nvidia-skill-finder
Use for NVIDIA-related requests where an NVIDIA skill might help, even if the user did not ask for a skill. Trigger on NVIDIA products, hardware, software,…
Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
$ npx -y skills add NVIDIA/skills --skill dicom-metadata-extract --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/dicom-metadata-extractContext preview
The summary Claude sees to decide when to auto-load this skill.
Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
name: dicom-metadata-extract
description: Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
license: Apache-2.0
allowed-tools: Bash
permissions: [file_read, file_write, shell]
metadata:
author: 'NVIDIA MedTech Team'
tags:
- MedTech
- DICOM
- metadata| Script | Purpose | Arguments | |---|---|---| | `scripts/extract_metadata.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_DICOM [--output OUT.json]` |
| Error | Cause | Fix | |---|---|---| | Missing dependency or import error | Runtime package drift from `skill_manifest.yaml`. | Install the packages declared in the manifest or use the documented setup command. | | Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. | | Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Reads one DICOM file with pydicom and emits JSON on stdout.
python scripts/extract_metadata.py PATH_TO_DICOM python scripts/extract_metadata.py PATH_TO_DICOM --output result.json
Output includes `transfer_syntax`, `modality`, grouped study/series/image metadata, `phi_present`, and `phi_tags_found`.
Use this as the smallest end-to-end example of a Medical AI Skills skill. Do not use it for anonymization, private-tag review, pixel PHI detection, or clinical interpretation.
For second-pass evidence review, generate a trusted run:
python -m eval_engine.run_trusted skills/dicom-metadata-extract \ --fixture skills/dicom-metadata-extract/fixtures/sample_ct.dcm \ --out runs/dicom_metadata_trusted
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