/holoscan-setup
Assess a host and choose a Holoscan SDK 5.x installation method using release availability, CUDA 13 compatibility, and component needs.
Claude Code
$ npx -y skills add NVIDIA/skills --skill holoscan-setup --agent claude-code
Codex
$ npx skills add NVIDIA/skills --skill holoscan-setup -a codex -y
Cursor
$ npx skills add NVIDIA/skills --skill holoscan-setup -a cursor -y
Gemini CLI
$ npx skills add NVIDIA/skills --skill holoscan-setup -a gemini-cli -y
GitHub Copilot
$ npx skills add NVIDIA/skills --skill holoscan-setup -a github-copilot -y
OpenCode
$ npx skills add NVIDIA/skills --skill holoscan-setup -a opencode -y
Windsurf
$ npx skills add NVIDIA/skills --skill holoscan-setup -a windsurf -y
SKILL.md
holoscan-setup.SKILL.mdname: holoscan-setup
description: "Assess a host and choose a Holoscan SDK 5.x installation method using release availability, CUDA 13 compatibility, and component needs."
license: Apache-2.0
metadata:
author: "Holoscan Team <holoscan-team@nvidia.com>"
version: "2.0.0"
github-url: "https://github.com/nvidia-holoscan/holoscan-sdk"
tags:
- holoscan
- installation
- setupHoloscan 5.x Setup
Establish the target
Determine the requested 5.x release or source branch and whether the user needs C++, Python, specific modules, or SDK development. Read the release notes and [SDK installation guide](https://docs.nvidia.com/holoscan/sdk-user-guide/setup/sdk-installation) with the matching 5.x version selected. An unversioned page may describe 4.x; for a development checkout use its `docs/sdk_installation.mdx` and `DEVELOP.md`. Treat Early Access binary methods as tentative until the exact artifacts are confirmed published. Package-name registration alone is not availability.
Inspect and assess
Use read-only checks on the intended Linux host:
uname -m
cat /etc/os-release
nvidia-smi
python3 --version
getconf GNU_LIBC_VERSION
docker --version
docker info
Report unavailable tools separately from incompatible hardware. Inspect an existing venv or SDK checkout only at user-selected or relevant known paths. Use `python -m pip list` in the intended environment and `dpkg-query -W 'holoscan*'` on Debian systems to distinguish 4.x installations from usable 5.x ones. A GPU passthrough check that runs/pulls an image is a separate execution step; a Docker version check alone does not establish passthrough.
The 5.0 baseline is CUDA 13 with dGPU support, Ubuntu 24.04 native packages, and x86_64/SBSA arm64. Wheels require glibc 2.39 and regular CPython 3.12–3.14. The `nvidia-smi` CUDA value describes driver capability, not toolkit presence. Check driver/runtime compatibility for the selected artifact; a CUDA 12-only host cannot be routed to a nonexistent 5.x CUDA 12 package. ARM64 does not establish Tegra/iGPU support. Confirm hardware-specific support before offering installation on Jetson, IGX, DGX Spark, or Grace systems.
To check an already-pulled image, run the bundled helper from this skill's installation directory with the **full confirmed 5.x tag**, for example:
bash scripts/check_ngc_image.sh v5.0.0-cuda13
It inspects the local image only, makes no registry request, and does not prove publication, GPU passthrough, or runtime success. Report its result accordingly.
Recommend and route
Present the relevant choices with separate publication and host-readiness status:
| Method | 5.x workflow | | --- | --- | | Development container | Published CUDA 13 multiarch image; full C++ and Python stack under `/usr` | | Debian | Published Ubuntu 24.04 C++ `holoscan` metapackage or Core/optional modules | | Python wheels | Published `holoscan` metapackage or CUDA 13 component wheels in a venv | | Source | Explicit 5.x ref; build container and checkout-specific options | | Conda | Unavailable for 5.0; check the selected release's documentation for later 5.x support |
Recommend source when the requested Early Access binaries are unavailable. Recommend the development container for a ready-to-use environment when the image and host support are confirmed; native C++ and Python needs can justify Debian or wheels. If the user already selected a supported method, honor that choice and proceed within their authorization. Otherwise ask one concise method-selection question before installation.
Use the corresponding `holoscan-install-container`, `holoscan-install-debian`, `holoscan-install-wheel`, or `holoscan-install-source` skill when available. Pass the release/source ref, host architecture, driver/runtime status, selected components, existing installation details, and verified artifact availability. If a companion skill is unavailable, consult the release instructions directly. For a Conda-specific request, `holoscan-install-conda` explains the limitation and alternatives; installing the 4.x Conda package does not meet a 5.x request.
