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Query and download NCI Imaging Data Commons (IDC) cancer radiology and pathology datasets via the idc-index Python client. No authentication required: the parquet index ships inside the pip wheel, SQL runs locally via DuckDB, and DICOM downloads stream from public S3/GCS buckets through s5cmd. Use sql_query() for DuckDB cohort selection, get_collections/get_patients/get_dicom_studies/get_dicom_series for hierarchical browsing, download_from_selection() for downloads, and get_viewer_URL() for OHI
Passed 22 of 22 test cases with the skill, and 10 of 22 without it.
and 13 more in the full report
12 fixed10 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
When calling client.get_patients('nsclc_radiomics'), what parameter and value must be passed to return the patient listing as a pandas DataFrame instead of a dictionary?
| Checked | Without skill | With skill |
|---|---|---|
| Sets parameter outputFormat='df' to return a pandas DataFrame. | Missed | Passed |
The instructions your model reads when the skill loads.
NCI Imaging Data Commons (IDC) is the largest public collection of cancer imaging data, hosting 175+ DICOM collections (CT, MR, PET, slide microscopy, segmentations, structured reports). The idc-index Python client ships the entire IDC metadata catalog as a parquet file bundled inside the pip wheel; IDCClient() loads it into DuckDB, so sql_query() runs locally with zero network calls.
Image downloads stream from public AWS S3 (default) or Google Cloud Storage buckets via the bundled s5cmd executable. No GCP/AWS credentials, no BigQuery billing, no service account JSON.
idc-index (>=0.12), pandas, pydicom (for reading DICOM files after download)series_size_MBs5cmd executable used for high-speed S3 transfers# Skip when already provisioned in a pixi or conda env
pip install idc-index pydicom
from idc_index import IDCClient
client = IDCClient()
print('IDC version:', client.get_idc_version())
print('Collections:', len(client.get_collections()))
print('First 5:', client.get_collections()[:5])
test cases passed, vs 10 / 22 without
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