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-datagit 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-data)<a href="https://agentmods.dev/skills/huang-sir1/radiology-skills/radiology-data"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-data/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-data"><img src="https://agentmods.dev/badge/skills/huang-sir1/radiology-skills/radiology-data.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 | $0.00146 | $0.01213 |
| Opus 5 | $0.00073 | $0.00607 |
| Sonnet 5 | $0.00029 | $0.00243 |
| Haiku 4.5 | $0.00015 | $0.00121 |
Grade A, and why
radiology-data 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data & Code Availability + De-identification
Prepare submission-ready Data Availability and Code/Model Availability statements, plan DICOM de-identification, choose repositories, and check FAIR — for imaging and imaging+omics (radiogenomics) studies.
Core stance
- Every result-supporting dataset maps to a concrete access route — public repository + accession, controlled access + steward, or a justified restriction. Avoid bare "available on reasonable request" (editors increasingly reject it; if used, name the controller and conditions). At Nature-portfolio venues this is stated as a condition of publication, not a recommendation — treat it accordingly.
- De-identify before sharing any imaging — DICOM headers and burned-in pixel PHI; defacing for head imaging.
- Cite datasets like literature (DataCite-style: creator, title, repository, year, identifier).
- Share code/models for reproducibility (CLAIM/TRIPOD+AI open-science items).
- Don't overstate or fabricate — no invented accessions; controlled data described honestly with the access process.
When to use
- "Write the Data Availability / Code Availability statement."
- "How do I de-identify these DICOMs for TCIA / a public release?"
- "Which repository for my images / radiomic features / RNA-seq?"
- "Write dataset citations / check FAIR."
- "We have controlled genomics (dbGaP/EGA) — how do I word availability?"
- "What goes in Extended Data vs Supplementary Information vs Source Data?" (Nature-portfolio)
When to open extra files
| File | Open when |
|---|---|
| references/dicom-deidentification.md | De-identifying imaging: DICOM tags, pixel PHI, defacing, standards/tools |
| references/repositories.md | Choosing a repository for images, features, code/models, and omics (open vs controlled) |
| references/availability-and-fair.md | Statement templates, dataset citations, FAIR checklist, Chinese-author alignment |
| references/ai-radiogenomics-public-resources.md | Selecting public datasets for radiology AI/radiogenomics, planning external validation or pretraining, or checking TCIA/GDC/PhysioNet/GEO/dbGaP/EGA-style resource roles |
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 · 74 lines · 146 tokens per session scan A 3031baef7bac
radiology-data is a skill published in the GitHub repository huang-sir1/radiology-skills (1,687 stars, last pushed 1mo ago), licensed MIT. It adds 146 tokens to every session and 1,213 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.
Other skills, from other repositories
analytical-method-validation
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP / / , ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays…
nature-paper-to-patent
Convert scientific papers, theses, technical reports, source code, figures, inventor notes, or research manuscripts into evidence-grounded Chinese invention patent drafts and attorney-facing technical disclosure materials. Use when an AI agent must mine patent points, draft or revise a Chinese technical disclosure…
auditing-part11-trails
Generates and verifies 21 CFR Part 11-style audit trails — who/what/when, electronic signatures, and tamper-evidence — for OpenMed pipelines in GxP and clinical-trial (GCP) settings. Use when the user runs OpenMed in a regulated/validated environment and needs an attributable, time-stamped, tamper-evident record of…
generating-synthetic-surrogates
Replace detected PHI with realistic, type-matched fake values in OpenMed so clinical notes stay readable and parseable instead of full of [REDACTED] markers. Use when the user wants surrogate names, MRNs, addresses, or dates rather than opaque masks, needs consistent fake identities across a document, must keep notes…
shifting-clinical-dates
Apply consistent per-patient date shifting in OpenMed that preserves intervals between events while satisfying HIPAA Safe Harbor's date rule. Use when the user needs to de-identify dates but keep temporal structure for research, shift all dates by the same offset per patient, preserve days-between-events for survival…
fda-database
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.