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-paper2pptgit 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-paper2ppt)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-paper2ppt"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-paper2ppt/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-paper2ppt"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-paper2ppt.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 | $0.00143 | $0.01065 |
| Opus 5 | $0.00072 | $0.00532 |
| Sonnet 5 | $0.00029 | $0.00213 |
| Haiku 4.5 | $0.00014 | $0.00106 |
Grade A, and why
radiology-paper2ppt 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Imaging Paper → Chinese Journal-Club Deck
Turn an imaging paper into a concise, faithful Chinese .pptx for 读片会 / 文献汇报 / 组会.
The deck follows the paper's scientific argument, not its section order.
Core stance
- Argument as the spine. Classify the paper (diagnostic-accuracy / prediction model / radiomics / radiogenomics / review), then build the slide flow from its evidence chain.
- Figures are evidence. Use the paper's key figures/tables (imaging panels, ROC, calibration, Kaplan-Meier, forest); crop or split dense panels rather than shrinking them unreadable. Keep windowing/arrows visible for imaging panels.
- Real
.pptx. Build an actual deck (use thepptxskill), Chinese slide text + speaker notes, not a text outline. - Integrity. Never fabricate results, numbers, datasets, mechanisms, or figure details; if the source is partial, label gaps.
When to use
- "把这篇论文做成读片会/文献汇报 PPT。" / "Make journal-club slides from this imaging paper."
- "组会要讲这篇 radiomics/AI 论文，做个中文 PPT。"
When to open extra files
| File | Open when |
|---|---|
| references/deck-quality-qa.md | Building a full PPTX, repairing a weak deck, fixing cropped figures/text overflow/loose alignment, or running final slide QA |
Slide flow (adapt to paper type)
- 标题页 — title, authors/journal/year, presenter/date.
- 背景与临床问题 — the gap (1–2 slides).
- 研究目的/假设.
- 方法 — 数据与队列 (n, source, scanner/protocol); 影像分析/参考标准/阅片; 模型/特征流程 (radiomics: IBSI/分割/谐化; AI: 架构/划分/验证). Use a pipeline figure.
- 主要结果 — performance with CIs; key figure (ROC/KM/calibration) per slide; comparison/validation.
- 关键图 — the decisive imaging panel(s) or chart, cropped legibly.
- 结论与局限 — bounded conclusion + honest limitations.
- 点评/讨论 — presenter's critique: 报告规范 (CLAIM/CLEAR), 有无外部验证/泄漏/校准, 可复现性, 临床意义 — a journal-club value-add (route critique points via radiology-reporting).
- (可选) 思考/可借鉴 — what to borrow for our own work.
What ships with it
2 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 · 67 lines · 143 tokens per session scan A f3c2232f8232
radiology-paper2ppt is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 143 tokens to every session and 1,065 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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