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Dicom Metadata Extract Agent Skill

Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.

10k tokens
context cost
the whole folder, loaded on every use
12
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
2778
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/NVIDIA/skills --skill dicom-metadata-extract

What comes with it

36 975 bytes besides the instruction
AGENTS.md
BENCHMARK.md
evals/baseline.yaml
evals/evals.json
fixtures/generate_sample.py
scripts/extract_metadata.py
skill-card.md
skill.oms.sig
skill_manifest.yaml
tests/test_basic.py
validators/output_schema.json

What it tells the agent to use

found in the instruction text
Bash runs shell commands — read the instruction before connecting

The instruction itself

7 sections, as written by the author

DICOM Metadata Extract

Purpose

  • Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
  • Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
  • Manifest I/O: inputs are dicom_path; outputs are metadata_json.

Instructions

  • Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
  • Run scripts/extract_metadata.py through the documented command below; keep outputs under a caller-provided run directory.
  • If a host agent exposes run_script, use run_script("scripts/extract_metadata.py", args=[...]); otherwise run the Bash/Python command shown below.
  • Check the emitted JSON and run medagent.verifiers.dicom_metadata_quality_v1 on evidence packs before treating the run as reviewed evidence.

Available Scripts

| Script | Purpose | Arguments |

|---|---|---|

| scripts/extract_metadata.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_DICOM [--output OUT.json] |

Prerequisites

  • Runtime requirements: Python packages listed in runtime.side_effects.pip_packages.
  • Run commands from the repository root unless an existing section below says otherwise.

Limitations

  • Small PS3.15-inspired standard-tag subset only; not a complete Basic Application Confidentiality Profile implementation.
  • Private tags not checked
  • Burnt-in pixel PHI not detected
  • Multi-frame handling minimal
  • Not for clinical deployment, regulatory de-identification, autonomous diagnosis, patient-facing use.

Troubleshooting

| 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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How to use it

Copy the folder

Take nvidia/dicom-metadata-extract from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.