Official agent skill

Jetson Video Setup

by NVIDIA in NVIDIA/skills

A skill your agent uses when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame…

OfficialApache-2.0Auto-check: notesAI & LLM Engineering

Install Jetson Video Setup

skills CLI
$ npx skills add NVIDIA/skills --skill jetson-video-setup -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/skills jetson-video-setup --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/jetson-video-setup .claude/skills/jetson-video-setup && 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
jetson-video-setup
GitHub stars
3.5k
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
1,231 words
Files
9 (incl. references)
Skills in repo
380
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame…

  • Works in 6 steps: Confirm commands would run on a Jetson.… → Inspect only the selected product with… → If inspection is all the user requested,… → …
  • Verifying readiness of the native NVIDIA Video Codec SDK
  • SKILL.md covers Purpose, Read before acting, Select the product and Workflow, plus 4 more sections
  • Calls apt-get

What it does

Jetson Video Setup is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/setup-install.md`).

It sits in AI & LLM Engineering, covering GPU and accelerator computing and QA and bug reports. 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

  • Verifying readiness of the native NVIDIA Video Codec SDK
  • PyNvVideoCodec on Jetson
  • Including the one-frame encode/decode smoke test with official samples
  • When interpreting what those readiness results

Example prompts

  • “/jetson-video-setup”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Confirm commands would run on a Jetson. Inspect /etc/nv_tegra_release,
  2. Inspect only the selected product with the direct commands in
  3. If inspection is all the user requested, report what is installed and stop.
  4. If the selected product is missing and the user asked to install or repair
  5. After installation, repeat the direct inspection. Then run the installed
  6. Report each selected product separately. Use ready only after its official

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 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:

    • apt-get

    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

Jetson Video Setup loads about 2.4k tokens when it runs, and up to ~7.8k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 1,231 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.8k

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.

  • NoteRuns commands with sudoSKILL.md:166
    he reviewed command with noninteractive `sudo -n`; if that

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 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 1,231 words, ~2,406 tokens.

Download SKILL.mdSave it as .claude/skills/jetson-video-setup/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
jetson-video-setup
description
Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.
license
Apache-2.0
metadata.author
Vinit Bansal <vinitkumarb@nvidia.com>
metadata.tags
jetson, video-codec-sdk, pynvvideocodec, setup, nvenc, nvdec
metadata.languages
markdown
metadata.data-classification
public

Jetson Video Setup

Purpose

Inspect, install, and verify the native NVIDIA Video Codec SDK and PyNvVideoCodec on a live Jetson. Setup owns product installation and readiness. Use the sibling video skills for codec support, encoder configuration, performance measurement, and application pipelines.

Use the standard package manager, Python environment tools, and installed NVIDIA samples directly, then return a concise readiness result.

Read before acting

Select the product

Resolve the requested product before touching the target:

  • “Video Codec SDK”, “VC SDK”, “native SDK”, or nvidia-video-codec-sdk selects the native product.
  • “PyNvVideoCodec”, “PyNv”, “PySDK”, “Python SDK”, or an explicitly Python interface selects PyNvVideoCodec.
  • Select both only when the user asks for both.

A genuinely bare “video SDK” setup or readiness request is ambiguous. Ask whether the user wants native Video Codec SDK, PyNvVideoCodec, or both, then stop. The word “report” does not resolve that ambiguity.

Keep native and Python work independent. A failure on one surface must not erase a successful result from the other.

