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-translationgit 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-translation)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-translation"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-translation/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-translation"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-translation.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 | $0.00165 | $0.01401 |
| Opus 5 | $0.00082 | $0.00700 |
| Sonnet 5 | $0.00033 | $0.00280 |
| Haiku 4.5 | $0.00016 | $0.00140 |
Grade A, and why
radiology-translation 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Translation, Reader Studies & Prospective Validation
Use this skill to turn "the model has good retrospective performance" into "the model helps in practice." This is the step top journals reward and reviewers demand before any clinical claim: a defined use scenario, a reader study, thresholds tied to actions, and prospective/real-world evidence.
Core stance
- Retrospective AUC ≠ clinical utility. Discrimination on a curated set says nothing about patient benefit. The claim must be earned with utility evidence, not asserted.
- Define the use scenario first. Screening, triage, diagnosis, staging, prognosis, treatment-response, surveillance, or MDT support — each fixes the output, the threshold, and the cost of errors.
- Reader studies are designed, not improvised. MRMC with washout, randomised order, radiologist-alone vs radiologist+AI, reader experience reported, and the right statistic (→ radiology-stats).
- Thresholds map to actions. Every operating point implies a clinical action and a cost of false positives/negatives; net-benefit / decision-curve quantifies it.
- Prospective beats retrospective; real-world beats curated. Plan temporal/prospective/ real-world validation and the PACS/RIS workflow position and constraints.
- Integrity & safety. Plan only; never claim deployment readiness without the evidence, and never give clinical or diagnostic recommendations for individual patients.
When to use
- "Design a reader study / clinician+AI gain study." / "帮我设计读者研究 / 医生+AI 增益研究。"
- "What does it take to claim clinical utility / move toward deployment?"
- "Plan a prospective / real-world validation." / "前瞻性验证、真实世界验证怎么设计？"
- "Map my model's threshold to a clinical action / net benefit."
- "Where does this sit in the PACS/RIS workflow?"
When to open extra files
| File | Open when |
|---|---|
| references/use-scenario.md | Defining the clinical-use scenario, the model output, and the cost of errors |
| references/reader-study.md | MRMC reader-study design: readers, washout, randomisation, with/without AI, outcomes |
| references/threshold-to-action.md | Mapping operating points to actions; net-benefit / decision-curve; false-positive/negative consequences |
| references/prospective-deployment.md | Prospective/real-world validation design; PACS/RIS integration; monitoring and drift |
| references/regulatory-and-deployment-readiness.md | The user asks about clinical translation, FDA/EU/regulatory readiness, silent deployment, locked/adaptive models, model cards, lifecycle monitoring, or whether a retrospective AI/radiogenomics model can claim clinical use |
What ships with it
6 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 · 89 lines · 165 tokens per session scan A 93678a8531cc
radiology-translation is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 165 tokens to every session and 1,401 once invoked, about $0.0008 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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