skill-creator

To build an AI skill, you define the intent, draft the `SKILL.md` file, set up test cases, and run evaluations to iteratively refine the skill's performance and triggering accuracy.

The best way to optimize a skill description is by running iterative evaluations and analyzing performance benchmarks to refine the text until the AI agent reliably triggers for relevant user queries.

Yes, you can enhance existing LLM skills by running new test cases, analyzing evaluation metrics, and iteratively refining both the skill logic and the description for improved accuracy.

The end-to-end skill development lifecycle involves intent capture, drafting `SKILL.md`, structuring resources, setting up test cases, running evaluations, and optimizing descriptions based on performance benchmarks.

You evaluate prompt engineering logic by setting up specific test cases, running evaluations within the skill development environment, and analyzing the resulting performance benchmarks to measure accuracy.

An AI skill fails to trigger reliably when its description is poorly optimized, which requires iterative testing and refinement of the description field based on performance benchmarks to achieve accurate triggering.