Workflow

  1. Confirm commands would run on a Jetson. Inspect /etc/nv_tegra_release, /etc/os-release, and the requested GPU ordinal. On another host, provide guidance only and make no readiness claim.
  2. Inspect only the selected product with the direct commands in setup-workflow.md. For native, identify the installed package, package-owned Samples tree, CUDA toolkit, and build tools. For Python, use the exact interpreter supplied by the user or created during this request and inspect its installed distribution and loaded module. Never scan the filesystem for virtual environments.
  3. If inspection is all the user requested, report what is installed and stop. Package or import presence is installed, not ready.
  4. If the selected product is missing and the user asked to install or repair it, follow that install reference. Show the exact package or pip commands before mutation. Install and verify system prerequisites before creating a final Python environment path. Change only the selected product and its missing prerequisites.
  5. After installation, repeat the direct inspection. Then run the installed release's official one-frame encode followed by independent decode as described in setup-workflow.md.
  6. Report each selected product separately. Use ready only after its official encode and independent decode pass all observable checks. Otherwise report installed, blocked, or failed, name the failing command or missing prerequisite, and give one concrete next action.

When another video skill asks only for readiness, perform steps 1 and 2 and return the exact package/Samples root or Python interpreter/package path. Do not run the setup smoke test if that consumer will immediately run its own authenticated operation.

PyNvVideoCodec environment selection

Use this interpreter precedence: an explicit user path, an exact path already established in the current conversation, then the conventional profile path. The conventional smoke interpreter is $HOME/.venvs/nvcodec/bin/python; the full-samples interpreter is $HOME/.venvs/nvcodec-full/bin/python. Checking one of these exact paths is not a filesystem scan. Never select a venv by directory order or fall back to system Python.

For a new environment, use the applicable conventional path when it is absent, or an explicit new absolute path in a durable user-owned location. If the conventional path exists, inspect it first. Reuse it when valid; otherwise report its exact defect, leave it untouched, and ask for a different new path. Return the selected interpreter path so downstream skills can use it directly.

Use pynvc-smoke for the setup smoke proof, decode-performance work, and a conventional interface-availability check. That availability check inspects only the exact smoke path and reports not_ready when it is absent or invalid. Any consumer encode operation, including a capability availability proof, and work using advanced raw decode, segmentation, or encode-performance samples selects the separate full-samples environment and dependencies described in the install guide. A media-free capability inventory may use either conventional profile: inspect smoke first, then full-samples when smoke is absent, and report the exact interpreter that answered. Apply that either-profile allowance only when the entire request is a media-free inventory. When a request also seeks an operation or a capability availability proof, use the profile that request binds; if that profile is not ready, report not_ready and do not query the other conventional profile. The interface-availability check above remains bound to the smoke path.

Show full SKILL.md (504 more words)Show less

Readiness proof

Both products use one generated 640×360 8-bit NV12 frame (345,600 bytes), H.264 encode, and an independent decode of the fresh bitstream.

ProductRequired evidence
NativePackage-owned AppEncCuda reports one encoded frame; package-owned AppDec consumes that exact nonempty bitstream, reports one decoded frame, and writes a 345,600-byte NV12 output.
PyNv full-samplesWheel-owned basic/encode.py reports one encoded CPU-buffer frame; wheel-owned advanced/decode.py consumes the exact bitstream, reports one frame, and writes a 345,600-byte output.
PyNv pynvc-smokeThe same encoder proof; wheel-owned advanced/decode_perf.py reports one requested decoded frame and a total of one, with no worker error, warning, or traceback. It does not claim a raw decoded file.

Exit zero or file creation alone is insufficient. Require the expected marker and count, a newly created nonempty bitstream, and the independent consumer. Do not require decoded bytes to equal the input because H.264 is lossy.

CPU-buffer and device-memory sample modes establish only the exact readiness operation. They do not establish external buffer sharing, zero copy, or a synchronization primitive for another process or pipeline stage. For a request to confirm an in-process or cross-stage buffer contract, invoke jetson-video-pipeline; require an authenticated operation of the actual downstream stage that proves the sharing handle, format and layout, ownership and lifetime, signal and wait behavior, and safe buffer reuse. Do not answer that request from setup evidence alone.

