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-statsgit 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-stats)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-stats"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-stats/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-stats"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-stats.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 | $0.00178 | $0.01738 |
| Opus 5 | $0.00089 | $0.00869 |
| Sonnet 5 | $0.00036 | $0.00348 |
| Haiku 4.5 | $0.00018 | $0.00174 |
Grade A, and why
radiology-stats 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Imaging Biostatistics for Radiology
Use this skill to choose the right test, run it correctly, and report it the way Radiology wants — estimates with 95% CIs, exact p-values, named tests, and multiplicity handled. It covers the statistics that imaging-AI, radiomics, and reader studies live or die on.
Core stance
- Estimate + uncertainty, not just p. Every primary result gets a 95% CI. Report exact
p-values (e.g.
P = .03, notP < .05); useP < .001only below that floor. - The test must match the design. Paired data → paired test (same patients/cases read by both methods); clustered data (multiple lesions per patient) → account for clustering; multiple readers → MRMC, not a naive average.
- Discrimination is not enough for a clinical model. Report calibration and clinical utility (decision-curve) alongside AUC.
- Control multiplicity honestly. Thousands of radiomic/omic features ⇒ FDR or stronger; pre-specify primary vs exploratory.
- No fishing, no fabrication. Pre-specify the primary analysis; never invent a number, a CI, or a p-value. If data are insufficient, say what is needed.
- Reproducible. Return runnable code (Python first; R where it is the field standard) with the software/version and the exact method for CIs.
When to use
- Diagnostic accuracy: sensitivity/specificity/PPV/NPV/accuracy/likelihood ratios + CIs.
- Comparing models/readers/tests: DeLong or bootstrap for AUCs; McNemar for paired sensitivity/specificity.
- Reader studies: kappa / weighted kappa / Fleiss / ICC / Bland-Altman; MRMC design and analysis.
- Prediction models: ROC, calibration (slope/intercept, Brier), decision-curve analysis, threshold selection.
- Radiomics/omics: feature reproducibility (ICC), multiple-testing correction, cross-validation/nested CV, bootstrap optimism.
- Survival/prognosis: Kaplan-Meier + log-rank, Cox, C-index, time-dependent ROC, competing risks.
- Planning: sample size for accuracy / AUC; EPV and Riley minimum sample size for prediction models.
What ships with it
7 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 · 102 lines · 178 tokens per session scan A 7eae821eda90
radiology-stats is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 178 tokens to every session and 1,738 once invoked, about $0.0009 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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