Official agent skill

Nv Segment Ct

Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

OfficialApache-2.0Auto-check: notesAI & LLM Engineering

Install Nv Segment Ct

skills CLI

$ npx skills add NVIDIA/skills --skill nv-segment-ct -a claude-code

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

GitHub CLI

$ gh skill install NVIDIA/skills nv-segment-ct --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/nv-segment-ct .claude/skills/nv-segment-ct && 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 "nv-segment-ct" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nv-segment-ct into .claude/skills/nv-segment-ct/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nv-segment-ct", 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/nv-segment-ct

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 nv-segment-ct -a codex

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

GitHub CLI

$ gh skill install NVIDIA/skills nv-segment-ct --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/nv-segment-ct .agents/skills/nv-segment-ct && 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 "nv-segment-ct" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nv-segment-ct into .agents/skills/nv-segment-ct/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nv-segment-ct", 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 nv-segment-ct -a cursor

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

GitHub CLI

$ gh skill install NVIDIA/skills nv-segment-ct --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/nv-segment-ct .cursor/skills/nv-segment-ct && 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 "nv-segment-ct" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nv-segment-ct into .cursor/skills/nv-segment-ct/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nv-segment-ct", 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/nv-segment-ct

--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 nv-segment-ct -a gemini-cli

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

GitHub CLI

$ gh skill install NVIDIA/skills nv-segment-ct --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/nv-segment-ct .gemini/skills/nv-segment-ct && 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 "nv-segment-ct" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nv-segment-ct into .gemini/skills/nv-segment-ct/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nv-segment-ct", 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 nv-segment-ct

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 nv-segment-ct -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/nv-segment-ct .github/skills/nv-segment-ct && 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 "nv-segment-ct" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nv-segment-ct into .github/skills/nv-segment-ct/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nv-segment-ct", 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 nv-segment-ct -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 nv-segment-ct --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/nv-segment-ct .opencode/skills/nv-segment-ct && 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 "nv-segment-ct" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nv-segment-ct into .opencode/skills/nv-segment-ct/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nv-segment-ct", 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: nv-segment-ct
GitHub stars: 3.5k
Token cost: ~2.1k tokens
SKILL.md length: 871 words
Files: 11 (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 running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

AI & LLM Engineering work in your project
SKILL.md covers Purpose, Instructions, Available Scripts and Prerequisites, plus 5 more sections
Runs Python scripts from its folder; calls python and hf; reaches raw.githubusercontent.com and huggingface.co

What it does

Nv Segment Ct is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 15 other files, including scripts (for example `BENCHMARK.md`, `evals/evals.json` and `fixtures/fetch_spleen_fixture.py`).

It sits in AI & LLM Engineering. It works with NVIDIA AI Platform, CUDA and Python. 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

AI & LLM Engineering work in your project

Example prompts

“/nv-segment-ct”

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
- hf
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- raw.githubusercontent.com
- huggingface.co
- msd-for-monai.s3-us-west-2.amazonaws.com
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

Nv Segment Ct loads about 2.1k tokens when it runs. Until then it costs about 27 tokens; SKILL.md has 871 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~27
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k

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). 871 words, ~2,090 tokens.

Download SKILL.mdSave it as .claude/skills/nv-segment-ct/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.

name: nv-segment-ct
description: Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.
allowed-tools: Bash
license: Apache-2.0
metadata.author: NVIDIA MedTech Team
metadata.tags: MedTech, CT, segmentation

NV-Segment-CT

Purpose

Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence. Not for clinical interpretation.
Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
Manifest I/O: inputs are ct_volume; outputs are label_map and result_json.

Instructions

Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
Run scripts/run_vista3d.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/run_vista3d.py", args=[...]); otherwise run the Bash/Python command shown below.
Create the documented Python 3.10 virtual environment and invoke its binaries directly; do not install model dependencies into the caller's active environment.
Check the emitted JSON and paired verifier guidance before treating the run as evidence.

