AI Feature Eval Writer — Golden Datasets, Rubrics, and LLM-as-Judge Prompts That Actually Catch Regressions

About This Skill

Teams ship LLM features with unit tests for the plumbing and vibes for the model — then a prompt tweak or model upgrade quietly breaks quality and nobody notices until users do. Evals are the missing test suite, and writing them is a craft: bad rubrics measure fluency instead of correctness, and naive judge prompts have known biases. AI Feature Eval Writer does the design and writes the artifacts. Describe your feature, what good looks like, and the failure modes you fear, and it produces the eval plan — each failure mode becomes its own metric with the cheapest grader that works (programmatic checks first, LLM-as-judge only where genuinely needed) and a blocking or warning threshold; a golden dataset design with 8 to 12 concrete starter cases including adversarial and correct-refusal cases; anchored 1-to-5 rubrics with observable level descriptions; ready-to-use judge prompts that mitigate position, length, self-preference, and halo biases, with a human calibration step before you trust them; and the CI regression gate that runs on every prompt or model change. The download includes three reference files: the eval-design worksheet, the LLM-as-judge prompt template with bias guards, and a complete worked sample suite. It designs and writes the artifacts; it does not execute evals or call models. Works with Claude Code, Cursor, Codex CLI, Gemini CLI, and any SKILL.md agent.