creating-skills

To create AI agent skills using Test-Driven Development, follow the RED-GREEN-REFACTOR cycle by writing failing pressure tests first, then authoring the skill documentation to pass them, and finally refining the prompts. This methodology ensures your skills are effective and robust against rationalization.

Skill pressure testing evaluates AI behavior under combined constraints like time limits, sunk costs, and authority figures to close loopholes. By creating these scenarios, you bulletproof skills against rationalization and ensure agents resist bypassing your defined instructions.

Naming AI skills for optimal agent discovery requires following specific conventions outlined in skill authoring best practices. Proper naming ensures your created skills are easily discoverable by AI agents, facilitating correct adoption and iterative development within your project.

Yes, you can test created skills with subagents before final deployment by placing them in simulated pressure scenarios. This iterative development process allows you to evaluate agent behavior, address rationalizations, and refine the skill documentation against combined constraints.

The best way to bulletproof AI prompts against rationalization is to use a Test-Driven Development methodology that creates specific pressure scenarios. This approach closes loopholes by actively testing skills against sunk costs, authority figures, and time constraints before finalizing documentation.

AI skills bypass instructions under authority pressure because they are not adequately bulletproofed against rationalizations. To fix this, you must implement pressure scenario testing using TDD, explicitly targeting combined constraints like authority and sunk costs to close logical loopholes.