imaging-data-commons
The imaging-data-commons skill enables querying and downloading public cancer imaging datasets from the National Cancer Institute Imaging Data Commons using the idc-index Python package, supporting radiology modalities like CT, MR, and PET along with pathology data. Use this skill when accessing large-scale medical imaging data for AI model training, research analysis, or visualization without requiring authentication, and when filtering datasets by cancer type, anatomical site, or other metadata criteria.
git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills /tmp/imaging-data-commons && cp -r /tmp/imaging-data-commons/skills/imaging-data-commons ~/.claude/skills/imaging-data-commonsSKILL.md
# Imaging Data Commons
## Overview
Query and download public cancer imaging data from the National Cancer Institute Imaging Data Commons (IDC). No authentication required for data access.
**Expected network access:** IDC metadata is reachable three ways — a local DuckDB index shipped with the `idc-index` Python package (no network), or the hosted IDC service over MCP or REST (`api.imaging.datacommons.cancer.gov`, no authentication). File downloads use public GCS (`storage.googleapis.com`) and AWS S3 (`s3.amazonaws.com`) — no authentication required. DICOMweb access uses either the public IDC proxy (`proxy.imaging.datacommons.cancer.gov`, no auth) or the Google Cloud Healthcare API (`healthcare.googleapis.com`, requires GCP authentication). Optional BigQuery queries (`bigquery.googleapis.com`) also require GCP authentication. No credentials or environment variables are accessed by this skill.
**Current IDC Data Version: v24** (always verify — see *Best Practices*)
**Choose the access path first.** There is no single default: the cheapest correct path depends
on the session and the task.
1. **Session already has the IDC MCP server?** Route discovery and metadata there — see *IDC
MCP Server*.
2. **Otherwise, is `idc-index` installed?** Run `python scripts/check_version.py`. If it passes,
use `idc-index` for everything.
3. **Not installed, and the task is read-only metadata** — counts, attribute values, collection
lookups, SQL under 10 000 rows, licenses, citations, viewer URLs? **Use the REST API over
`curl`; do not install anything.** Installing costs ~77 MB of packaged index data plus
pandas, pyarrow, and duckdb, which a metadata question does not need. See *Data Access
Options*.
4. **Not installed, and the task needs more than metadata** — downloading files, pandas or
plotting, pydicom/SimpleITK, pathology tiling, results past 10 000 rows, or a version-pinned
script the user re-runs? Install `idc-index`: `check_version.py` exits non-zero and prints
the exact install command for the running interpreter. Prefer a virtual environment, then
restart Python.
`idc-index` ([GitHub](https://github.com/imagingdatacommons/idc-index)) is still the most
capable path and the only one that moves image bytes; the rule is just not to pay for it before
the task calls for it. `check_version.py` never installs anything itself — it also flags a newer
`idc-index` or skill release when one exists.
**Setup for the `idc-index` path:**
```python
from idc_index import IDCClient
client = IDCClient()
# Verify IDC data version (should be "v24")
print(f"IDC data version: {client.get_idc_version()}")
```
**Core workflow:** query metadata with `client.sql_query()` → download with
`client.download_from_selection()` → visualize with `client.get_viewer_URL()`. Python examples
below assume this `client`; *Data Access Options* has the REST equivalents. For current data
scale, run the summary query in `references/sql_patterns.md` or `GET /v3/stats`.
## IDC MCP Server
IDC operates a hosted MCP server at `https://api.imaging.datacommons.cancer.gov/mcp`
(streamable HTTP, no authentication). Where it is available it complements — it does not
replace — the `idc-index` workflow below.
**Identify it** by the MCP resource `idc://guide`, or by three or more of the tool names
`build_cohort`, `get_cohort_urls`, `list_analysis_results`, and `get_idc_version`. Generic
names such as `run_sql` are not evidence on their own. If identification is ambiguous, use
`idc-index`.
**If this session has the server**, treat it as authoritative for discovery and metadata —
IDC version, counts, attribute values, cohort building, metadata SQL — and follow the
server's own instructions rather than re-deriving them from this file. Its data version is
whatever the server reports: call `get_idc_version` instead of relying on the version pinned
in this file.
Return here for what the server does not do: downloading files, local pandas/notebook
analysis, DICOMweb, BigQuery, digital pathology tiling, and reproducible scripts. Hand off by
passing SeriesInstanceUIDs from the server to `client.download_from_selection(...)`, and run
`scripts/check_version.py` at that point.
**If it is not available**, the identical service is reachable with no configuration as a REST
API at `https://api.imaging.datacommons.cancer.gov/v3` — use it for read-only metadata rather
than installing `idc-index`, per the routing gate in *Overview*. Suggest connecting the MCP
server at most once, only for repeated interactive discovery, and never change the user's
configuration yourself.
See `references/mcp_guide.md` for the tool inventory, handoff patterns, and per-host notes.
## 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
## Quick Navigation
Inline below: the MCP/REST routing rules, the IDC data model, the index tables and how they
join, the core API patterns (query, download, visualize, license, cite), best practices, and
troubleshooting.
**Reference Guides (load on demand):**
| Guide | When to Load |
|-------|--------------|
| `index_tables_guide.md` | Complex JOINs, schema discovery, DataFrame access |
| `use_cases.md` | End-to-end workflows: training datasets, batch downloads, DICOM reading with pydicom/SimpleITK, pipeline integration |
| `sql_patterns.md` | Quick SQL patterns for filter discovery, annotations, size estimation |
| `clinical_data_guide.md` | Clinical/tabular data, imaging+clinical joins, value mapping |
| `licensing_and_citation.md` | Commercial-use questions, mixed-license cohorts, citation formats |
| `cloud_storage_guide.md` | Direct S3/GCS access, versioning, UUID mapping |How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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