imaging-data-commons

imaging-data-commons is a skill for Claude Code, Codex from Lord1Egypt/scientific-agent-toolkit. It costs 62 tokens per session (8,604 once invoked), scanned A, a copy of imaging-data-commons, MIT.

A way to query and download public cancer scans and pathology data from the National Cancer Institute's Imaging Data Commons, a large medical-imaging repository.

In plain words
What is it for?
Use it to search the collection with the idc-index Python package, inspect metadata, check the data version and licenses, download studies, and view them in a browser.
Why use it?
It removes the need to manually locate and retrieve imaging records for AI training or research.

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 search the collection with the idc-index Python package, inspect metadata, check the data version and licenses, download studies, and view them in a browser.

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/Lord1Egypt/scientific-agent-toolkit
agentmods
npx agentmods add skills/lord1egypt/scientific-agent-toolkit/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/lord1egypt/scientific-agent-toolkit/imaging-data-commons/github.svg)](https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/imaging-data-commons)
Your own site
<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/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/lord1egypt/scientific-agent-toolkit/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/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 8,604 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 $0.08604
Opus 5 $0.00031 $0.04302
Sonnet 5 $0.00012 $0.01721
Haiku 4.5 $0.00006 $0.00860

Measured 8d ago against content hash 3401dc7e9bb9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 8d 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 — 31 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.

scientific-skills/imaging-data-commons/SKILL.md · 863 lines

How it starts

The opening of the file, as written. The whole thing — 863 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 · 863 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. 8d ago First seen · 863 lines · 62 tokens per session scan A 3401dc7e9bb9

Subscribe to this mod's changes

imaging-data-commons is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (2 stars, last pushed 3mo ago), licensed MIT. It adds 62 tokens to every session and 8,604 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 31 lines, and is treated as a copy.

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