What problem does it solve? Writing documentation for AI agents often fails because untested instructions get rationalized away under pressure. This Skill applies test-driven development to skill authoring so you can verify agents actually comply before deployment. ## 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 effective YAML frontmatter, keyword-rich descriptions, and token-efficient content so agents find and load the skill. - Testing Methodology: Detailed reference for building multi-pressure scenarios, rationalization tables, and red-flag lists to bulletproof discipline-enforcing skills. - Use Case: When creating a new skill that enforces a rule like TDD, run a scenario where an agent has sunk cost and time pressure, watch it violate the rule, then write the skill to counter those exact rationalizations. ## Quick Start Ask the agent to help you create a new skill by first running a baseline pressure scenario with a subagent before writing any documentation.