mllm-eval

# MLLM-Eval Skill ## Purpose This skill makes an **LLM / MLLM clinical evaluation defensible**: a real adjudicated reference standard, faithfulness measured not assumed, clinical-efficacy metrics beyond n-gram overlap, a pretraining- contamination check, prompt-sensitivity disclosed, and a reader study where text is generated. It is **model-agnostic** — every check applies to a closed API and to open weights — and **read-only** (an advisory design/audit skill): it audits the evaluation design and **specifies and routes** the clinical-efficacy metrics (RadGraph-F1 / CheXbert-F1 via their published extractors) rather than running the model or computing the metrics itself. It is the LLM/MLLM **evaluation-design counterpart** in the lane — an auditor that hands the specified metrics to their extractors and `/analyze-stats`, parallel to how `/model-validation` audits an imaging model's design (the imaging metrics themselves are computed by `/model-evaluation`). The reviewer-side audit of a finished manuscript uses the `mllm_evaluation.md` (ME0–ME8) probe via `/self-review` and `/peer-review`; this skill is the author-side harness design. It routes the reader study to `/design-ai-benchmarking`, the sizing to `/calc-sample-size`, and TRIPOD-LLM / MI-CLEAR-LLM compliance to `/check-reporting`. ## When to use - You are designing or auditing an evaluation of an LLM/MLLM on a clinical task and want it to cover the axes a reviewer will check (reference standard, faithfulness, conta...