What problem does it solve? Writing documentation-style skills for AI agents often produces instructions that agents ignore or rationalize away under pressure. This Skill applies test-driven development to skill authoring so you can 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). - Skill Discovery Optimization (SDO): Guidance for writing description fields, keyword coverage, naming conventions, and token-efficient content so agents find and load the right skill. - Testing methodology: Detailed reference on pressure scenario design, rationalization tables, red flags lists, and meta-testing techniques. - Use Case: You want to create a new discipline-enforcing skill (e.g., requiring verification before completion). You run baseline scenarios without the skill, document the agent's exact rationalizations, write the skill to counter them, and re-test until the agent complies under maximum pressure. ## 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 based on the observed failures.