self-improving-agent

What This Skill Does

The self-improving-agent is a sophisticated, instinct-based continuous learning system designed for the OpenClaw AI agent. It allows the agent to transform individual Claude Code sessions into a persistent, evolving body of knowledge. By capturing atomic "instincts"—small, granular behavioral patterns—the agent can remember user preferences, coding standards, and debugging strategies across different project contexts. This skill supports two distinct modes: the modern, confidence-weighted v2 (Instinct-Based) mode and the legacy v1 (Markdown-Based) mode. V2 is the recommended approach for its project-scoped isolation and ability to evolve behaviors from tentative suggestions to core, automated command-line instructions.

Installation

To integrate this intelligence layer into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/huamu668/self-improving-agent-ecc Ensure you have the necessary permissions enabled for file-write access, as the agent requires this to store instinct YAML files and update its knowledge base.

Use Cases

• Command Failures: If a specific terminal command or script fails, the agent records the context to prevent future execution errors.
• User Corrections: When you correct the agent's output, it captures the correction as an instinct to refine future responses.
• Pattern Discovery: The agent monitors workflow sequences, automatically flagging recurring patterns for future automation.
• Project Separation: Easily manage distinct coding conventions by scoping instincts to specific project directories, ensuring React patterns don't bleed into your Python backend scripts.

Example Prompts

1. "I corrected your approach to the API wrapper implementation; please save that as an instinct so you remember it for the rest of this project."
2. "We've been using this specific logging format for the last five files. Can you observe this pattern and create a new instinct with high confidence?"
3. "Run /instinct-status to show me all the learned behaviors currently affecting this repository and let me know if any need to be promoted to global scope."

Tips & Limitations

• Confidence Management: Use lower confidence scores (0.3) for experimental behaviors to avoid over-automation until you are certain of the pattern's reliability.
• Regular Pruning: Periodically use the /evolve command to consolidate atomic instincts into more complex, manageable skills.
• Scope Discipline: Keep project-specific logic scoped locally to avoid cluttering your global command namespace with repo-specific quirks.
• Limitations: While highly capable, the agent relies on your feedback for high-accuracy reinforcement; be proactive in correcting it early to train it effectively.