skill-creator

To create and iterate on Claude skills, draft the initial SKILL.md intent, run structured evaluation loops to benchmark trigger accuracy and performance, then collect results and refine until satisfied. This eval-and-iterate loop ensures consistent skill quality.

The best way to benchmark Claude skill performance is running structured evals and viewing quantified results with the eval-viewer. This approach measures trigger accuracy and performance, allowing you to capture feedback and generate improvements iteratively.

To validate SKILL.md frontmatter and structure, package and validate the skill files using the built-in automation scripts. This ensures your skill's frontmatter and structure meet the required deployment standards before release.

Yes, you can use Python and PyYAML to automate Claude skill creation workflows. The skill relies on the Anthropic and PyYAML dependencies to execute end-to-end skill development, from drafting intents to running evaluation loops.

Yes, the skill-creator workflow supports continuous improvement through feedback collection. It captures feedback from evaluation runs, generates targeted skill improvements, and re-runs the benchmark loop until the desired performance is achieved.