What problem does it solve? Writing effective skills for AI agents is hard because untested documentation often fails under real-world pressure, and authors cannot know if a skill actually changes agent behavior without observing baseline failures first. ## Core Features & Use Cases - TDD-Based Skill Authoring: Applies the RED-GREEN-REFACTOR cycle to documentation by running baseline pressure scenarios with subagents before writing any skill content. - Rationalization Bulletproofing: Provides techniques to close loopholes, build rationalization tables, and create red flag lists so discipline-enforcing skills resist agent workarounds. - Discovery Optimization: Covers Claude Search Optimization (CSO) rules for names, descriptions, keywords, and token efficiency so skills are found when needed. - Use Case: When creating a new skill that enforces a rule like test-driven development, run pressure scenarios without the skill, document the agent's exact excuses, then write minimal content addressing those specific failures and re-test until compliant. ## Quick Start Ask the agent to help you create a new skill by first running a baseline pressure scenario without it, then drafting the SKILL.md and verifying compliance.