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Holoscan Setup

NVIDIAOfficialCreated by Holoscan TeamDistributed by NVIDIA

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

Assess a host and choose a Holoscan SDK 5.x installation method using release availability, CUDA 13 compatibility, and component needs.
— the description by Holoscan Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The holoscan-setup skill provides a structured procedure for evaluating a Linux host's readiness for NVIDIA Holoscan SDK 5.x. It guides the user through verifying system requirements, such as CUDA 13 compatibility, OS version, and hardware architecture, before recommending the most appropriate installation path—whether via development containers, Debian packages, Python wheels, or source builds. The skill ensures that users select a valid, available release and helps identify potential conflicts with existing SDK versions or hardware limitations.

What it does

  • Assesses host system specifications including architecture, OS, and driver versions.
  • Validates compatibility between the host environment and Holoscan 5.x requirements.
  • Checks for existing SDK installations to prevent version conflicts.
  • Recommends the best installation method based on user needs and host readiness.
  • Provides guidance on verifying local container images for Holoscan components.
  • Routes users to specific installation skills based on the chosen deployment method.

When to use it

  • When preparing a new environment for Holoscan SDK 5.x development.
  • When troubleshooting installation failures or version mismatches.
  • When determining whether to use containers, native packages, or source builds.
  • When verifying if a specific host supports the requirements for CUDA 13 and Holoscan 5.x.

Advantages

  • Prevents installation errors by validating host prerequisites first.
  • Provides clear decision logic for choosing between multiple installation methods.
  • Helps distinguish between 4.x and 5.x SDK environments.
  • Ensures hardware-specific requirements are addressed before deployment.

Usage examples

“I need to set up Holoscan 5.0 on my Ubuntu 24.04 workstation. How do I start?”
The agent will perform a system check, verify your CUDA and OS environment, and recommend the appropriate installation method (e.g., container or Debian package) based on your hardware.
“Can I install Holoscan 5.x using Conda?”
The agent will assess the current 5.x release status, inform you that Conda is currently unsupported for 5.0, and suggest alternative installation paths.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/holoscan-setup/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip holoscan-setup.zip -d .agents/skills

Documentation by Holoscan Team

Holoscan 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 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:

Method5.x workflow
Development containerPublished CUDA 13 multiarch image; full C++ and Python stack under /usr
DebianPublished Ubuntu 24.04 C++ holoscan metapackage or Core/optional modules
Python wheelsPublished holoscan metapackage or CUDA 13 component wheels in a venv
SourceExplicit 5.x ref; build container and checkout-specific options
CondaUnavailable 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.

Files

BENCHMARK.md

Skill Benchmark: holoscan-setup

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: holoscan-setup

  • Evaluation date: 2026-10-15

  • Evaluator version: 1.5.6

  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)

  • Tasks: 5 evaluation tasks (4 positive, 1 negative)

  • Dataset digest: sha256:eb042e5df52f57f4abef43703a6a63b1a537a38068aadd09374d605aebbdf208 (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 1

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;

  • produces correct answers;

  • is discovered and activated when needed;

  • helps the agent complete the user's goal and expected workflow; and

  • avoids wasted skill and tool usage.

Results at a Glance

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall96.1% — baseline ran, but no comparable score was available; uplift unavailable95.3% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)80.0% → 100.0% (+20.0 points)
Correctness96.0% → 100.0% (+4.0 points)100.0% → 100.0% (±0.0 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness82.0% → 82.0% (±0.0 points)68.0% → 83.0% (+15.0 points)
Efficiency98.7% — baseline ran, but no comparable score was available; uplift unavailable98.5% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

A partial dimension was calculated from only the available configured signals; review the detailed report before relying on it.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases497,6971,006,939-509,242-50.57%skill 5/5; base 5/5
claude-codeholoscan-setup-5x-00162,658188,559-125,901-66.77%skill 1/1; base 1/1
claude-codeholoscan-setup-5x-002125,757184,072-58,315-31.68%skill 1/1; base 1/1
claude-codeholoscan-setup-5x-00360,055327,067-267,012-81.64%skill 1/1; base 1/1
claude-codeholoscan-setup-5x-00462,380123,964-61,584-49.68%skill 1/1; base 1/1
claude-codeholoscan-setup-5x-005186,847183,277+3,570+1.95%skill 1/1; base 1/1
codexAll cases313,462302,067+11,395+3.77%skill 5/5; base 5/5
codexholoscan-setup-5x-00128,67614,283+14,393+100.77%skill 1/1; base 1/1
codexholoscan-setup-5x-00228,83114,053+14,778+105.16%skill 1/1; base 1/1
codexholoscan-setup-5x-00328,63113,933+14,698+105.49%skill 1/1; base 1/1
codexholoscan-setup-5x-00428,57114,195+14,376+101.28%skill 1/1; base 1/1
codexholoscan-setup-5x-005198,753245,603-46,850-19.08%skill 1/1; base 1/1
ALL AGENTSDataset aggregate811,1591,309,006-497,847-38.03%skill 10/10; base 10/10

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

TierPurposeStatusEvidence
Tier 1Static validationPASSED WITH OBSERVATIONS11 validator(s); 11 finding(s)
Tier 2Semantic deduplicationPASSED2 validator(s); 0 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 5 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

  • MEDIUM QUALITY/quality_correctness: No documented scripts in table format (team-skills/holoscan/holoscan-sdk/holoscan-setup/SKILL.md)

  • MEDIUM QUALITY/quality_correctness: Instructions don't mention 'run_script' (team-skills/holoscan/holoscan-sdk/holoscan-setup/SKILL.md)

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (team-skills/holoscan/holoscan-sdk/holoscan-setup/SKILL.md)

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (team-skills/holoscan/holoscan-sdk/holoscan-setup/SKILL.md)

  • LOW QUALITY/quality_discoverability: Description doesn't mention WHEN to use this skill (team-skills/holoscan/holoscan-sdk/holoscan-setup/SKILL.md)

  • 6 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid it avoid wasted tool calls and token usage?skill_efficiency (50%) + token_efficiency (50%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

  • accuracy (Accuracy): final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.

  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Holoscan Team
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/holoscan-setup · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Holoscan Team or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download holoscan-setup.zip