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
, parallel to how
audits an imaging
model's design (the imaging metrics themselves are computed by
). The reviewer-side
audit of a finished manuscript uses the
(ME0–ME8) probe via
and
; this skill is the author-side harness design. It routes the reader study to
, the sizing to
, and TRIPOD-LLM / MI-CLEAR-LLM compliance to
.
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 → .
- Imaging prediction/segmentation model → + .
- Image-to-image generative model → the probe.
- Training / serving the LLM → out of scope.
- Item-level TRIPOD-LLM / MI-CLEAR-LLM audit of a finished manuscript → .
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
, ICC/κ to
, sizing to
).
Phase 5 — Gate the plan (deterministic)
bash
python3 ${CLAUDE_SKILL_DIR}/scripts/check_mllm_eval_completeness.py \
--plan plan.md --task report_generation|vqa|classification --strict
/
/
REFERENCE_STANDARD_MISSING
/
CONTAMINATION_UNADDRESSED
/
must be resolved.
Phase 6 — Hand off
Methods/Results →
; compliance (TRIPOD-LLM / MI-CLEAR-LLM) →
; reviewer
audit →
(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 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