Applies Test-Driven Development principles to documentation and skills to ensure reliable AI agent compliance.
This skill transforms how AI instructions are authored by treating documentation as production code through a rigorous Red-Green-Refactor cycle. By pressuring subagents to fail before writing a skill and verifying their compliance afterward, it ensures that every instruction is necessary, bulletproof, and token-efficient. It provides a standardized framework for creating, testing, and optimizing Claude Code skills for maximum discovery and impact across development workflows.
主要功能
010 GitHub stars
02Red-Green-Refactor cycle for process documentation
03Subagent pressure testing to identify instruction loopholes
04Claude Search Optimization (CSO) for better skill discovery
05Token-efficient documentation patterns and structure
06Standardized directory and SKILL.md formatting
使用场景
01Optimizing context usage by refactoring verbose skills into efficient references
02Verifying that complex instructions actually change AI behavior before deployment
03Developing reusable technical patterns for cross-project AI guidance
