self-improvement

Name: self-improvement
Availability: InStock
Author: dsactivi-2

Log learnings, errors, and feature requests in structured markdown files.

Updated Feb 16, 2026

Search Tags:#documentation #learning #error logging #continuous improvement #skill extraction #ai feedback

Ready to run

npx skills add https://github.com/dsactivi-2/Mujo-Team --skill self-improvement-dsactivi-2

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-improvement
Source: https://github.com/dsactivi-2/Mujo-Team/tree/main/skills/self-improving-agent
Command: npx skills add https://github.com/dsactivi-2/Mujo-Team --skill self-improvement-dsactivi-2

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill captures learnings, errors, and corrections to enable continuous improvement of AI agents and project documentation, preventing recurring mistakes and accelerating development.

Core Features & Use Cases

Error Logging: Records command failures and unexpected behaviors.
Learning Capture: Logs user corrections, knowledge gaps, and discovered best practices.
Feature Request Tracking: Documents missing capabilities requested by users.
Promotion: Facilitates promoting learnings to project memory (e.g., CLAUDE.md, AGENTS.md) or extracting them as new skills.
Use Case: When an agent makes a mistake, like misinterpreting a command, this skill logs the error and the correct approach. Over time, these logs help refine agent behavior and update documentation, making the AI more reliable.

Quick Start

Log a command failure by appending the details to the ERRORS.md file within the .learnings directory.

Frequently Asked Questions about self-improvement

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

FAQPage Schema

How do I log AI agent errors and corrections for continuous improvement?▼

Log AI agent errors by appending command failures and unexpected behaviors to structured markdown files like ERRORS.md within a .learnings directory. This captures mistakes and correct approaches to prevent recurring issues and refine agent behavior over time.

What is structured learning capture in AI coding environments?▼

Structured learning capture records user corrections, knowledge gaps, and discovered best practices in markdown files. It enables continuous improvement by systematically documenting insights across various AI coding environments to accelerate development.

How do I promote captured learnings to project memory documentation?▼

Promote captured learnings to project memory by integrating valuable insights into project documentation files like CLAUDE.md or AGENTS.md. This updates documentation and makes AI agents more reliable by retaining operational best practices.

Can I track feature requests and knowledge gaps using markdown logs?▼

Track feature requests and knowledge gaps by documenting missing capabilities requested by users in structured markdown logs. This records operational failures and corrections, supporting continuous improvement across AI coding environments.

What's the best way to extract reusable skills from logged AI mistakes?▼

Extract reusable skills from logged AI mistakes by reviewing structured markdown logs of errors and best practices, then promoting those valuable insights into new skills. This prevents recurring mistakes and refines agent behavior.

Does continuous improvement logging work without external dependencies?▼

Continuous improvement logging works without external dependencies by using structured markdown files to capture errors, learnings, and feature requests. It relies on appending details to files like ERRORS.md within a .learnings directory.