writing-skills

TDD for AI skills adapts the RED-GREEN-REFACTOR cycle to skill authoring by first demonstrating agent failures, writing the skill to enforce compliance, and iteratively refactoring. This structured testing ensures skills remain robust and discoverable.

Agent rationalization occurs when AI bypasses skill rules under realistic agentic pressures like time, sunk cost, or authority. It is countered by applying structured pressure testing frameworks during skill development to enforce compliance.

Pressure testing an AI skill involves evaluating it under realistic agentic pressures such as time constraints, sunk costs, and authority. This process uses structured testing frameworks to identify and counter common excuses agents use to bypass rules.

Yes, this TDD methodology provides specific testing strategies for various skill types, including discipline-enforcing, technique, pattern, and reference skills, ensuring comprehensive coverage across different agent behaviors and use cases.

AI agents bypass documentation and review rules due to rationalization under agentic pressures. Applying a test-driven development approach with rationalization countermeasures prevents these common excuses and enforces strict rule compliance.