radiology-dataset-guide

radiology-dataset-guide is a skill for Claude Code, Codex from aizech/clinical-skills. It costs 56 tokens per session (573 once invoked), scanned A, original, MIT.

Guidance for choosing, accessing, and using medical imaging datasets for AI research and development. It explains datasets, imaging types, tasks, annotations, and access restrictions.

In plain words
What is it for?
Use it to compare datasets for detection, segmentation, classification, reconstruction, or measurement tasks, including resources such as RSNA, MIMIC, CheXpert, and NIH datasets.
Why use it?
It helps developers find data that matches their AI problem while considering whether the data can be used for their intended purpose.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to compare datasets for detection, segmentation, classification, reconstruction, or measurement tasks, including resources such as RSNA, MIMIC, CheXpert, and NIH datasets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aizech/clinical-skills/radiology-dataset-guide
Install

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.

Any agent
npx skills add aizech/clinical-skills --skill radiology-dataset-guide
Clone the repo
git clone --depth 1 https://github.com/aizech/clinical-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for radiology-dataset-guide

README.md
[![agentmods](https://agentmods.dev/badge/skills/aizech/clinical-skills/radiology-dataset-guide/github.svg)](https://agentmods.dev/skills/aizech/clinical-skills/radiology-dataset-guide)
Your own site
<a href="https://agentmods.dev/skills/aizech/clinical-skills/radiology-dataset-guide"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/radiology-dataset-guide/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.

agentmods 80×15 button for radiology-dataset-guide

Your own site · 80×15
<a href="https://agentmods.dev/skills/aizech/clinical-skills/radiology-dataset-guide"><img src="https://agentmods.dev/badge/skills/aizech/clinical-skills/radiology-dataset-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 573 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00056 $0.00573
Opus 5 $0.00028 $0.00287
Sonnet 5 $0.00011 $0.00115
Haiku 4.5 $0.00006 $0.00057

Measured 11d ago against content hash a3a2bf23eb01, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

radiology-dataset-guide 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 11d 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.

.agents/skills/radiology-dataset-guide/SKILL.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Radiology Dataset Guide Skill

Triggers

  • "radiology dataset"
  • "medical imaging data"
  • "RSNA challenge"
  • "MIMIC data access"
  • "CheXpert download"
  • "dataset comparison"
  • "training data preparation"
  • "public dataset"

Parameters

  • task_type (required): ML/AI task being solved
    • detection - Abnormality/nodule/cancer detection
    • segmentation - Organ or lesion segmentation
    • classification - Disease or finding classification
    • reconstruction - Image reconstruction/enhancement
    • quantification - Measurement and feature extraction
  • anatomy (optional): Body region or organ system
  • modality (optional): Imaging modality preference
  • access_requirements (optional): Data use restrictions
  • commercial_use (optional): Boolean for commercial application intent

Dataset Inventory

Dataset Modality Primary Task Access Annotations
RSNA Bone Age X-ray Regression Public Age, quality
RSNA Pneumonia Chest X-ray Detection Public Bounding boxes
RSNA Brain Hemorrhage CT Detection Public Bounding boxes, type
NIH ChestX-ray14 Chest X-ray Classification Public Labels
CheXpert Chest X-ray Classification Institutional Labels
MIMIC-CXR Chest X-ray Multi PhysioNet Labels, reports
CheXphoto Chest X-ray Classification Public Synth/real pairs
LUNA16 CT Detection Public Nodule centers
KiTS CT Segmentation Public Kidney/tumor
BraTS MRI Segmentation Research Multi-modal seg
PANDA Histology Classification Public Biopsy grades
OBJ-CXR Chest X-ray Detection Public Bounding boxes

Output Format

Returns structured JSON with:

  • Relevant datasets ranked by suitability
  • Annotation quality and completeness
  • Access procedure and requirements
  • Key publications and benchmarks
  • Preprocessing recommendations
  • Compliance and ethics considerations

Read the full file on GitHub · 70 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 11d ago First seen · 70 lines · 56 tokens per session scan A a3a2bf23eb01

Subscribe to this mod's changes

radiology-dataset-guide is a skill published in the GitHub repository aizech/clinical-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 573 once invoked, about $0.0003 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 2026-08-31.

Related

Other skills, from other repositories

flux-analyzer

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

aiming-lab/AutoResearchClaw · 44 tokens

fba-simulator

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

aiming-lab/AutoResearchClaw · 69 tokens

gsmm-validator

Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

aiming-lab/AutoResearchClaw · 52 tokens

metabolic-study-planner

Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is…

aiming-lab/AutoResearchClaw · 69 tokens

gsmm-builder

Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

aiming-lab/AutoResearchClaw · 45 tokens

stat-research-orchestrator

Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

aiming-lab/AutoResearchClaw · 36 tokens