Official agent skill

Dicom Metadata Extract

Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence.

OfficialApache-2.0Auto-check: notesResearch & Science

Install Dicom Metadata Extract

skills CLI

$ npx skills add NVIDIA/skills --skill dicom-metadata-extract -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills dicom-metadata-extract --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dicom-metadata-extract .claude/skills/dicom-metadata-extract && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "dicom-metadata-extract" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract into .claude/skills/dicom-metadata-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-metadata-extract", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill dicom-metadata-extract -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills dicom-metadata-extract --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dicom-metadata-extract .agents/skills/dicom-metadata-extract && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "dicom-metadata-extract" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract into .agents/skills/dicom-metadata-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-metadata-extract", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill dicom-metadata-extract -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills dicom-metadata-extract --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dicom-metadata-extract .cursor/skills/dicom-metadata-extract && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "dicom-metadata-extract" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract into .cursor/skills/dicom-metadata-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-metadata-extract", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/dicom-metadata-extract

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill dicom-metadata-extract -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills dicom-metadata-extract --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dicom-metadata-extract .gemini/skills/dicom-metadata-extract && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "dicom-metadata-extract" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract into .gemini/skills/dicom-metadata-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-metadata-extract", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills dicom-metadata-extract

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill dicom-metadata-extract -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dicom-metadata-extract .github/skills/dicom-metadata-extract && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "dicom-metadata-extract" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract into .github/skills/dicom-metadata-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-metadata-extract", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill dicom-metadata-extract -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills dicom-metadata-extract --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dicom-metadata-extract .opencode/skills/dicom-metadata-extract && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "dicom-metadata-extract" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract into .opencode/skills/dicom-metadata-extract/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dicom-metadata-extract", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: dicom-metadata-extract
GitHub stars: 3.5k
Token cost: ~821 tokens
SKILL.md length: 309 words
Files: 12 (incl. scripts)
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence.

Tasks that involve Clinical and healthcare research
SKILL.md covers Purpose, Instructions, Available Scripts and Prerequisites, plus 2 more sections
Runs Python scripts from its folder; calls python

What it does

Dicom Metadata Extract is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.

Its SKILL.md is about 820 tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including scripts (for example `AGENTS.md`, `BENCHMARK.md` and `evals/baseline.yaml`).

It sits in Research & Science, covering Clinical and healthcare research. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Tasks that involve Clinical and healthcare research

Example prompts

“/dicom-metadata-extract”

Requirements

Python 3
Pre-approved tools (allowed-tools): Bash

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Bash
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dicom Metadata Extract loads about 821 tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 309 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~40
When it runs · the whole SKILL.md, loaded when a task matches: ~821

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
```
allowed-tools: Bash
```

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 309 words, ~821 tokens.

Download SKILL.mdSave it as .claude/skills/dicom-metadata-extract/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.

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.
allowed-tools: Bash
license: Apache-2.0
permissions: file_read, file_write, shell
metadata.author: NVIDIA MedTech Team
metadata.tags: MedTech, DICOM, metadata

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.

bash

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:

bash

python -m eval_engine.run_trusted skills/dicom-metadata-extract \
  --fixture skills/dicom-metadata-extract/fixtures/sample_ct.dcm \
  --out runs/dicom_metadata_trusted

Files

SKILL.md and 11 other files (scripts) in skills/dicom-metadata-extract of NVIDIA/skills.

SKILL.md
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

Open the folder on GitHubat commit 0e0d506

Compare with similar skills

Dicom Metadata Extract next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dicom Metadata Extract compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Dicom Metadata Extract this skillNVIDIA/skills	3.5k	—	~821	Automated safety check: Notes	Apache-2.0	today
Clinical Trials Databasegoogle-deepmind/science-skills	3.2k	2 repos	~3.2k	Automated safety check: Pass	Apache-2.0	22 days ago
CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw	617	1 repos	~1.8k	Automated safety check: Pass	None	6 mo ago
Model AssessmentAperivue/medsci-skills	329	1 repos	~4.5k	Automated safety check: Pass	MIT	2 days ago
Biomedical Analysis Dispatchxjtulyc/MedgeClaw	617	1 repos	~2k	Automated safety check: Pass	None	6 mo ago
Research Proposalluwill/research-skills	857	—	~4.4k	Automated safety check: Notes	None	7 days ago

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Questions about Dicom Metadata Extract

What does Dicom Metadata Extract do?

Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Dicom Metadata Extract is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence.

When should I use Dicom Metadata Extract?

Dicom Metadata Extract fits situations like: tasks that involve Clinical and healthcare research.

How do I install Dicom Metadata Extract in Claude Code?

Run `npx skills add NVIDIA/skills --skill dicom-metadata-extract -a claude-code`. Or copy the skill folder (skills/dicom-metadata-extract in NVIDIA/skills) into .claude/skills/dicom-metadata-extract in your project. Claude Code loads it when a task matches its description.

How do I install Dicom Metadata Extract in Codex?

Run `npx skills add NVIDIA/skills --skill dicom-metadata-extract -a codex`. Or copy the skill folder (skills/dicom-metadata-extract in NVIDIA/skills) into .agents/skills/dicom-metadata-extract in your project. Codex loads it when a task matches its description.

Can I use Dicom Metadata Extract in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill dicom-metadata-extract -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dicom-metadata-extract, .gemini/skills/dicom-metadata-extract, .github/skills/dicom-metadata-extract and .opencode/skills/dicom-metadata-extract in your project.

What does Dicom Metadata Extract need to run?

Going by SKILL.md and its folder, Dicom Metadata Extract needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash.

Does Dicom Metadata Extract access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Dicom Metadata Extract safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Dicom Metadata Extract use?

Dicom Metadata Extract is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dicom Metadata Extract use?

About 821 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Dicom Metadata Extract?

Skills that share tags, products or a category with Dicom Metadata Extract: Clinical Trials Database (google-deepmind/science-skills, 3.2k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Model Assessment (Aperivue/medsci-skills, 329 stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dicom Metadata Extract?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Dicom Metadata Extract

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

DICOM Metadata Extract

Purpose

Instructions

Available Scripts

Prerequisites

Limitations

Troubleshooting

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Dicom Metadata Extract