skill-creator

To create and test Claude skills, use a structured workflow that drafts SKILL.md frontmatter, runs evaluation scaffolding, and iterates on trigger accuracy to produce reliable, deployable skill artifacts.

Frontmatter validation and evals scaffolding are automated through built-in templates and tooling that generate evals.json, grading, and benchmark outputs to ensure consistent metadata and systematic testing for skill creation.

Yes, the skill creation tooling requires the pyyaml Python dependency to execute iteration loops, validate frontmatter, and package structured artifacts for consistent skill deployment.

The iterative improvement loop uses run_loop and improve_description functions to repeatedly test prompts, grade responses, and adjust descriptions, directly boosting trigger accuracy and robustness across domains.

Yes, packaging and distribution utilities compile validated skills into a .skill format, enabling easy sharing and consistent deployment of structured artifacts across different environments.

Inconsistent triggers occur without systematic evaluation; running evals scaffolding with grading and timing benchmarks, followed by iterative improvement loops, resolves this by refining trigger accuracy and reliability across domains.