A comprehensive framework for AI coding agents to capture errors, user corrections, and best practices to enable continuous autonomous improvement.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-self-improving-agent
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-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?
The Self-Improvement skill is a systematic approach for AI coding agents to learn from their own operational history. By logging failures, knowledge gaps, and user feedback into structured markdown files, agents can refine their behavior and accuracy over time. It transforms one-off corrections into permanent project memory, ensuring that the same mistake never happens twice and that the agent's performance improves with every interaction.
This skill is a core component within the ecosystem of Openclaw Skills, allowing developers to build more resilient and context-aware autonomous workflows. It provides a standardized way to manage long-term memory and cross-session learning, making it essential for complex, long-running development projects where maintaining technical context is critical.
Self-Improvement Skill Use Cases
- Capturing unexpected command or API failures for later debugging and automated resolution.
- Logging direct user corrections to update the agent's internal knowledge base and behavioral style.
- Identifying recurring coding patterns to simplify and harden the codebase through pattern tracking.
- Documenting missing capabilities as feature requests to guide the roadmap of autonomous tools.
- Promoting localized learnings into global project conventions to maintain consistency across multi-agent teams.
How Self-Improvement Skill Works
- The agent detects a trigger such as a failed bash command, a specific user correction, or a discovered best practice.
- Relevant technical details and context are appended to structured log files like ERRORS.md or LEARNINGS.md within a .learnings directory.
- Periodic reviews or automated hooks prompt the agent to evaluate, prioritize, and resolve pending entries.
- High-value, broadly applicable learnings are promoted to permanent project files such as CLAUDE.md, SOUL.md, or AGENTS.md.
- Complex or frequently recurring solutions are extracted into standalone, reusable modules to expand the library of available Openclaw Skills.
Self-Improvement Skill Setup
To install the skill using ClawdHub as part of your Openclaw Skills collection, run:
clawdhub install self-improving-agent
Alternatively, clone it manually into your local skills directory:
git clone https://github.com/peterskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent
Initialize the workspace structure to store your logs:
mkdir -p ~/.openclaw/workspace/.learnings
Optionally, enable the automated hook for session-start reminders:
openclaw hooks enable self-improvement
Self-Improvement Skill Data Schema & Taxonomy
The skill organizes data within a dedicated .learnings directory, categorizing information into three primary markdown files:
| File | Purpose | Key Metadata |
|---|---|---|
LEARNINGS.md |
Records corrections and best practices | Priority, Area, Pattern-Key, Status |
ERRORS.md |
Logs command and tool failures | Exit Codes, Stack Traces, Context |
FEATURE_REQUESTS.md |
Tracks missing agent capabilities | Complexity, Frequency, User Context |
Each entry follows a strict ID format (TYPE-YYYYMMDD-XXX) to ensure easy grep-ability and cross-referencing between related issues.
Self-Improvement Skill Advanced Features
- Multi-agent support providing consistent learning protocols across OpenClaw, Claude Code, and GitHub Copilot.
- Automated skill extraction scripts that turn proven fixes into standalone Openclaw Skills.
- Inter-session communication tools (sessions_send, sessions_history) to share context between disparate agent tasks.
- Recurring pattern detection that triggers architectural hardening once a specific issue is seen multiple times.
- Integration with project-level memory files (CLAUDE.md/AGENTS.md) to ensure systemic improvements persist across the entire development lifecycle.
SKILL.md
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