skill-creator

You can automate Claude skill creation and evaluation by running a structured workflow that drafts SKILL.md descriptions, executes trigger evaluations, and benchmarks iteration results using provided scripts and a viewer.

The best way to iterate on prompt engineering workflows is to apply automated benchmarking and trigger evaluation loops, refining SKILL.md descriptions progressively until measurable, production-ready capability is achieved.

Yes, you need Python 3.8 or higher, the anthropic SDK, and PyYAML installed to run the evaluation scripts, aggregate benchmarking results, and access the repository's skill assets.

Yes, the evaluation loop specifically runs trigger evaluations and refines SKILL.md descriptions separately, allowing you to measure and optimize metadata alignment against actual skill performance.

Progressive skill loading works by loading metadata first, followed by the SKILL.md body, and finally bundling optional scripts, references, and assets only when they are needed during execution.

Limitations include requiring explicit access to the repository's skill asset directories and dependencies, meaning the automation cannot evaluate or benchmark skills without the underlying scripts and references present.