writing-skills

To create AI agent skills using test-driven development, apply the RED-GREEN-REFACTOR cycle to skill documentation, ensuring your AI skills are robust and effective through structured writing and verification before deployment.

Optimizing skill documentation for AI discoverability involves structuring your SKILL.md frontmatter with targeted keywords and clear content, improving how effectively AI agents can find and interact with your skills.

Preventing AI rationalization requires explicit loophole closure techniques in your skill documentation, ensuring strict compliance with skill rules and stopping the AI from bypassing intended constraints.

SKILL.md files should follow specific formatting guidelines for optimal AI agent interaction, incorporating structured frontmatter, clear task descriptions, and keyword optimization to maximize AI comprehension and execution accuracy.

Prior testing experience is beneficial but not strictly required, as the framework guides you through applying TDD principles like RED-GREEN-REFACTOR directly to skill documentation, making the methodology accessible for skill creation.

Your AI agent may fail to discover or correctly use custom skills due to poorly structured SKILL.md documentation lacking keyword optimization, explicit instructions, and proper loophole closure, which this test-driven framework resolves.