Available Scripts

Script	Purpose	Arguments
`scripts/run_vista3d.py`	Primary entrypoint declared by skill_manifest.yaml.	`PATH_TO_CT.nii.gz [--output-dir OUT_DIR] [--label-prompts IDS]`

Prerequisites

Runtime requirements: Python 3.10 with venv support and GPU/CUDA when declared by the manifest. Model packages come from the pinned upstream requirements file; only wrapper-specific packages are added locally.
Side effects: creates an isolated environment under ~/.cache/nvidia-skills/venvs/nv-segment-ct-f9f5f51/, writes the downloaded bundle under skills/nv-segment-ct/bundle/, may cache model assets under ~/.cache/huggingface/, and may contact https://huggingface.co and https://raw.githubusercontent.com during first setup; the optional spleen fixture fetcher downloads MSD09 from https://msd-for-monai.s3-us-west-2.amazonaws.com.
Run commands from the repository root unless an existing section below says otherwise.

Limitations

This is a thin wrapper. Inference, preprocessing, and postprocessing are delegated entirely to the official hugging_face_pipeline.HuggingFacePipelineHelper in bundle/. Do not modify code under bundle/.
transformers==4.46.3 is the wrapper compatibility overlay tested with the upstream requirements' Torch 2.0.1; newer Transformers releases can disable that older Torch backend.
The pinned upstream requirements include Torch 2.0.1. Use only the pinned NVIDIA model assets; do not load untrusted checkpoints in this legacy reproduction environment.
Device auto-detected (cuda if available, else cpu); --device flag overrides.
Output may be schema-valid but semantically empty (e.g. label prompts that do not match the input anatomy). Sanity gates assert at least one foreground voxel per requested anatomy.
Not for clinical deployment, clinical interpretation, autonomous diagnosis, regulatory submission.

Troubleshooting

Error	Cause	Fix
`ensurepip is not available` while creating the environment	The host Python installation omitted its OS `venv` package.	Install the matching Python 3.10 venv support package or create the same isolated environment with `virtualenv -p python3.10`.
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.

Wraps the upstream nvidia/NV-Segment-CT helper. The wrapper does not reimplement VISTA3D inference.

Exact Runnable Surface

For CT segmentation user runs, use this repo-root wrapper path exactly:

bash

"$NV_SEGMENT_CT_VENV/bin/python" skills/nv-segment-ct/scripts/run_vista3d.py PATH_TO_CT.nii.gz --label-prompts "1,3,5,14" --output-dir OUT_DIR

Do not invent infer.py, Medical AI Skills run, python -m nv_segment_ct, or anatomy-name-only flags. For spleen, liver, right kidney, and left kidney, the required VISTA3D label IDs are exactly 1,3,5,14.

Show full SKILL.md (366 more words)Show less

Preconditions

The skill assumes a Python 3.10 interpreter with venv support. Its documented command creates a dedicated environment and installs the model dependencies from NV-Segment-CT/requirements.txt at the immutable NVIDIA-Medtech commit f9f5f51b589e5dc9c23c453cf5138398e4084056. The Hugging Face bundle itself does not ship a requirements.txt.

Two one-time downloads (the documented command does the first one; the fixture fetch is a separate step you run when bootstrapping):

bash

# Spleen example fixture from Decathlon MSD09 (~1.5 GB tar, ~11 MB
# fixture extracted into skills/nv-segment-ct/fixtures/spleen_03.nii.gz):
python skills/nv-segment-ct/fixtures/fetch_spleen_fixture.py

Both downloads (the bundle below, and the fixture) are gitignored (Medical AI Skills policy: no medical data or model weights in git). The fetch script is idempotent and caches the tar under .workbench_data/datasets/ so re-runs are no-ops.

Runtime needs an NVIDIA GPU with CUDA. CPU fallback is supported but slow.

Usage

From the skills repository root, run the complete bootstrap. Invoke the virtual environment's binaries directly so the caller's active environment is not modified:

bash

export NV_SEGMENT_CT_VENV="${NV_SEGMENT_CT_VENV:-$HOME/.cache/nvidia-skills/venvs/nv-segment-ct-f9f5f51}"
export NV_SEGMENT_CT_REQUIREMENTS="${NV_SEGMENT_CT_REQUIREMENTS:-https://raw.githubusercontent.com/NVIDIA-Medtech/NV-Segment-CTMR/f9f5f51b589e5dc9c23c453cf5138398e4084056/NV-Segment-CT/requirements.txt}"

if [ ! -x "$NV_SEGMENT_CT_VENV/bin/python" ]; then
  python3.10 -m venv "$NV_SEGMENT_CT_VENV"
fi

