skill-creator

To create and test AI skills systematically, draft a concise SKILL.md, prepare evaluation prompts, and iterate on the skill body and triggering description based on benchmark results. This structured workflow ensures quality-controlled skill development.

The best workflow for optimizing AI skill descriptions involves drafting the skill content, running trigger tests with evaluation prompts, and refining the frontmatter based on feedback and benchmark aggregation to improve triggering accuracy.

Yes, you need pyyaml to automate skill evaluation and iteration. This dependency supports the Python scripts used for generating reports, running evaluations, and aggregating benchmarks to scale skill development.

Yes, you can use automation scripts like generate_report.py, run_eval.py, improve_description.py, and aggregate_benchmark.py to scale AI skill benchmarking, analyze evaluation results, and refine both the skill body and frontmatter.

An evaluation and iteration loop for AI skills works by running trigger tests, analyzing the results against prepared eval prompts, and refining the SKILL.md content and description based on feedback until the skill meets quality benchmarks.

Before drafting an AI skill, you should prepare a clear intent for the skill, optional references, assets, and any supporting scripts to organize multi-domain functionality, ensuring a structured and repeatable creation workflow.