self-improvement

Log errors, learnings, and feature requests to markdown files.

26|9|Updated Mar 9, 2026
One-click install
npx skills add https://github.com/Zerone-Agent/agent-use-skills --skill self-improvement-zerone-agent
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: self-improvement
Source: https://github.com/Zerone-Agent/agent-use-skills/tree/main/awesome-skills/skills/self-improving-agent
Command: npx skills add https://github.com/Zerone-Agent/agent-use-skills --skill self-improvement-zerone-agent

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and assets (resource) and references (resource) components.

What problem does it solve?

This Skill provides a structured way to capture learnings, errors, and corrections, enabling continuous improvement of AI agent performance and knowledge.

Core Features & Use Cases

  • Error Logging: Records command failures, exceptions, and unexpected behaviors.
  • Learning Capture: Logs user corrections, knowledge gaps, and discovered best practices.
  • Feature Request Tracking: Documents user-requested capabilities that are missing.
  • Promotion: Facilitates promoting valuable learnings to permanent project memory (e.g., CLAUDE.md, AGENTS.md).
  • Skill Extraction: Supports extracting recurring learnings into new, reusable skills.
  • Use Case: When an agent makes a mistake, it logs the error and the correction. Over time, these logs are reviewed, and significant learnings are promoted to the agent's knowledge base, preventing future errors and improving its overall effectiveness.

Quick Start

Log a user correction by appending the details to .learnings/LEARNINGS.md using the specified format.

Frequently Asked Questions about self-improvement

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I log errors and user corrections for continuous improvement of AI agents?

To enable continuous improvement, log errors and user corrections by appending the details to structured markdown files in a `.learnings/` directory. This captures debugging failures and feedback for subsequent analysis.

What is the best way to capture knowledge gaps and feature requests during debugging?

The best way to capture knowledge gaps and feature requests is using structured logging in markdown files. This records missing capabilities and discovered best practices to prevent future errors and document needed corrections.

Can I promote recurring learnings to permanent agent memory like CLAUDE.md?

Yes, you can promote recurring learnings to permanent project memory files like `CLAUDE.md` or `AGENTS.md`. This elevates significant corrections from logs into the agent's core knowledge base, improving overall effectiveness.

How do I extract recurring agent errors into new reusable skills?

You can extract recurring errors by reviewing structured error logs and identifying patterns in command failures or exceptions. These recurring learnings are then extracted into new, reusable skills to prevent future mistakes.

Does this approach to error handling require specific dependencies or frameworks?

No specific dependencies are required. The approach relies solely on structured logging to markdown files for knowledge management, making it compatible with any software engineering environment that supports basic file operations.

What limitations exist when using markdown files for agent feedback loops?

The primary limitation is that markdown files require manual or subsequent analysis to identify knowledge gaps and promote best practices. The feedback loop depends on reviewing these logs rather than automated memory updates.