skill-creator

Automating prompt engineering evaluation requires running trigger-evaluation loops with holdout and test splits. You can execute parallel runs to test how prompts trigger skills, then feed the results back into SKILL.md to improve triggering accuracy iteratively.

Benchmarking skill versions involves generating benchmark.json and benchmark.md files to compare performance metrics and identify meaningful gains. This structured approach produces audit-friendly reports that track improvements across skill iterations.

Creating production-ready Claude skills requires systematizing the end-to-end pipeline using SKILL.md to define identity and metadata. You enforce frontmatter-based metadata surfaces, then validate and iterate through automated evaluation loops to achieve traceable results.

Yes, the skill evaluation pipeline requires both the anthropic and pyyaml dependencies to support its end-to-end automation. These external dependencies provide the foundational tooling needed to execute trigger-evaluation loops and process structured metadata.

Collecting qualitative feedback on skill outputs is handled through an eval-review workflow that produces audit-friendly reports. This workflow sits alongside the automated benchmark generation to capture human insights during the iterative improvement cycle.

Inconsistent skill triggering often stems from unrefined descriptions in SKILL.md. You can use the improve_description tool to iterate on the skill description, feeding results back into the metadata to increase trigger evaluation accuracy across test splits.