skill-creator

To create and test AI agent skills, you draft skill definitions, set up test cases, and run evaluations against baseline models to measure performance. This allows you to analyze results and iteratively improve skill effectiveness through automated benchmarking.

Automated evaluation in prompt engineering is the process of running tests with and without a skill to benchmark performance. It generates metrics and reporting to analyze results and iteratively improve the effectiveness of AI skills.

To optimize AI skill descriptions for better triggering, use description tuning tools to automatically refine the text. This ensures optimal triggering accuracy and improves the discoverability of your AI capabilities within the system.

Yes, you can benchmark performance against baseline models without coding. The skill-creator guides users of all technical backgrounds through running comprehensive evaluations and analyzing benchmark reporting to measure performance differences.

You need Python dependencies like pypdf, pdfplumber, and pdf2image if your skill processes PDF files. These libraries provide the environment setup required to extract text and images from documents during skill development and testing.

The best way to iteratively improve AI skills is to refine them based on user feedback and performance metrics gathered through automated testing. This involves analyzing evaluation results against baseline models to guide modifications.