skill-creator

Skill iteration and benchmarking is the process of running structured evaluation loops on Claude skills, comparing results against baselines, and refining descriptions to maximize triggering accuracy and performance. It helps teams test and improve skill reliability.

To create and optimize Claude skills, you draft a skill concept, write the SKILL.md file, and run initial evaluations to test triggering and performance. This structured process allows you to iteratively refine the skill description and capabilities.

Yes, the skill-creator requires the pyyaml dependency to function. You need this Python library installed in your environment to orchestrate the end-to-end creation and optimization workflows.

The best way to improve triggering accuracy and coverage is through description optimization. By running structured evals, aggregating benchmark results across runs, and refining the triggering description, you can identify when changes actually improve reliability.

Evaluation loop benchmarking works by running structured evaluations, comparing performance with baselines, and aggregating results across multiple runs. This identifies when iterative description changes actually improve triggering accuracy and usefulness across diverse user intents.