skill-creator

You can automate Claude Code skill creation and evaluation by using Python-based CLI tooling to parse SKILL.md files, run evaluation loops, and benchmark performance via the Anthropic API. This handles drafting, refinement, and triggering optimization end-to-end.

The best way to benchmark and refine Claude Code skill triggers is to run automated iteration loops that test triggering descriptions against evaluation datasets. The CLI utilities highlight accuracy gaps and apply performance improvements directly to the skill definitions.

Yes, you can use Python scripts to iterate on Claude Code skill drafts before deployment. The tooling parses SKILL.md files, runs evals, and automates the iteration loop to test triggers and measure performance improvements using the Anthropic API.

Yes, you need both the Anthropic API and pyyaml dependencies to run Claude Code skill benchmarks. The Anthropic API handles the evaluation calls, while pyyaml parses the skill configuration files during the automated iteration and benchmarking process.

Claude Code skill evaluation loops fail to parse SKILL.md files when the YAML front matter or content structure does not match the expected schema. Ensure your skill definitions are properly formatted so the Python CLI utilities can extract and benchmark the triggering descriptions.

You need to run benchmarks on existing Claude Code skills when updating triggering descriptions, measuring performance regressions, or validating improvements after refinement. The benchmarking tooling uses evals to highlight areas needing optimization before redeployment.