Eval Writer

Original name

Original description

Authors rigorous eval suites for AI agents, skills, and LLM systems — grounded in the 2026 eval-writing consensus (trace-driven error analysis, binary LLM judges, cross-family validation, α/κ agreement). Produces characterization, failure taxonomies, judge prompts, rubrics, and calibration protocols that harnesses (pmo-skill-refiner, CI) then execute. Two modes — Author (write from scratch) and Review (audit against the framework). First-class playbooks for per-skill evals and for pipeline stage-gate judgment content; generic fallback for arbitrary AI systems. Use whenever the user asks to write evals, audit evals, add eval coverage, calibrate a judge, build a rubric, write a judge prompt, or diagnose why a judge keeps passing broken outputs.