skill-creator

To build and test AI skills, use a structured workflow that guides you through defining skill intent, writing SKILL.md documentation, developing test cases, and running evaluations against baseline models to measure performance.

Optimizing AI skill descriptions for triggering accuracy involves iteratively refining the text based on performance metrics and evaluation results. This process automatically tunes the skill descriptions so the LLM invokes the capability whenever relevant.

Iterative improvement for LLM prompt engineering works by running evaluations against baseline models, analyzing performance metrics, and refining skill descriptions based on user feedback to achieve enhanced triggering accuracy and better overall AI agent responses.

Yes, you need to write test cases to evaluate AI agent performance. Developing test cases is a core part of the workflow, allowing you to run evaluations against baseline models and analyze performance metrics for iterative improvement.

Yes, you can use this workflow to refine existing AI skills. It supports end-to-end creation, testing, and optimization, allowing you to analyze performance metrics and iteratively improve skill descriptions for existing capabilities.