skill-creator

To build and evaluate Claude Agent Skills, you define the skill intent, draft a SKILL.md file, prepare evaluation prompts, run structured evals, and iterate on the description until performance targets are met.

The best way to quantify agent performance is by executing structured evaluation loops that benchmark results, allowing you to measure performance and guide description improvements iteratively.

You package and document agent scripts by bundling scripts, references, and assets into a distributable skill file while maintaining metadata to ensure reuse and benchmarking capability.

Yes, you need PyYAML and Anthropic dependencies to support the advanced internal design and reasoning required to codify new agent skills and execute structured evaluations.

Agent skill evaluation performance may fail to stabilize if the SKILL.md description is not iteratively rewritten and tested against benchmark evals until the quantified results meet your targets.