Official agent skill

Holoscan Install Container

by NVIDIA in NVIDIA/skills

Install Holoscan SDK via the NGC Docker container. An agent skill from NVIDIA/skills.

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install Holoscan Install Container

skills CLI
$ npx skills add NVIDIA/skills --skill holoscan-install-container -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills holoscan-install-container --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/holoscan-install-container .claude/skills/holoscan-install-container && 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/

Facts

Skill name
holoscan-install-container
GitHub stars
3.5k
Token cost
~1.9k tokens
SKILL.md length
492 words
Files
5
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

Install Holoscan SDK via the NGC Docker container. An agent skill from NVIDIA/skills.

  • Works in 4 steps: Pick the tag → Verify GPU passthrough, then pull → Verify with six examples → …
  • Container-based installs
  • SKILL.md covers Purpose, Prerequisites, Limitations and Instructions, plus 5 more sections
  • Calls docker, python3 and bash

What it does

Holoscan Install Container is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Install Holoscan SDK via the NGC Docker container. Use for container-based installs; not for native apt/pip/Conda installs.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `BENCHMARK.md`, `evals/evals.json` and `skill-card.md`).

It sits in DevOps & Cloud, covering Containers. It works with NVIDIA AI Platform, Docker, 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

  • Container-based installs
  • Not for native apt/pip/Conda installs

Example prompts

  • “/holoscan-install-container”

Requirements

  • Python 3
  • Docker

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Pick the tag
  2. Verify GPU passthrough, then pull
  3. Verify with six examples
  4. Launch command

What it can do on your machine

Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • docker
    • python3
    • bash

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.nvidia.com
    • docs.docker.com
    • catalog.ngc.nvidia.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

Holoscan Install Container loads about 1.9k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 492 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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 passed

The automated check found no risky patterns in SKILL.md.

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 492 words, ~1,861 tokens.

Download SKILL.mdSave it as .claude/skills/holoscan-install-container/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
holoscan-install-container
description
Install Holoscan SDK via the NGC Docker container. Use for container-based installs; not for native apt/pip/Conda installs.
version
1.0.0
license
Apache-2.0
metadata.author
Holoscan Team <holoscan-team@nvidia.com>
metadata.github-url
https://github.com/nvidia-holoscan/holoscan-sdk
metadata.tags
holoscan, install, container, docker, ngc

Holoscan NGC Container Installation

Purpose

Pull and verify the official Holoscan SDK container from NGC (nvcr.io/nvidia/clara-holoscan/holoscan), selecting the right CUDA/arch tag for the host GPU and validating with the bundled Python and C++ examples.

Prerequisites

  • Linux host with an NVIDIA GPU and a working driver (nvidia-smi).
  • Docker installed and the user in the docker group (or sudo).
  • NVIDIA Container Toolkit installed (docker run --gpus all works).
  • ~10–20 GB free disk for the image pull.
  • Network access to nvcr.io and docs.nvidia.com.

Limitations

  • Container images cover only the tag matrix below — no Conda/pip env inside.
  • GUI examples require X11 forwarding; this skill runs Holoviz headless to avoid that.
  • Tag suffix must match the host GPU/driver (cuda13 / cuda12-dgpu / cuda12-igpu) — wrong suffix → CUDA init failures.

Instructions

  • Container repo: nvcr.io/nvidia/clara-holoscan/holoscan.
  • The doc page at https://docs.nvidia.com/holoscan/sdk-user-guide/sdk_installation.html is canonical — fetch it if anything below disagrees.
  • Work through the steps below in order: pick the tag, verify GPU passthrough and pull, verify with the six examples, then hand off the launch command.

Step 1: Pick the tag

Tag = <version>-<suffix>, e.g. v4.1.0-cuda13. Get the current SDK version from the doc page above; pick the suffix from nvidia-smi (the "CUDA Version" field, top-right of the table header):

nvidia-smi CUDA VersionSuffix
13.x+cuda13
12.x, Ampere/Ada dGPUcuda12-dgpu
12.x, ARM64 iGPU (nvgpu)cuda12-igpu

The "CUDA Forward Compatibility mode ENABLED" banner is expected — not an error — when the container ships a newer CUDA minor version than the host driver supports. The forward-compat shim lets the container's CUDA runtime work against the older host driver within the same major version.

