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To create robust AI skills that reliably trigger and resist pressure, use a structured test-driven development methodology that guides you through planning, TDD content creation, and evaluation stages to prevent common failure modes.

Test-driven development for AI agents confronts failure by identifying how Claude fails without a skill in the RED phase, then writing minimal content to pass those tests in the GREEN phase, preventing poor triggering or ignored content.

You pressure test AI agent skills by designing and running specific pressure scenarios using subagent testing methodologies, evaluating trigger accuracy and pressure resistance to ensure the skill remains effective under stress.

The best way to evaluate prompt engineering content is through a structured eval and benchmark stage that uses rationalization tables and red flags checklists to assess trigger accuracy, behavioral change, and overall skill robustness.

You do not need extensive prior testing experience to formalize existing behavior into a skill. The structured workflow guides you through planning, TDD content creation, and evaluation stages step by step to ensure robustness.

AI skills get ignored or fail to trigger due to common failure modes like poor triggering or content that gets ignored. Using TDD methodologies and red flags checklists ensures skill robustness and accurate trigger design.