Compose requested sibling work

For a request that also asks about support, encoder configuration, throughput, or an application workflow, invoke only the matching public skill:

  • jetson-video-capability
  • jetson-video-recipe
  • jetson-video-benchmark
  • jetson-video-pipeline

Pass the selected product and exact local paths as data. Do not read or import a sibling skill's private files. If a required sibling is unavailable, preserve completed setup results and name the missing skill.

Safety

  • For a report-only request, perform direct read-only inspection only; never build samples, create a workspace or venv, launch a codec operation, or mutate packages or an existing Python environment.
  • Use only already configured, signature-authenticated package repositories. NVIDIA SDK and CUDA packages must come from the public Jetson repository for the installed release. Never add or edit a source, key, or trust bypass.
  • Before APT installation, inspect the candidate and origin, run the exact apt-get -s simulation, and reject removals, downgrades, or unexpected packages. Apply the reviewed command with noninteractive sudo -n; if that authorization is unavailable, stop rather than using a password prompt, su, or another escalation path.
  • Run dpkg --audit after an APT mutation and stop if it is nonempty or fails.
  • Keep native and Python acquisition separate. A Python-only request must not install nvidia-video-codec-sdk; a native-only request must not create a venv.
  • Keep credentials out of commands, logs, and reports. Reject symlinked or unexpected install targets and use fresh build/output paths.
  • Local installation and smoke results do not establish product support or that the release is the newest compatible release. Use current official NVIDIA documentation for those claims.

For PSNR, SSIM, DRM playback, capture, inference, or display work, state that setup does not own that workflow and route only an explicitly requested video codec portion.

© 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 8 other files (references) in skills/jetson-video-setup of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • references/setup-install.md
  • references/setup-output-contract.md
  • references/setup-workflow.md
  • references/video-content.md
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 0e0d506

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Jetson Video Setup 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.

Jetson Video Setup compared with similar skills
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Jetson Video Setup this skillNVIDIA/skills3.5k1 repos~2.4kAutomated safety check: NotesApache-2.0
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Hyperpod Version Checkerawslabs/agent-plugins9121 repos~910Automated safety check: PassApache-2.0
Megatron-LM on SLURMNVIDIA/Megatron-LM18k—~1.8kAutomated safety check: PassApache-2.0
Debug Failing GPUfacebookexperimental/triton201—~709Automated safety check: PassMIT
Optimize For GPUK-Dense-AI/scientific-agent-skills48k1 repos~3.4kAutomated safety check: PassMIT

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Questions about Jetson Video Setup

What does Jetson Video Setup do?

A skill your agent uses when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame…. Jetson Video Setup is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish.

When should I use Jetson Video Setup?

Jetson Video Setup fits situations like: verifying readiness of the native NVIDIA Video Codec SDK; pyNvVideoCodec on Jetson; including the one-frame encode/decode smoke test with official samples; when interpreting what those readiness results.

How do I install Jetson Video Setup in Claude Code?

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

How do I install Jetson Video Setup in Codex?

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

Can I use Jetson Video Setup 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 jetson-video-setup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jetson-video-setup, .gemini/skills/jetson-video-setup, .github/skills/jetson-video-setup and .opencode/skills/jetson-video-setup in your project.

What does Jetson Video Setup need to run?

Going by SKILL.md and its folder, Jetson Video Setup needs the command-line tools its instructions call (apt-get). Our summary lists: Python 3.

Does Jetson Video Setup 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 Jetson Video Setup safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Jetson Video Setup use?

Jetson Video Setup 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 Jetson Video Setup use?

About 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.4k tokens, read only when the agent opens those files.

What are the alternatives to Jetson Video Setup?

Skills that share tags, products or a category with Jetson Video Setup: Triton Skill (slowlyC/agent-gpu-skills, 169 stars), Hyperpod Version Checker (awslabs/agent-plugins, 912 stars), Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars) and Debug Failing GPU (facebookexperimental/triton, 201 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jetson Video Setup?

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.