"$NV_SEGMENT_CT_VENV/bin/python" -m pip install \
  -r "$NV_SEGMENT_CT_REQUIREMENTS" \
  "transformers==4.46.3" \
  "typer>=0.9"

"$NV_SEGMENT_CT_VENV/bin/hf" download nvidia/NV-Segment-CT \
  --revision afb51518689f71e6abb367ee6301b2cd0225c66a \
  --local-dir skills/nv-segment-ct/bundle/

"$NV_SEGMENT_CT_VENV/bin/python" skills/nv-segment-ct/scripts/run_vista3d.py PATH_TO_CT.nii.gz \
  --label-prompts "1,3,5,14" \
  --output-dir vista3d_outputs

When the user names anatomies, translate them to VISTA3D class IDs before running. For the common abdominal CT request:

Anatomy	VISTA3D class ID
liver	1
spleen	3
right kidney	5
left kidney	14

For "segment the spleen, liver, right kidney, and left kidney", the correct --label-prompts value is exactly "1,3,5,14". Do not substitute kidney IDs from another label dictionary; the wrapper validates the requested label set and will mark the run invalid if the emitted mask contains labels outside the requested set.

The install and download steps are load-bearing. The pinned upstream file owns the model environment, while Transformers and Typer support this thin wrapper. hf download pulls the ~832 MB model bundle into skills/nv-segment-ct/bundle/; subsequent calls reuse the caches.

label-prompts are VISTA3D class IDs. The evidence output records input geometry, output mask path, observed label IDs, unexpected labels, per-class voxel counts, per-class physical volumes computed from the output mask header spacing, runtime, model identity, and fixed code-derived artifact checks such as mask shape, affine match, label set, foreground count, and class-volume bounds.

Pass --ground-truth PATH to record a reference label-map path under input.ground_truth_path. The skill does not compute Dice; that is the paired verifier's job.

Anatomy plausibility (per-class volume bounds, fragmentation, bilateral symmetry, liver larger than spleen) and optional per-class Dice/IoU against the recorded ground truth are checked by verifiers/ct_segmentation_quality_v1.

Not for clinical interpretation, production deployment, or non-CT modalities.

Files

SKILL.md and 10 other files (scripts) in skills/nv-segment-ct of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
fixtures/fetch_spleen_fixture.py
fixtures/generate_preflight_fixture.py
scripts/run_vista3d.py
skill-card.md
skill.oms.sig
skill_manifest.yaml
tests/test_run_vista3d.py
validators/output_schema.json

Open the folder on GitHubat commit 0e0d506

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Triton SkillslowlyC/agent-gpu-skills	169	—	~1.3k	Automated safety check: Pass	MIT	2 mo ago
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Works with

Questions about Nv Segment Ct

What does Nv Segment Ct do?

Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence. Nv Segment Ct is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Used for running NV-Segment-CT VISTA3D on CT NIfTI volumes and recording label-map evidence.

When should I use Nv Segment Ct?

Nv Segment Ct fits situations like: AI & LLM Engineering work in your project.

How do I install Nv Segment Ct in Claude Code?

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

How do I install Nv Segment Ct in Codex?

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

Can I use Nv Segment Ct 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 nv-segment-ct -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nv-segment-ct, .gemini/skills/nv-segment-ct, .github/skills/nv-segment-ct and .opencode/skills/nv-segment-ct in your project.

What does Nv Segment Ct need to run?

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

Does Nv Segment Ct access the network?

SKILL.md names 3 domains. In commands or code: raw.githubusercontent.com, huggingface.co and msd-for-monai.s3-us-west-2.amazonaws.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Nv Segment Ct 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 Nv Segment Ct use?

Nv Segment Ct 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 Nv Segment Ct use?

About 2.1k tokens (SKILL.md is roughly 8.4k 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 Nv Segment Ct?

Skills that share tags, products or a category with Nv Segment Ct: Optimize Op (CVCUDA/CV-CUDA, 2.7k stars), Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars), Triton Skill (slowlyC/agent-gpu-skills, 169 stars) and Make Op Scaffold (CVCUDA/CV-CUDA, 2.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nv Segment Ct?

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.

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SKILL.md

NV-Segment-CT

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