Radiology Data

CommunityPopular

huang-sir1

radiology-data

Prepare and audit Data/Code Availability statements, DICOM de-identification plans, repository selection, dataset citations, and FAIR/sharing checks for Radiology (RSNA) and Nature-portfolio imaging+omics submissions. Use when the user needs a data availability statement, must de-identify DICOM imaging, choose a repository (TCIA, Zenodo, GEO, dbGaP, EGA), share code/models, write dataset citations, check FAIR compliance, or needs Extended Data / Supplementary Information / Source Data planning for a Nature-family journal — including controlled-access genomics for radiogenomics. Bilingual-aware (中文作者备注 → submission-ready English). Never overstates availability or invents accessions.

Overview

Publisher	huang-sir1
Repository	`radiology-skills`
Skill name	`radiology-data`
Stars	1.7K
Forks	17
Bundled files	4
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by huang-sir1 on GitHub. Read the source before you install it.

Installation

Install the Radiology Data AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/modules/radiology-data

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/huang-sir1/radiology-skills.git /tmp/radiology-skills
mkdir -p .claude/skills
cp -r /tmp/radiology-skills/radiology-skills/modules/radiology-data .claude/skills/radiology-data

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Radiology Data in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Radiology Data on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Radiology Data is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Workflow

Inventory result-supporting data: imaging, radiomic feature tables, clinical data, omics (bulk/scRNA/spatial), code, trained models.
For public-resource planning, open ai-radiogenomics-public-resources.md and mark each dataset as pretraining, development, internal test, external validation, or citation-only.
Classify each as public / depositable / restricted (privacy, consent, DUA, commercial).
De-identify imaging (dicom-deidentification.md); confirm no pixel PHI; deface head MRI/CT.
Pick repositories (repositories.md): images → TCIA/Zenodo; features/code → Zenodo/ GitHub (+ DOI); expression → GEO; controlled genomics → dbGaP/EGA.
Draft statements (availability-and-fair.md) with accessions/placeholders; write dataset citations; run the FAIR check.
Flag restrictions honestly: reason, controller, review route, conditions.

Output contract

Data inventory — item → sensitivity → access route → repository → accession/placeholder.
Data Availability statement and Code/Model Availability statement (submission-ready).
Dataset citations (DataCite-style) for any public data used.
Public-resource role map — dataset → role (pretraining/development/test/external validation) → overlap/leakage/access risks.
De-identification plan (if imaging is shared).
FAIR/issues — gaps and fixes; restricted-data wording.
Extended Data / Source Data plan (Nature-portfolio only) — which items are main-text, Extended Data, Supplementary Information, and confirmation that Source Data will be exported per figure.
待确认（中文） for Chinese authors — items needing author confirmation.

Handoffs

Reporting-guideline availability items, Reporting Summary → radiology-reporting; dataset discovery → radiology-search; controlled-cohort design → radiology-radiogenomics; figure-level Source Data / Extended Data figure count → radiology-figure/nature-figure-spec.md; display-item plan in the manuscript → radiology-writing. Not legal advice — confirm consent/DUA/IRB terms with your institution.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Radiology Data AI skill do?

Why use Radiology Data on TypingMind?

Because you install it once and use it with any model. Radiology Data is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Radiology Data in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huang-sir1/radiology-skills/tree/main/radiology-skills/modules/radiology-data. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Radiology Data?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Radiology Data?

As many as you like. As long as a model supports skills, you can use Radiology Data with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Radiology Data AI skill free?

Yes. It is published on GitHub by huang-sir1 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data & Code Availability + De-identification

Core stance

When to use

When to open extra files

Workflow

Output contract

Handoffs

Bundled files

Frequently asked questions

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