skill-creator

To create and optimize AI skills, draft a SKILL.md file, run trigger evaluations, and iteratively refine the prompts until accuracy improves. This structured loop ensures consistent quality across prompt engineering and deployments.

Benchmarking AI agent prompt performance involves running comparative experiments and trigger evaluations to measure accuracy. Generate evaluation reports to identify weaknesses and propose stronger trigger descriptions for better discovery.

Iterative prompt engineering improves skill deployment by applying a structured create, validate, and refine loop. You modify the SKILL.md, run tests, and package the skill only after triggering accuracy meets your benchmark.

Yes, YAML is required as a dependency to validate and package AI skills. The skill lifecycle leverages YAML configurations for scripts and references, ensuring structured validation before deployment.

Yes, you can automate trigger description optimization by leveraging built-in tooling that proposes stronger descriptions based on evaluation results and holdout testing, ensuring better discovery and performance.

AI agent skills fail to trigger correctly when prompt descriptions lack accuracy. Running evaluation loops and holdout testing identifies these gaps, allowing you to iteratively refine the SKILL.md until triggering improves.