skill-creator

To create and package AI skills, draft an initial SKILL.md file, run an evaluation loop to benchmark performance, and validate the skill to produce a distributable .skill archive. This workflow ensures skills are tested and reproducible before deployment.

An auditable evaluation loop for skill development formalizes testing by running trigger evaluations, collecting metrics, and comparing configurations. This process identifies bottlenecks and generates benchmarks to understand the impact of iterative improvements.

Skill-creator requires the pyyaml dependency to function. You need this library installed in your environment to support the automation workflow, evaluation processes, and packaging of the generated skill artifacts.

You can optimize SKILL.md descriptions by utilizing the description optimization feature, which generates improved text based on trigger evaluation results and collected metrics. This ensures the skill description aligns with actual benchmarked performance.

The best way to identify bottlenecks in a prompt-engineering workflow is to formalize an auditable evaluation loop. By collecting metrics and comparing configurations, you can pinpoint exactly where skill triggers fail or underperform during testing.

You should avoid using an automated skill packaging workflow if your project lacks an initial SKILL.md draft or evaluation metrics. Without baseline trigger evaluation data, the packaging process cannot ensure reproducibility or validate performance improvements.