self-improvement-codex-copilot

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

Updated Feb 24, 2026

Search Tags:#self-improvement #error logging #agent training #learning system #feedback analysis

Ready to run

npx skills add https://github.com/dothackerman/keyline-planner --skill self-improvement-codex-copilot

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-improvement-codex-copilot
Source: https://github.com/dothackerman/keyline-planner/tree/main
Command: npx skills add https://github.com/dothackerman/keyline-planner --skill self-improvement-codex-copilot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps AI agents capture errors, corrections, and best practices, enabling continuous improvement of behavior and reliability.

Core Features & Use Cases

Error logging: Records unexpected failures during agent commands in dedicated files.
Correction tracking: Stores user corrections and improved approaches for future reference.
Pattern recognition: Analyzes recurring issues to suggest code or process enhancements.

Quick Start

Log a new failed command attempt by creating an entry in '.learnings/ERRORS.md' with relevant details.

Frequently Asked Questions about self-improvement-codex-copilot

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

FAQPage Schema

How do I log agent errors and corrections for continuous learning in my repository?▼

To log agent errors and corrections for continuous learning, create structured entries in `.learnings/ERRORS.md` to record unexpected failures and store improved approaches for future reference.

What is an in-repo learning system for AI agents?▼

An in-repo learning system for AI agents captures errors, corrections, and best practices within structured markdown templates to support iterative refinement and improve behavior reliability.

How do I analyze recurring agent errors to suggest code enhancements?▼

To analyze recurring agent errors and suggest code enhancements, the system reviews logged failures and user corrections in dedicated files to recognize patterns and propose process improvements.

Do I need PyYAML to set up the agent feedback analysis environment?▼

Yes, PyYAML is required to set up the environment for agent feedback analysis, as the skill depends on it to process structured markdown templates and configuration files.

Can I use markdown templates for tracking AI behavior corrections?▼

Yes, you can use structured markdown templates for tracking AI behavior corrections by creating dedicated files to store user corrections and improved approaches during agent workflows.