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-figuregit 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-figure)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-figure"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-figure/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-figure"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-figure.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 | $0.02107 |
| Opus 5 | $0.00082 | $0.01053 |
| Sonnet 5 | $0.00033 | $0.00421 |
| Haiku 4.5 | $0.00016 | $0.00211 |
Grade A, and why
radiology-figure 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Radiology Publication Figures
Use this skill to build figures that pass Radiology's technical and editorial bar: correct file format and resolution, legible typography, color-blind-safe palettes, honest axes, and the specific chart types imaging-AI reviewers expect (ROC, calibration, decision-curve, forest/SROC, Kaplan-Meier, Bland-Altman), plus de-identified annotated imaging panels.
Core stance
- Vector first. Primary output is editable
.svg(or.pdf); secondary is a ≥ 300 dpi raster (TIFF/PNG). Keep text as text (svg.fonttype='none'), not outlines, so editors can re-typeset. - One figure, one message. Each panel answers one question; no two panels duplicate it. Panels are labelled A, B, C (Radiology-family) or a, b, c (Nature-family — the case is venue-dependent, never mixed within one manuscript; see "When to open extra files").
- Honest graphics. Axes start where the data demand (don't truncate to exaggerate); show uncertainty (CI bands, error bars); state n.
- De-identify every image. No PHI burned into pixels, no faces/identifiers; scrub DICOM overlays; report windowing (WL/WW) and add a scale bar where size matters.
- Match the journal. Sans-serif (Arial/Helvetica), figure width to column — Radiology-family single ~85 mm / double ~170 mm, or Nature-family single 89 mm / double 183 mm (max height 170 mm) — adequate font size at final print size (≈ 7–9 pt min). Confirm the target venue before sizing the first figure.
- Never fabricate data. Plot only supplied/loaded values; mark simulated/example data clearly.
When to use
- Statistical figures: ROC (+ DeLong annotation), calibration, decision-curve, forest, SROC, Kaplan-Meier (with numbers-at-risk), Bland-Altman, box/violin, heatmaps/clustermaps.
- Radiogenomics: MOFA/factor plots, deconvolution stacked bars, habitat maps, correlation heatmaps.
- Imaging panels: multi-row montages, before/after, arrows/insets, windowing labels, scale bars.
- Flow diagrams: CONSORT / STARD / PRISMA patient-selection diagrams.
What ships with it
12 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.
- README.md 2.2 KB
- references/api.md 12 KB
- references/chart-types.md 2.9 KB
- references/color-systems.md 4.0 KB
- references/design-theory.md 2.6 KB
- references/figure-intent-and-render-qa.md 4.8 KB
- references/figure-set-consistency.md 3.8 KB
- references/imaging-panels.md 3.1 KB
- references/journal-family-visual-style.md 8.4 KB
- references/nature-figure-spec.md 6.1 KB
- references/radiology-figure-guidelines.md 2.9 KB
- references/survival-figures.md 5.4 KB
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 · 118 lines · 165 tokens per session scan A ed7be4258874
radiology-figure 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 2,107 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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