skill-creator

To create and test Claude skills at scale, use a structured workflow to draft SKILL.md definitions, build evaluation prompts, run end-to-end eval loops, and iteratively refine descriptions based on benchmark results.

Evaluation iteration for prompt engineering works by applying feedback to the skill description and associated artifacts, then re-running evaluations to measure triggering accuracy and reliability across train and test prompts.

The best way to structure SKILL.md for automation is to use a formal frontmatter structure that defines the skill, enabling repeatable validation and packaging for deployment or integration with other tooling.

Yes, you can use Python and YAML to manage Claude skill authoring evals, as the workflow relies on anthropic and pyyaml dependencies to generate eval artifacts like evals.json and iterations history.

When running Claude skill evaluations, the workflow generates eval artifacts including evals.json, iterations history, and benchmark-ready reports to track results and measure triggering accuracy.

Inaccurate triggering during testing often requires iterative improvement; you can apply feedback to the skill description and associated artifacts, then re-run evaluations to refine reliability.