A self-optimizing feedback loop that turns every AI interaction into a learning opportunity by tracking mistakes and saving successful patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-improve
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 auto-improve 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 Auto-Improve Skill?
The Auto-Improve Skill is a sophisticated self-correction framework designed to make your AI coding agent smarter with every task it completes. By implementing a continuous improvement loop, it ensures that your Openclaw Skills don't just execute commands but actually evolve by analyzing failures and documenting successful architectural decisions. It serves as a persistent long-term memory layer that reduces technical debt and prevents the recurrence of known bugs across different development sessions.
At its core, the skill acts as an intelligent layer between task request and execution. It proactively checks historical data to warn the developer of potential pitfalls before they happen. This transformational approach shifts the AI from a stateless executor to a project-aware partner that gains institutional knowledge of your specific codebase and standards over time.
Auto-Improve Skill Use Cases
- Automatically documenting lessons learned after complex debugging sessions.
- Preventing the repetition of specific syntax or architectural errors across different Openclaw Skills sessions.
- Building a local repository of reusable coding patterns tailored to project-specific requirements.
- Ensuring new sessions start with full context by loading historical memory and project-specific antigravity files.
- Monitoring development metrics such as first-time-right ratios and pattern reuse frequency.
How Auto-Improve Skill Works
- At the start of a session, the skill automatically loads the local project context from .antigravity.md and recalls global memory for Openclaw Skills.
- Before any code modification, it runs a pre-action check against known mistakes and saved patterns to ensure compliance with existing project standards.
- During and after execution, the skill monitors the outcome through verification loops including tests and linting processes.
- If an action fails, it triggers a learning protocol that identifies the root cause and logs an actionable lesson to prevent future repeats.
- If an action succeeds, it extracts the reusable pattern and saves it to the knowledge base for future task acceleration.
- On session close, it summarizes all learnings and updates the project-specific documentation.
Auto-Improve Skill Setup
To integrate this capability into your environment, ensure your project root is properly initialized. Use the following commands to manage the learning state:
# Create the local context file if it doesn't exist
touch .antigravity.md
# Force a learning cycle from the most recent action
/improve
# List all learned mistakes and their solutions
/mistakes
# Display stored success patterns
/patterns
Auto-Improve Skill Data Schema & Taxonomy
The skill organizes its intelligence using a combination of local files and tagged global memory to ensure Openclaw Skills stay relevant to your project.
| Component | Type | Description |
|---|---|---|
| .antigravity.md | File | Local markdown storage for project-specific context and error history. |
| Pattern Library | Data | Structured records containing task situation, approach taken, and successful outcomes. |
| Mistake Log | Data | Tagged entries containing error descriptions, root causes, and avoidance strategies. |
| Global Memory | Storage | Cross-session database for broader domain-specific knowledge and lessons. |
Auto-Improve Skill Advanced Features
- Deep integration with mistake-tracker and verification-loop Openclaw Skills for automated quality assurance.
- Cross-session learning that persists actionable insights across multiple terminal or IDE restarts.
- Real-time proactive warning system that alerts users to potential errors based on project history before code is written.
- Automated metric tracking to visualize the agent's improvement in accuracy and efficiency over time.
- Support for specific domain tagging, allowing the agent to distinguish between frontend, backend, or DevOps lessons.
SKILL.md
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