Implements Eval-Driven Development (EDD) to quantify AI performance and prevent regressions during coding sessions.
The Eval Harness skill provides a formal framework for implementing Eval-Driven Development (EDD) within Claude Code. By treating evaluations as the unit tests of AI development, it allows developers to define success criteria before implementation, track progress using pass@k metrics, and ensure long-term stability via automated regression checks. This skill is essential for transitioning from trial-and-error AI interactions to a measurable, deterministic development process that ensures Claude's outputs meet specific project requirements.
Key Features
01CLI-integrated workflow for defining, checking, and reporting evals
02Standardized evaluation reporting and history logging
0334,604 GitHub stars
04Multi-modal grading including Code-based, Model-based, and Human review
05Capability and regression evaluation frameworks
06Pass@k and Pass^k reliability metric tracking
Use Cases
01Defining feature success criteria before initiating AI-led code generation
02Quantifying the reliability of AI-generated logic across multiple attempts
03Preventing regressions in legacy codebases during complex refactoring tasks
