What problem does it solve? Writing documentation-style skills for AI agents often produces untested instructions that agents ignore or rationalize away under pressure. This Skill applies test-driven development to skill authoring so you verify agents actually comply before deploying. ## Core Features & Use Cases - TDD-based skill authoring: Run baseline pressure scenarios with subagents (RED), write the skill addressing observed failures (GREEN), then close rationalization loopholes (REFACTOR). - Discovery optimization: Guidance for writing descriptions, keywords, and naming so future agents find the skill via Claude Search Optimization. - Testing methodology: Includes pressure scenario design, rationalization tables, red flag lists, and meta-testing techniques for bulletproofing discipline-enforcing skills. - Use Case: You want to create a new skill enforcing code review before merging. You run a scenario with time and sunk-cost pressure without the skill, watch the agent skip review, then write the skill countering those exact rationalizations and re-test until compliant. ## Quick Start Ask the agent to help you create a new skill by first running a baseline pressure scenario with a subagent, then drafting the SKILL.md and verifying compliance.