Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Aperivue/medsci-skills --skill mllm-evalgit clone --depth 1 https://github.com/Aperivue/medsci-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aperivue/medsci-skills/mllm-eval)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/mllm-eval"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/mllm-eval/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/mllm-eval"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/mllm-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00140 | $0.01729 |
| Opus 5 | $0.00070 | $0.00864 |
| Sonnet 5 | $0.00028 | $0.00346 |
| Haiku 4.5 | $0.00014 | $0.00173 |
Grade A, and why
mllm-eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_synthesisprobe. - 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.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/evaluation_axes.md 11 KB
- scripts/check_mllm_eval_completeness.py 9.2 KB runs code
- scripts/mllm_eval_completeness_challenge/fixture/plan_bad.md 202 B
- scripts/mllm_eval_completeness_challenge/fixture/plan_good.md 795 B
- scripts/mllm_eval_completeness_challenge/problem.md 1.6 KB
- scripts/mllm_eval_completeness_challenge/verify.sh 1.3 KB runs code
- skill.yml 3.3 KB
- tests/test_mllm_eval_completeness.sh 3.1 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 117 lines · 140 tokens per session scan A afdc167bfccb
mllm-eval is a skill published in the GitHub repository Aperivue/medsci-skills (291 stars, last pushed 3d ago), licensed MIT. It adds 140 tokens to every session and 1,729 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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