What problem does it solve? AI-assisted coding sessions often lack objective success criteria, making it hard to know whether an agent's output actually works or whether a prompt change broke existing behavior. This Skill introduces eval-driven development (EDD), treating evals as unit tests for AI workflows so you can define pass/fail criteria before implementation and measure reliability over time. ## Core Features & Use Cases - Capability and Regression Evals: Define structured eval templates that test new agent abilities and guard existing functionality against regressions. - Three Grader Types: Use deterministic code-based graders (grep, npm test, build checks), model-based graders for open-ended outputs, and human review flags for high-risk changes. - Reliability Metrics: Track pass@k (at least one success in k attempts) and pass^k (all k trials succeed) to benchmark agent performance across model versions. - Use Case: Before adding an authentication feature, define evals for registration, login, and session handling, then run /eval check add-authentication during implementation and generate a final report showing 5/5 capability evals passed with pass@3 at 100%. ## Quick Start Ask the agent to define an eval-driven development plan with pass/fail criteria for the feature you are about to implement.