radiology-skills is a collection of Codex skills for medical-imaging research, covering radiomics, deep learning, imaging genomics, multimodal studies, statistics, validation, and scientific publishing. It is intended for researchers who design, analyze, write, and submit medical-imaging AI studies. The catalogue entries are its modular research workflows and specialist advisory skills.
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 huang-sir1/radiology-skills --skill radiology-frontiergit clone --depth 1 https://github.com/huang-sir1/radiology-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/huang-sir1/radiology-skills/radiology-frontier)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-frontier"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-frontier/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/huang-sir1/radiology-skills/radiology-frontier"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-frontier.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 | $0.00207 | $0.01600 |
| Opus 5 | $0.00103 | $0.00800 |
| Sonnet 5 | $0.00041 | $0.00320 |
| Haiku 4.5 | $0.00021 | $0.00160 |
Grade A, and why
radiology-frontier 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 13d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Frontier Directions & Evidence Layer
Use this skill to turn "what's hot in imaging AI" into a publishable question matched to the user's data — and to expose the publication-pattern evidence behind each recommendation. It is the strategic front of the chain: before designing (→ radiology-design), decide what is worth doing and likely to be accepted at a high-impact venue.
Core stance
- Frontier ≠ feasible for you. A direction is only useful if the user's data can actually carry it. Always test a trend against their disease, modality, n, centers, labels, and omics.
- Evidence over vibes. Recommendations are grounded in how top journals actually publish — design patterns, validation expectations, and what each venue rewards — not in slogans.
- Patterns are durable; specific papers are not. This skill encodes publication-pattern heuristics (the kinds of studies that get into each journal and the methodological bar they meet). It does not ship a fixed citation list. Concrete recent papers must be retrieved and verified live (→ radiology-search); never cite a PMID/DOI from memory.
- Separate hot from suitable. Name directions that are trendy but a poor fit for the data, and say why — steering away from a wrong direction is as valuable as suggesting a right one.
- Bound novelty claims. "First/novel" is a liability without a literature check. Frame innovation as a specific, defensible gap, not a superlative.
- Integrity. Never fabricate references, effect sizes, or "recent studies show…" claims. Mark anything that needs same-day verification.
When to use
- "Give me frontier directions for [disease/modality] I can publish in the next 1–2 years."
- "找近三年的前沿方向和创新点" / "结合我的数据找创新点。"
- "Is [foundation models / self-supervised / VLM / multimodal / federated] right for my data?"
- "What's the evidence/publication-pattern basis for this recommendation?" / "有什么文献依据？"
- "Which top journals publish this kind of study, and what do they demand?"
What ships with it
5 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.
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.
- 13d ago First seen · 94 lines · 207 tokens per session scan A 24b52701a907
radiology-frontier is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 207 tokens to every session and 1,600 once invoked, about $0.0010 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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