What problem does it solve? Writing documentation for AI agents often fails because authors guess what guidance agents need instead of observing actual failures, resulting in skills that agents ignore or rationalize away under pressure. ## 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. - Skill Discovery Optimization: Provides rules for writing effective name and description frontmatter, keyword coverage, and token-efficient structure so agents can find skills. - Rationalization Bulletproofing: Includes techniques like rationalization tables, red flags lists, and persuasion principles to close loopholes agents exploit under time or sunk-cost pressure. - Use Case: When creating a new skill that enforces a discipline like TDD, run pressure scenarios with subagents first, document their verbatim excuses, then write the skill to counter those specific rationalizations. ## Quick Start Ask the agent to create a new skill following the test-driven development process, starting with baseline pressure scenarios before writing any documentation.
