skill-creator

To create and test AI agent skills, you can use a scaffolded workflow to capture intent, draft instructions, run evaluations, and benchmark trigger accuracy before deployment. This iterative process validates performance and ensures the skill behaves as expected.

Skill evaluation and benchmarking is the process of measuring trigger rates, timing, and outputs using specific scripts. It guides improvements by confirming triggering accuracy and performance, ensuring the AI agent activates only when appropriate.

You iterate on a SKILL.md file by drafting frontmatter and detailed instructions, then running an evaluation loop to test triggering. Reviewing the benchmark results allows you to refine the description and tooling until skill performance stabilizes.

Yes, you need Python and YAML dependencies to develop and validate skills. The workflow relies on anthropic and pyyaml libraries to run evaluation scripts, parse configuration files, and benchmark the AI agent interactions.

The best way to capture user intent is by analyzing user goals to explicitly shape the skill's scope, inputs, and outputs. This design phase produces analysis and design docs that define the boundaries before writing the actual instructions.

Incorrect skill triggering often happens when evaluations are skipped before deployment. Running benchmark and evaluation scripts to measure trigger rates and iterate on the SKILL.md description helps prevent false activations and refines performance.