Mllm Eval Agent Skill

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, contamination, prompt sensitivity, reader

study).

When NOT to use

• AI-vs-human-expert benchmark with a rated rubric → /design-ai-benchmarking.
• Imaging prediction/segmentation model → /model-evaluation + /model-validation.
• Image-to-image generative model → the image_synthesis probe.
• Training / serving the LLM → out of scope.
• Item-level TRIPOD-LLM / MI-CLEAR-LLM audit of a finished manuscript → /check-reporting.

Workflow

Phase 1 — Pin the task, model, comparator, decoding (ME0)

State the task (report generation / VQA / extraction-classification), the exact model + version/date

(closed API or open-weights id), the decoding settings (temperature, seed, max tokens), and what the

outputs are scored against.

Phase 2 — Reference standard + metrics (ME1, ME2)

Require an adjudicated expert reference (not a single unverified report or a model-derived label).

For report generation, report a clinical-efficacy metric — RadGraph-F1 (Jain et al., NeurIPS

2021) or CheXbert-F1 (Smit et al., 2020), or the composite RadCliQ (Yu et al., *Patterns* 2023)

— alongside any BLEU/ROUGE, with CIs. For VQA/classification, report accuracy at the

real prevalence with a stated answer-matching rule.

Phase 3 — Faithfulness + contamination (ME3, ME4)

Add an atomic-fact faithfulness measure + a false-premise / abstention probe (MedVH, Med-HALT) —

report a hallucination rate, not just accuracy. For any public benchmark (VQA-RAD, SLAKE, MIMIC-CXR-

derived, MedQA), add a contamination statement: training cutoff vs benchmark release, a held-out /

post-cutoff set, or a contamination probe.

Phase 4 — Prompt sensitivity + reader study (ME5, ME7)

Disclose the exact prompt(s), temperature/seed, ≥ 3 runs with variance, and a prompt-robustness

check. For a deployment/utility claim, design a blinded reader study with an error taxonomy (route

the rubric/IRR to /design-ai-benchmarking, ICC/κ to /analyze-stats, sizing to /calc-sample-size).

Phase 5 — Gate the plan (deterministic)

python3 ${CLAUDE_SKILL_DIR}/scripts/check_mllm_eval_completeness.py \
  --plan plan.md --task report_generation|vqa|classification --strict

NGRAM_ONLY / FAITHFULNESS_MISSING / REFERENCE_STANDARD_MISSING / CONTAMINATION_UNADDRESSED /

READER_STUDY_MISSING must be resolved.

Phase 6 — Hand off

Methods/Results → /write-paper; compliance (TRIPOD-LLM / MI-CLEAR-LLM) → /check-reporting; reviewer

audit → /self-review (loads ME0–ME8).

Anti-Hallucination

• Never fabricate model outputs, reference labels, or metric scores. Compute only what the supplied

outputs allow; if a reference standard or outputs are missing, say so and stop.

• Never report n-gram overlap (BLEU/ROUGE) as clinical correctness — pair it with a clinical-efficacy

metric, and flag the n-gram score for what it is.

• Never claim "no contamination" without a stated check when a public benchmark is used.
• If a metric (RadGraph-F1 / CheXbert-F1) or its extractor is uncertain, flag [VERIFY] and ask rather

than inventing a number.

Deterministic gate

scripts/check_mllm_eval_completeness.py — task-aware presence gate on the evaluation plan (stdlib,

network-free). Reproducible challenge:

bash ${CLAUDE_SKILL_DIR}/scripts/mllm_eval_completeness_challenge/verify.sh.

Boundaries

mllm-eval (this skill: harness design + completeness gate, model-agnostic)
  ├─ design-ai-benchmarking (reader-study rubric / IRR)
  ├─ calc-sample-size (reader + case sizing)
  ├─ write-paper + check-reporting (TRIPOD-LLM / MI-CLEAR-LLM)
  └─ self-review / peer-review (ME0–ME8 reviewer probe)

Reference Files

• ${CLAUDE_SKILL_DIR}/references/evaluation_axes.md — the *why* behind the ME2–ME7 axes:

clinical-efficacy metrics beyond n-gram overlap (e.g. RadGraph-F1 / CheXbert-F1 vs BLEU/ROUGE),

faithfulness & hallucination, pretraining/benchmark contamination, prompt-sensitivity &

determinism, answer-matching, and the reader study — each mapped to its gate verdict. Load on

demand during Phases 2–4.