learning-quality

What problem does it solve?

This Skill provides a structured process to identify, generalize, and capture valuable learnings during AI agent runs, preventing repeated mistakes and building organizational knowledge.

Core Features & Use Cases

• Decision Tree: Guides users on whether a learning is worth capturing based on criteria like non-obviousness, generalizability, and scope.
• Scope Classification: Differentiates between project-specific learnings (CLAUDE.md) and globally applicable patterns (org-patterns.toon).
• Pattern Generalization: Provides examples and a formula for transforming specific instances into reusable principles.
• Conflict Resolution: Outlines a process for checking and resolving contradictions with existing learned patterns.
• Capture Workflow: Details the steps for writing and saving learnings in the correct format and location.
• Use Case: An AI agent repeatedly makes a specific coding error due to a misunderstanding of a framework's enum usage. This skill ensures that misunderstanding is captured as a generalizable pattern, preventing future agents from making the same mistake.

Quick Start

Use the learning-quality skill to capture a new pattern about handling optional dictionaries in FastAPI.