skill-creator

Automating AI skill creation involves using bundled scripts to draft, test, and benchmark skills iteratively. This approach supports evaluation with eval prompts and trigger analysis to improve triggering accuracy and refine performance.

Iterating and benchmarking AI skills requires running an evaluation loop to test, review, and track progress across iterations. You can leverage bundled metadata to manage packaging, evaluation, and version history for production-ready artifacts.

Trigger analysis for AI skill descriptions evaluates how accurately a skill activates in response to expected prompts. By running iterative evaluations, you optimize descriptions to improve triggering accuracy across various use cases.

Yes, you need pyyaml installed to support packaging and validating AI skills. This dependency enables the bundled scripts to parse metadata and manage skill packaging as portable units for reuse across projects.

Yes, you can review evaluation results via an integrated viewer during the skill iteration workflow. This allows you to track progress across iterations, assess benchmark performance, and refine triggers before final packaging.

An automated workflow for skill development is not suited for one-off, non-repeatable prompts that do not require benchmarking or version history tracking. It is designed for structured, auditable loops needing iterative evaluation and deployment.