skill-creator

You create and iterate Claude skills by drafting a SKILL.md with name and description, then running an evaluation loop with train/test holdouts to quantify improvements and prevent overfitting. This automated workflow handles testing, reporting, and packaging into deployable artifacts.

The best way to evaluate prompt engineering workflows is to apply train/test holdouts during iteration to prevent overfitting. This approach quantifies improvements through automated benchmarks and reporting, ensuring your skill triggers and descriptions are accurately tuned before deployment.

Yes, you need the pyyaml dependency installed to support the skill creation workflow. It enables the automated evaluation, reporting, and packaging processes that convert your SKILL.md drafts and associated scripts into deployable skill artifacts.

Automated benchmarking improves skill description optimization by providing quantitative feedback during the iteration loop. By evaluating trigger tuning and description accuracy against test holdouts, you can measure performance gains and refine the skill to achieve higher accuracy before packaging.

Yes, you can use this skill evaluation workflow for any domain. The end-to-end lifecycle of drafting, testing, evaluating, and iterating applies universally across domains, allowing you to standardize skill creation and deployment regardless of the specific subject matter.

You should use train/test holdouts for skill iteration when you want to prevent overfitting and accurately quantify improvements. This method ensures that your evaluation benchmarks reflect genuine performance gains rather than memorized data, yielding more robust deployable artifacts.