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.
git clone --depth 1 https://github.com/x-cmd/skillnpx agentmods add skills/x-cmd/skill/imaging-data-commonsWrote 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/x-cmd/skill/imaging-data-commons)<a href="https://agentmods.dev/skills/x-cmd/skill/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/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.
<a href="https://agentmods.dev/skills/x-cmd/skill/imaging-data-commons"><img src="https://agentmods.dev/badge/skills/x-cmd/skill/imaging-data-commons.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00062 | $0.09708 |
| Opus 5.5 | $0.00025 | $0.03883 |
| Sonnet 5.5 | $0.00012 | $0.01942 |
| Haiku 4.5 | $0.00006 | $0.00971 |
Grade A, and why
imaging-data-commons 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 1mo 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.
This is a copy
92% identical to imaging-data-commons — 39 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.
How it starts
The opening of the file, as written. The whole thing — 1,151 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.
Primary tool: idc-index (GitHub)
Check current data scale for the latest version:
from idc_index import IDCClient
client = IDCClient()
# get IDC data version
print(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:
- Query metadata →
client.sql_query() - Download DICOM files →
client.download_from_selection() - 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
IDC Data Model
IDC adds two grouping levels above the standard DICOM hierarchy (Patient → Study → Series → Instance):
- collection_id: Groups patients by disease, modality, or research focus (e.g.,
tcga_luad,nlst). A patient belongs to exactly one collection. - analysis_result_id: Identifies derived objects (segmentations, annotations, radiomics features) across one or more original collections.
Use collection_id to find original imaging data, may include annotations deposited along with the images; use analysis_result_id to find AI-generated or expert annotations.
What ships with it
2 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.
- 1mo ago First seen · 1,151 lines · 62 tokens per session scan A ce4e7699f347
imaging-data-commons is a skill published in the GitHub repository x-cmd/skill (26 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 9,708 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to imaging-data-commons, differing in 39 lines, and is treated as a copy.
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