill for Coding Agents Use Cases

  • Automatically logging terminal command failures and software execution exceptions to identify systemic build issues.
  • Tracking specific developer corrections and instructions to prevent the agent from repeating the same logical errors.
  • Documenting missing capabilities and developer feature requests directly during active pairing sessions.
  • Recording tool integration limitations or API version differences that cause unexpected execution failures.
  • Spotting recurring patterns and promoting generalized technical solutions to project-wide guidelines.

How Self-Improvement Skill for Coding Agents Works

  1. Detect Triggers: The agent detects execution failures, user corrections, knowledge gaps, or explicit user requests for a feature.
  2. Log to Markdown: The agent appends a highly detailed entry to the appropriate file in the .learnings/ directory using standardized formats and generating a unique ID like LRN-YYYYMMDD-XXX.
  3. De-duplicate and Recur: When logging, the agent searches past logs using grep to find similar events. It updates recurrence metrics and links related errors to prioritize persistent issues.
  4. Promote Learnings: Broadly applicable workflows, tool configurations, or behavioral changes are distilled and promoted to global project memory files like CLAUDE.md, AGENTS.md, or SOUL.md.
  5. Extract Skills: If a learning matches criteria for being recurring, verified, and non-obvious, a script helper extracts it into a fresh, reusable skill package.

Self-Improvement Skill for Coding Agents Setup

Workspace Initialization

Ensure the .learnings/ directory and baseline templates exist in your workspace root:

mkdir -p .learnings
[ -f .learnings/LEARNINGS.md ] || printf "# Learnings\n\nCorrections, insights, and knowledge gaps captured during development.\n\n**Categories**: correction | insight | knowledge_gap | best_practice\n\n---\n" > .learnings/LEARNINGS.md
[ -f .learnings/ERRORS.md ] || printf "# Errors\n\nCommand failures and integration errors.\n\n---\n" > .learnings/ERRORS.md
[ -f .learnings/FEATURE_REQUESTS.md ] || printf "# Feature Requests\n\nCapabilities requested by the user.\n\n---\n" > .learnings/FEATURE_REQUESTS.md

OpenClaw Installation

Configure this skill directly within your Openclaw Skills ecosystem:

Via ClawdHub:

clawdhub install self-improving-agent

Manual Method:

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

Enabling Reminder Hooks (Optional)

To enable automatic prompts and post-tool execution checks, register the opt-in hook scripts in your project configuration (e.g., .claude/settings.json):

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

Self-Improvement Skill for Coding Agents Data Schema & Taxonomy

The self-improvement skill organizes tracking logs into structured, machine-readable Markdown lists located in the .learnings/ workspace directory.

Directory and Files

File Path Description Key Metadata Tracked
.learnings/LEARNINGS.md General learnings, insights, and best practices Source, Pattern-Key, Recurrence-Count
.learnings/ERRORS.md Terminal errors, build failures, and exceptions Error Output, Reproducible, Related Files
.learnings/FEATURE_REQUESTS.md User-requested features and ideas Requested Capability, Complexity Estimate

Taxonomy Fields

All entries must support standard identification and filtering fields:

  • ID Schema: TYPE-YYYYMMDD-XXX where TYPE is LRN (learning), ERR (error), or FEAT (feature).
  • Priority: Must use critical, high, medium, or low.
  • Status: Transitioned through pending, in_progress, resolved, wont_fix, and promoted.
  • Area: Tracked by codebase layers: frontend, backend, infra, tests, docs, and config.

Self-Improvement Skill for Coding Agents Advanced Features

  • Inter-Session Syncing: Leverages custom OpenClaw commands to list, view, and send logged learnings across active sessions, ensuring multi-agent synchronization.
  • Pattern-Key Deduplication: Integrates tracking loops from other system utilities to detect recurring problems across different tasks over a 30-day window.
  • System Prompt Feedback Loop: Automatically promotes recurring patterns that have a recurrence count of 3 or more into core memory files.
  • Skill Extraction Engine: Includes a CLI tool script (extract-skill.sh) to package resolved, non-obvious workspace learnings into custom, reusable Openclaw Skills.
  • Cross-Agent Settings: Integrates manual and automatic prompt configurations for Claude Code, Codex, and GitHub Copilot instruction blocks.

SKILL.md


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