Implements a formal evaluation framework for Claude Code sessions using eval-driven development (EDD) principles.
The Eval Harness skill brings rigorous testing methodologies to AI-assisted workflows by enabling Eval-Driven Development (EDD) within Claude Code. It allows developers to define success criteria and benchmarks before implementation, ensuring that AI-generated code meets specific functional requirements and reliability standards. By supporting deterministic code-based graders, probabilistic model-based evaluations, and manual human reviews, this skill helps teams track agent performance through pass@k metrics and maintain high-quality codebases through structured regression testing.
Key Features
01112,914 GitHub stars
02Reliability measurement with pass@k and pass^k metrics
03Multi-modal grading including code-based and model-based graders
04Standardized capability and performance benchmarking
05Eval-Driven Development (EDD) workflow management
06Automated regression testing for agent and prompt stability
Use Cases
01Measuring agent reliability across different LLM model versions
02Defining pass/fail criteria for complex AI-assisted coding tasks
03Creating regression test suites to prevent breaking changes in AI prompts
