error-record

Log AI-generated errors and resolutions into dated Markdown files.

Updated Feb 17, 2026

Search Tags:#code quality #debugging #learning #knowledge base #error logging #ai mistakes

Ready to run

npx skills add https://github.com/Gatsbyhateyou/gatsby-website --skill error-record

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: error-record
Source: https://github.com/Gatsbyhateyou/gatsby-website/tree/main/.cursor/skills/error-record
Command: npx skills add https://github.com/Gatsbyhateyou/gatsby-website --skill error-record

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you and the AI learn from mistakes by systematically recording and summarizing errors made during AI-assisted tasks, preventing recurrence.

Core Features & Use Cases

Error Summarization: Captures the context, cause, and resolution of AI errors.
Chronological Archiving: Organizes error records by date in a dedicated project folder.
Preventative Learning: Creates a knowledge base to avoid repeating past mistakes.
Use Case: After an AI incorrectly formats a code snippet, use this Skill to log the error, its cause (e.g., misunderstanding of specific syntax), the fix, and a lesson learned for future code generation.

Quick Start

Record the recent error and its correction in the error-record folder.

Frequently Asked Questions about error-record

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

FAQPage Schema

How do I log AI-generated code errors for future reference?▼

To log AI-generated code errors, this Skill records the scenario, manifestation, cause, correction, and lessons learned into a dated Markdown file. It archives these records chronologically within a dedicated 'error-record' directory in your project.

What is the best way to document AI mistakes and prevent recurrence?▼

The best way to document AI mistakes is by systematically summarizing the context, root cause, and resolution of each error. This builds a preventative knowledge base that helps the AI learn from past errors and avoid repeating them.

How does archiving AI debugging errors into a knowledge base work?▼

Archiving AI debugging errors works by capturing the error context and its fix, then saving them as structured Markdown files. This creates a historical knowledge base within your project to track continuous improvement and corrective actions.

Can I use this error logging technique for any AI-assisted coding project?▼

Yes, you can use this error logging technique for any AI-assisted coding project. It requires no specific dependencies and simply creates an 'error-record' directory to store dated Markdown files for your code quality tracking.

What specific fields are needed to record AI mistakes systematically?▼

To record AI mistakes systematically, you need specific fields for the scenario, manifestation, cause, correction, and lessons learned. This structure ensures all error records capture the full context and preventative measures.