A sophisticated memory and correction framework that enables AI agents to learn from mistakes and user feedback to prevent repeating errors.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install self-improving-agent-cn
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 self-improving-agent-cn 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-Improving Agent Skill?
The Self-Improving Agent is a technical framework designed to enhance Openclaw Skills by solving the common problem of AI agents forgetting user preferences or repeating failed commands. It acts as a long-term memory layer that systematically captures command errors, user-provided corrections, and technical best practices. By turning every session into a learning opportunity, it ensures that your agent evolves and becomes more specialized to your specific development environment over time.
This skill is essential for developers who want a truly persistent AI assistant. Instead of correcting the same coding style or fixing the same permission error in every new chat, this system records those events into structured logs. It then proactively checks this memory before executing future tasks, ensuring the AI applies previously learned lessons across different projects and sessions.
Self-Improving Agent Skill Use Cases
- Preventing the AI from using the same failed command syntax multiple times in a row.
- Storing project-specific styling preferences, such as mandatory single quotes or specific indentation rules.
- Capturing updated API usage when the AI's internal training data for a library is outdated.
- Building a global repository of security best practices that the agent must check before installing new packages.
- Synchronizing learned behaviors between different development projects via global memory files.
How Self-Improving Agent Skill Works
- The system monitors command line activity and automatically triggers error logging if an exit code is non-zero.
- Natural language triggers like "you are wrong" or "it should be" prompt the agent to categorize and store a user correction.
- Discovered optimizations are recorded as best practices to be prioritized in future task planning.
- Before any command execution, the agent runs a memory check script to see if a similar task has failed or been corrected previously.
- The skill synchronizes these insights between a global configuration directory and project-level learning folders to maintain consistency.
Self-Improving Agent Skill Setup
To begin using this with your Openclaw Skills, you need to initialize the local memory structure:
# Create the dedicated memory directory
mkdir -p ~/.openclaw/memory/self-improving
# Access the skill definition to ensure the environment is ready
cat ~/.openclaw/skills/self-improving-agent/SKILL.md
Self-Improving Agent Skill Data Schema & Taxonomy
The skill organizes its long-term memory within ~/.openclaw/memory/self-improving/ using the following structure:
| File | Data Type | Description |
|---|---|---|
errors.jsonl |
JSON Lines | Logs failed commands, error messages, and the successful fix used. |
corrections.jsonl |
JSON Lines | Stores user feedback regarding style, logic, or preferences. |
best_practices.jsonl |
JSON Lines | Records optimized methods and security audit requirements. |
knowledge_gaps.jsonl |
JSON Lines | Identifies where the AI's knowledge is deprecated or missing. |
index.json |
JSON | A high-speed index for quick memory retrieval during execution. |
Self-Improving Agent Skill Advanced Features
- Automated NLP Triggering: Detects corrections naturally within conversation without needing specific commands.
- Pre-execution Guardrails: Intercepts commands to suggest fixes based on historical failure data.
- Hybrid Memory Sync: Seamlessly merges global developer preferences with project-specific requirements.
- Knowledge Gap Tracking: Specifically identifies when external tools or APIs have changed compared to the AI's base training.
- Multi-Layer Updates: Simultaneously updates AGENTS.md for style and MEMORY.md for technical wisdom.
SKILL.md
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