eval-harness

Run regression evaluations for Claude Code by defining capability and regression evals before implementation, then execute /eval commands to check and report pass@k metrics across model changes and tooling usage.

Eval-driven development replaces ad-hoc testing by structuring AI coding sessions around formal evaluation patterns, defining success criteria and capability evals prior to implementation to validate behavior across refactors.

Yes, AI feature evaluation supports deterministic code graders, model-based graders, and human-review prompts simultaneously, providing reusable templates to ensure quality and safety across integration points.

Baseline expected behavior by defining capability and regression evals with clear success criteria before implementation, then track pass@k metrics to validate regressions across refactors and tooling changes.

Track pass@k metrics during AI coding sessions by integrating reusable evaluation templates into Claude Code workflows, utilizing model-graders and code-based graders to monitor and report progress.

No, integration point evaluations require no external dependencies, allowing teams to directly integrate the evaluation templates into their existing Claude Code workflows and run /eval commands.