skill-eval-methodology

To standardize skill evaluation workflows and prevent stale artifacts, use a reproducible eval pipeline that enforces consistent frontmatter and artifact formats like evals.json, timing.json, and grading.json across all runs.

A reproducible eval pipeline for benchmark iterations is a structured process that organizes artifact creation, validation, and tracking using standardized formats like benchmark.json to ensure auditable and consistent evaluation results.

Set up directory structures for reproducible skill evals by creating an eval iteration directory with an evals.json file and per-eval folders, then run a validation suite to verify frontmatter and structural completeness.

Yes, you can integrate pre-flight checklists and guardrails into your validation suite to ensure complete and consistent eval artifacts are generated, satisfying requirements for verification steps before running iterations.

Yes, this evaluation methodology integrates seamlessly with an eval-viewer workflow, enabling you to compare iterations, track improvements, and visualize skill evaluations through a viewer-friendly artifact structure.

You need consistent frontmatter and artifact formats for grading.json to prevent data loss and wasted iterations, ensuring that verification steps can validate evaluation outputs and maintain operational discipline across runs.