Step 2: Verify GPU passthrough, then pull

bash
docker run --rm --gpus all ubuntu:22.04 nvidia-smi 2>&1 | tail -5

If Docker is missing → install from https://docs.docker.com/engine/install/. If GPU passthrough fails → install the NVIDIA Container Toolkit per https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html, then retry.

Pull (~10–20 GB — warn the user before starting):

bash
docker pull nvcr.io/nvidia/clara-holoscan/holoscan:<TAG>
Show full SKILL.md (200 more words)Show less

Step 3: Verify with six examples

Tests cover: bare Python binding (1a), bare C++ runtime (1b, 2a), Python + Holoviz/Vulkan (2b, 3a), and C++ + Holoviz/Vulkan (3b). Holoviz examples always run headless (inject headless: true into the YAML) — this works whether or not a display is attached and avoids GUI failure modes over SSH.

bash
IMG=nvcr.io/nvidia/clara-holoscan/holoscan:<TAG>
RUN=(docker run --rm --runtime=nvidia --gpus all --cap-add CAP_SYS_PTRACE --ipc=host --ulimit memlock=-1 --ulimit stack=67108864)

# 1a. hello_world (Python) — expect "Hello World!"
"${RUN[@]}" "$IMG" bash -c \
  "ulimit -s 32768 && python3 /opt/nvidia/holoscan/examples/hello_world/python/hello_world.py"

# 1b. hello_world (C++) — expect "Hello World!"
"${RUN[@]}" "$IMG" bash -c \
  "ulimit -s 32768 && /opt/nvidia/holoscan/examples/hello_world/cpp/hello_world"

# 2a. tensor_interop (C++) — expect tensors doubling each pass, "Graph execution finished."
"${RUN[@]}" "$IMG" bash -c \
  "ulimit -s 32768 && /opt/nvidia/holoscan/examples/tensor_interop/cpp/tensor_interop"

# 2b. tensor_interop (Python, 10 frames) — Holoviz, headless. The YAML has no
#     headless field by default, so inject one under `holoviz:`. Expect
#     "message received (count: 10)".
"${RUN[@]}" "$IMG" bash -c "
  ulimit -s 32768
  sed -e 's/count: 0/count: 10/' \
      -e 's/repeat: true/repeat: false/' \
      -e 's/realtime: true/realtime: false/' \
      -e 's/^holoviz:/holoviz:\n  headless: true/' \
      /opt/nvidia/holoscan/examples/tensor_interop/python/tensor_interop.yaml > /tmp/ti.yaml
  cd /opt/nvidia/holoscan/examples/tensor_interop/python
  python3 tensor_interop.py --config /tmp/ti.yaml
"

# 3a. video_replayer (Python, 10 frames) — Holoviz, headless. Inject `headless: true`
#     under `holoviz:` (above `width: 854`). Same sed works for the C++ YAML in 3b —
#     both files share the same `holoviz:` section shape.
"${RUN[@]}" "$IMG" bash -c "
  ulimit -s 32768
  sed -e 's/count: 0/count: 10/' \
      -e 's/repeat: true/repeat: false/' \
      -e 's/realtime: true/realtime: false/' \
      -e 's/^  width: 854/  headless: true\n  width: 854/' \
      /opt/nvidia/holoscan/examples/video_replayer/python/video_replayer.yaml > /tmp/vr.yaml
  cd /opt/nvidia/holoscan/examples/video_replayer/python
  HOLOSCAN_INPUT_PATH=/opt/nvidia/holoscan/data python3 video_replayer.py --config /tmp/vr.yaml
"

# 3b. video_replayer (C++, 10 frames) — same headless injection as 3a. The C++
#     YAML hard-codes `directory: "../data/racerx"`, but HOLOSCAN_INPUT_PATH
#     overrides it, so we don't need to patch that field.
"${RUN[@]}" "$IMG" bash -c "
  ulimit -s 32768
  sed -e 's/count: 0/count: 10/' \
      -e 's/repeat: true/repeat: false/' \
      -e 's/realtime: true/realtime: false/' \
      -e 's/^  width: 854/  headless: true\n  width: 854/' \
      /opt/nvidia/holoscan/examples/video_replayer/cpp/video_replayer.yaml > /tmp/vr_cpp.yaml
  cd /opt/nvidia/holoscan/examples/video_replayer/cpp
  HOLOSCAN_INPUT_PATH=/opt/nvidia/holoscan/data ./video_replayer --config /tmp/vr_cpp.yaml
"

