imaging-data-commons

imaging-data-commons is a skill for Claude Code, Codex from yanjumlinnb-boop/scientific-agent-skills. It costs 62 tokens per session (8,617 once invoked), scanned A, a copy of imaging-data-commons, MIT.

A way to access public cancer scans and pathology data from the National Cancer Institute's Imaging Data Commons, a collection of medical imaging research data. It uses Python to search, inspect, and download datasets without authentication.

In plain words
What is it for?
Use it to find and download CT, MRI, PET, and pathology datasets for medical-imaging research or AI training, while checking versions and licenses.
Why use it?
It removes the need to manually browse a large medical-data collection and helps researchers work from consistent metadata and downloads.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is # Results in: ./data/tcga_luad/TCGA-05-4244/CT/.

Good fit Use it to find and download CT, MRI, PET, and pathology datasets for medical-imaging research or AI training, while checking versions and licenses.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/yanjumlinnb-boop/scientific-agent-skills
agentmods
npx agentmods add skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons

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 imaging-data-commons

README.md
[![agentmods](https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons/github.svg)](https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons)
Your own site
<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons/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 imaging-data-commons

Your own site · 80×15
<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/imaging-data-commons.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,617 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 91% copy Near-identical to another mod 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 $0.00062 $0.08617
Opus 5 $0.00031 $0.04308
Sonnet 5 $0.00012 $0.01723
Haiku 4.5 $0.00006 $0.00862

Measured 11d ago against content hash 01bf3245ce5c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

imaging-data-commons scanned grade A with 1 finding 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(["pip3", "install", "--upgrade", "--break-system-packages", "idc-index"], check=True)
Origin

This is a copy

91% identical to imaging-data-commons — 34 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/imaging-data-commons/SKILL.md · 858 lines

How it starts

The opening of the file, as written. The whole thing — 858 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Imaging Data Commons

Overview

Use the idc-index Python package to query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access.

Current IDC Data Version: v23 (always verify with IDCClient().get_idc_version())

Primary tool: idc-index (GitHub)

CRITICAL - Check package version and upgrade if needed (run this FIRST):

import idc_index

REQUIRED_VERSION = "0.11.14"  # Must match metadata.idc-index in this file
installed = idc_index.__version__

if installed < REQUIRED_VERSION:
    print(f"Upgrading idc-index from {installed} to {REQUIRED_VERSION}...")
    import subprocess
    subprocess.run(["pip3", "install", "--upgrade", "--break-system-packages", "idc-index"], check=True)
    print("Upgrade complete. Restart Python to use new version.")
else:
    print(f"idc-index {installed} meets requirement ({REQUIRED_VERSION})")

Verify IDC data version and check current data scale:

from idc_index import IDCClient
client = IDCClient()

# Verify IDC data version (should be "v23")
print(f"IDC data version: {client.get_idc_version()}")

# Get collection count and total series
stats = client.sql_query("""
    SELECT
        COUNT(DISTINCT collection_id) as collections,
        COUNT(DISTINCT analysis_result_id) as analysis_results,
        COUNT(DISTINCT PatientID) as patients,
        COUNT(DISTINCT StudyInstanceUID) as studies,
        COUNT(DISTINCT SeriesInstanceUID) as series,
        SUM(instanceCount) as instances,
        SUM(series_size_MB)/1000000 as size_TB
    FROM index
""")
print(stats)

Core workflow:

  1. Query metadata → client.sql_query()
  2. Download DICOM files → client.download_from_selection()
  3. Visualize in browser → client.get_viewer_URL(seriesInstanceUID=...)

When to Use This Skill

  • Finding publicly available radiology (CT, MR, PET) or pathology (slide microscopy) images
  • Selecting image subsets by cancer type, modality, anatomical site, or other metadata
  • Downloading DICOM data from IDC
  • Checking data licenses before use in research or commercial applications
  • Visualizing medical images in a browser without local DICOM viewer software

Read the full file on GitHub · 858 lines

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 · 858 lines · 62 tokens per session scan A 01bf3245ce5c

Subscribe to this mod's changes

imaging-data-commons is a skill published in the GitHub repository yanjumlinnb-boop/scientific-agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 62 tokens to every session and 8,617 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 91% identical to imaging-data-commons, differing in 34 lines, and is treated as a copy.

Related

Other skills, from other repositories

torchdrug

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.

K-Dense-AI/scientific-agent-skills · 61 tokens

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…

K-Dense-AI/scientific-agent-skills · 66 tokens

pyhealth

Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…

K-Dense-AI/scientific-agent-skills · 216 tokens

deepspot-m

Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…

K-Dense-AI/scientific-agent-skills · 80 tokens

nemo-mbridge-perf-expert-parallel-overlap

Validate and use MoE expert-parallel communication overlap in Megatron-Bridge, including overlapmoeexpertparallelcomm, delaywgradcompute, and flex dispatcher backends such as DeepEP and HybridEP.

NVIDIA/skills · 56 tokens

pick-a-pii-model

Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.

maziyarpanahi/openmed · 64 tokens