Report the outcome
Report the selected SDK version/ref, method, component versions, and checks actually executed. Separate availability checks, imports, graph execution, and module/GPU qualification. Missing binaries should produce an actionable source alternative; missing host support should produce the prerequisite to resolve.
Read more
name: holoscan-setup
description: "Assess a host and choose a Holoscan SDK 5.x installation method using release availability, CUDA 13 compatibility, and component needs."
license: Apache-2.0
metadata:
author: "Holoscan Team <holoscan-team@nvidia.com>"
version: "2.0.0"
github-url: "https://github.com/nvidia-holoscan/holoscan-sdk"
tags:
- holoscan
- installation
- setupHoloscan 5.x Setup
Establish the target
Determine the requested 5.x release or source branch and whether the user needs C++, Python, specific modules, or SDK development. Read the release notes and [SDK installation guide](https://docs.nvidia.com/holoscan/sdk-user-guide/setup/sdk-installation) with the matching 5.x version selected. An unversioned page may describe 4.x; for a development checkout use its `docs/sdk_installation.mdx` and `DEVELOP.md`. Treat Early Access binary methods as tentative until the exact artifacts are confirmed published. Package-name registration alone is not availability.
Inspect and assess
Use read-only checks on the intended Linux host:
uname -m cat /etc/os-release nvidia-smi python3 --version getconf GNU_LIBC_VERSION docker --version docker info
Report unavailable tools separately from incompatible hardware. Inspect an existing venv or SDK checkout only at user-selected or relevant known paths. Use `python -m pip list` in the intended environment and `dpkg-query -W 'holoscan*'` on Debian systems to distinguish 4.x installations from usable 5.x ones. A GPU passthrough check that runs/pulls an image is a separate execution step; a Docker version check alone does not establish passthrough.
The 5.0 baseline is CUDA 13 with dGPU support, Ubuntu 24.04 native packages, and x86_64/SBSA arm64. Wheels require glibc 2.39 and regular CPython 3.12–3.14. The `nvidia-smi` CUDA value describes driver capability, not toolkit presence. Check driver/runtime compatibility for the selected artifact; a CUDA 12-only host cannot be routed to a nonexistent 5.x CUDA 12 package. ARM64 does not establish Tegra/iGPU support. Confirm hardware-specific support before offering installation on Jetson, IGX, DGX Spark, or Grace systems.
To check an already-pulled image, run the bundled helper from this skill's installation directory with the **full confirmed 5.x tag**, for example:
bash scripts/check_ngc_image.sh v5.0.0-cuda13
It inspects the local image only, makes no registry request, and does not prove publication, GPU passthrough, or runtime success. Report its result accordingly.
Recommend and route
Present the relevant choices with separate publication and host-readiness status:
| Method | 5.x workflow | | --- | --- | | Development container | Published CUDA 13 multiarch image; full C++ and Python stack under `/usr` | | Debian | Published Ubuntu 24.04 C++ `holoscan` metapackage or Core/optional modules | | Python wheels | Published `holoscan` metapackage or CUDA 13 component wheels in a venv | | Source | Explicit 5.x ref; build container and checkout-specific options | | Conda | Unavailable for 5.0; check the selected release's documentation for later 5.x support |
Recommend source when the requested Early Access binaries are unavailable. Recommend the development container for a ready-to-use environment when the image and host support are confirmed; native C++ and Python needs can justify Debian or wheels. If the user already selected a supported method, honor that choice and proceed within their authorization. Otherwise ask one concise method-selection question before installation.
Use the corresponding `holoscan-install-container`, `holoscan-install-debian`, `holoscan-install-wheel`, or `holoscan-install-source` skill when available. Pass the release/source ref, host architecture, driver/runtime status, selected components, existing installation details, and verified artifact availability. If a companion skill is unavailable, consult the release instructions directly. For a Conda-specific request, `holoscan-install-conda` explains the limitation and alternatives; installing the 4.x Conda package does not meet a 5.x request.
Report the outcome
Report the selected SDK version/ref, method, component versions, and checks actually executed. Separate availability checks, imports, graph execution, and module/GPU qualification. Missing binaries should produce an actionable source alternative; missing host support should produce the prerequisite to resolve.
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Questions about holoscan-setup.
- Which coding agents can use holoscan-setup?
- holoscan-setup installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