Step 4: Launch command

bash
docker run -it --rm \
  --runtime=nvidia --gpus all --cap-add CAP_SYS_PTRACE \
  --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
  nvcr.io/nvidia/clara-holoscan/holoscan:<TAG>
# Examples: /opt/nvidia/holoscan/examples/
# Mount files: -v /host/path:/container/path
# GUI examples: add -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY

Next:

  • Explore: ls /opt/nvidia/holoscan/examples/
  • Walk through one: /holoscan-explain-example

Troubleshooting

  • docker: Error response from daemon: could not select device driver "nvidia". NVIDIA Container Toolkit is missing or not configured. Install per the link in Step 2 and restart Docker.
  • CUDA init failure inside the container. Tag suffix doesn't match the host. Re-check nvidia-smi CUDA Version and the table in Step 1.
  • Segmentation fault when launching an example. ulimit -s 32768 wasn't applied inside the container. Use the bash -c "ulimit -s 32768 && ..." pattern shown in Step 3.
  • Holoviz example hangs / no window over SSH. YAML wasn't patched to headless: true. Use the sed injection shown in Step 3.
  • video_replayer can't find data. Set HOLOSCAN_INPUT_PATH=/opt/nvidia/holoscan/data — overrides the YAML's hard-coded path.

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in skills/holoscan-install-container of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 67a13c0

Compare with similar skills

Holoscan Install Container 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.

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Holoscan Install Container this skillNVIDIA/skills3.5k—~1.9kAutomated safety check: PassApache-2.0
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Generate Nemo Gym Envadithya-s-k/FineEnvs443—~2.1kAutomated safety check: PassApache-2.0
Init GPU Serverdrawthingsai/draw-things-community580—~2.2kAutomated safety check: PassGPL-3.0
Migrate Workflow Ec2 To Osdcpytorch/test-infra113—~2kAutomated safety check: PassCustom licence
Vllm Deploy Dockervllm-project/vllm-skills103—~2.5kAutomated safety check: NotesApache-2.0

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Categories

Questions about Holoscan Install Container

What does Holoscan Install Container do?

Install Holoscan SDK via the NGC Docker container. An agent skill from NVIDIA/skills. Holoscan Install Container is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Install Holoscan SDK via the NGC Docker container.

When should I use Holoscan Install Container?

Holoscan Install Container fits situations like: container-based installs; not for native apt/pip/Conda installs.

How do I install Holoscan Install Container in Claude Code?

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

How do I install Holoscan Install Container in Codex?

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

Can I use Holoscan Install Container 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 holoscan-install-container -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/holoscan-install-container, .gemini/skills/holoscan-install-container, .github/skills/holoscan-install-container and .opencode/skills/holoscan-install-container in your project.

What does Holoscan Install Container need to run?

Going by SKILL.md and its folder, Holoscan Install Container needs the command-line tools its instructions call (docker, python3 and bash). Our summary lists: Python 3; Docker.

Does Holoscan Install Container access the network?

SKILL.md names 3 domains. As links in the text: docs.nvidia.com, docs.docker.com and catalog.ngc.nvidia.com. This is read from the text; nothing was executed.

Is Holoscan Install Container safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Holoscan Install Container use?

Holoscan Install Container 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 Holoscan Install Container use?

About 1.9k tokens (SKILL.md is roughly 7.4k 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 Holoscan Install Container?

Skills that share tags, products or a category with Holoscan Install Container: Cosmos3 Env Troubleshoot (NVIDIA/cosmos-framework, 558 stars), Generate Nemo Gym Env (adithya-s-k/FineEnvs, 443 stars), Init GPU Server (drawthingsai/draw-things-community, 580 stars) and Migrate Workflow Ec2 To Osdc (pytorch/test-infra, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Holoscan Install Container?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 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.