skill-creator

To create and optimize Claude skills, draft an initial SKILL.md defining the capability, outline an evaluation plan, and run benchmarking iterations to refine prompt behavior and triggering accuracy for deployment.

Benchmarking Claude skill performance involves running structured evaluations on test prompts, comparing results quantitatively across iterations, and applying trigger optimization to frontmatter and prompts to improve reliability.

The three-level loading model progressively loads skill metadata, operational instructions, and resource bundles, ensuring efficient context management and reliable execution during Claude skill processing.

Yes, you can improve triggering accuracy by applying trigger optimization techniques to existing skill frontmatter and prompts, evaluating performance, and iterating until reliable activation is achieved.

A structured evaluation plan is required before benchmarking Claude skills, as it defines test prompts, expected behaviors, and quantitative metrics to compare performance across multiple iterations effectively.

Unreliable triggering in Claude skills usually stems from unoptimized frontmatter or ambiguous prompts, requiring trigger optimization and benchmarking iterations to quantify and improve activation accuracy.