Agent skill

dicom-metadata-extract

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

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexcan modify filesships scriptsApache-2.0
Install
npx skills add NVIDIA/skills --skill dicom-metadata-extract --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 12
SKILL.md size: 3 KB
Bundled scripts: yes
Declared author: NVIDIA MedTech Team
Allowed tools: Bash
Path: skills/dicom-metadata-extract/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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.y

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About this skill
What does the dicom-metadata-extract skill do?

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

How do I install it?

Run `npx skills add NVIDIA/skills --skill dicom-metadata-extract --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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