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Official agent skill

Jetson Video Recipe

by NVIDIA in NVIDIA/skills

A skill your agent uses when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Jetson Video Recipe

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

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

GitHub CLI
$ gh skill install NVIDIA/skills jetson-video-recipe --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-recipe .claude/skills/jetson-video-recipe && 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-recipe
GitHub stars
3.6k
Used in
1 other repo
Token cost
~1.3k tokens
SKILL.md length
629 words
Files
7 (incl. references)
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.

  • Works in 5 steps: Collect use case, codec, positive… → Apply the fixed defaults and constraints… → Validate the document structurally:… → …
  • Turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections
  • SKILL.md covers Boundaries, Workflow, Live and downstream work and Required outcomes, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jetson Video Recipe is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/recipes-knobs-and-constraints.md`).

It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform. 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

  • Turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections
  • Tasks that involve GPU and accelerator computing

Example prompts

  • “/jetson-video-recipe”

Workflow steps

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

  1. Collect use case, codec, positive integer width/height/fps, input format,
  2. Apply the fixed defaults and constraints in
  3. Validate the document structurally: exact schema/kind; finite JSON; positive
  4. Write canonical, sorted JSON to a fresh path without overwriting anything.
  5. Return the intent, assumptions/defaults, both projections, all losses, and

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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 Recipe loads about 1.3k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 39 tokens; SKILL.md has 629 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.8k

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 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 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 629 words, ~1,289 tokens.

Download SKILL.mdSave it as .claude/skills/jetson-video-recipe/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
jetson-video-recipe
description
Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.
license
Apache-2.0
metadata.author
Vinit Bansal <vinitkumarb@nvidia.com>
metadata.tags
jetson, video-codec-sdk, pynvvideocodec, nvenc, recipe
metadata.languages
markdown
metadata.data-classification
public

Jetson Video Recipe

Create one deterministic nvcodec-recipe schema 2.0 document. Recipe work is off-target and media-free: it does not probe, install, encode, decode, or claim support, quality, or performance.

Boundaries

  • For a request solely about PSNR, SSIM, or another objective quality metric, say that a separately authorized quality workflow is required and stop.
  • Resolve a supplied CQ plus average or maximum bitrate conflict first. Return input_required, ask only which one to keep, and stop; do not reinterpret a bitrate as a cap or emit a recipe.
  • An unqualified “low latency” does not select a use case. Ask whether it means conferencing, live streaming, or another contract, return input_required, and stop without emitting a recipe.
  • Preserve every explicit control. If one surface cannot express it, publish a projection loss; never silently discard or weaken it.

Workflow

  1. Collect use case, codec, positive integer width/height/fps, input format, GPU, and any explicit profile, preset, tuning, rate-control, bitrate, GOP, B-frame, lookahead, AQ, multipass, or buffering controls. frame_count is required before raw encode or measurement, but may remain unknown until an authenticated decoder/transcoder reports it for compressed input. If use_case, width, or height is missing, return input_required naming exactly the missing items and stop; apply the documented defaults for every other omitted item. Leave omitted profile SDK-selected.
  2. Apply the fixed defaults and constraints in recipes-knobs-and-constraints.md, then build the schema-2 document exactly as described in recipes-workflow.md. Keep caller values and defaults separately attributable.
  3. Validate the document structurally: exact schema/kind; finite JSON; positive bounded integers; legal enum strings; mutually exclusive rate-control fields; format/profile constraints; projections derived from the same encoder_intent; and every explicit caller control represented in each projection or named in that projection's losses. Regenerate rather than editing an accepted recipe. If the regenerated document still fails structural validation, return failed with the exact defect and do not emit a recipe.
  4. Write canonical, sorted JSON to a fresh path without overwriting anything. Record its canonical absolute path, byte count, and SHA-256; every consumer rehashes that exact file. If no safe fresh path exists or writing/rehashing fails, return failed with the exact reason and do not claim a recipe.
  5. Return the intent, assumptions/defaults, both projections, all losses, and the recipe file's canonical absolute path, byte count, and SHA-256. For both, retain both outcomes. auto means retain both projections without selecting either; the pipeline or benchmark selects from fresh live eligibility evidence.

For a plan-only recipe, these Markdown rules are the complete authority. Do not probe the target, inspect installed SDK/sample source, scan the filesystem for example JSON, or invoke another skill merely to confirm the projection. Plan-only still requires steps 2-5, including writing and rehashing the fresh canonical recipe JSON and reporting its absolute path, byte count, and SHA-256; it forbids target and media operations, not local recipe-artifact creation.

Show full SKILL.md (163 more words)Show less

Live and downstream work

For a requested live classification, obtain a fresh read-only readiness result from jetson-video-setup for the selected product and GPU, plus the applicable raw/documentation result from jetson-video-capability. Missing facts remain unknown; explicit negatives or an unrepresentable projection are unsupported. API-reported capability is not operation proof.

Pass the original recipe identity as data to jetson-video-pipeline for execution or jetson-video-benchmark for measurement. Those skills must rehash it and hold non-compared controls constant. Do not import or recreate a sibling skill's implementation.

Required outcomes

  • H.264 1920x1080@60, 6 Mbps CBR live streaming defaults to P4, low_latency, GOP 60, one B-frame, zero lookahead, full-resolution multipass, max bitrate 6,000,000, and VBV 3,000,000.
  • An explicit H.264 High profile is exact in the native projection and unrepresentable in the public PyNvVideoCodec 2.1 sample projection.
  • A CQ plus average bitrate request produces no recipe until resolved.

Limitations

This skill produces elementary encoder configuration only. Content selection, container/transcode work, independent decode, benchmarking, and evidence capture belong to their owning skills.

© 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 6 other files (references) in skills/jetson-video-recipe of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • references/recipes-knobs-and-constraints.md
  • references/recipes-workflow.md
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 14a98ae

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 Recipe 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 Recipe compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jetson Video Recipe this skillNVIDIA/skills3.6k1 repos~1.3kAutomated safety check: PassApache-2.0
Megatron-LM on SLURMNVIDIA/Megatron-LM18k—~1.8kAutomated safety check: PassApache-2.0
DGX Spark Memory and Thermal Opswshobson/agents40k—~2kAutomated safety check: PassMIT
DGX Spark Training Gotchaswshobson/agents40k—~2kAutomated safety check: PassMIT
Cosmos Policy EvaluationOrchestra-Research/AI-Research-SKILLs13k—~3.7kAutomated safety check: PassMIT
OpenVLA-OFT Fine-TuningOrchestra-Research/AI-Research-SKILLs13k—~3.7kAutomated safety check: PassMIT

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Works with

Categories

Questions about Jetson Video Recipe

What does Jetson Video Recipe do?

A skill your agent uses when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections. Jetson Video Recipe is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.

When should I use Jetson Video Recipe?

Jetson Video Recipe fits situations like: turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections; tasks that involve GPU and accelerator computing.

How do I install Jetson Video Recipe in Claude Code?

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

How do I install Jetson Video Recipe in Codex?

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

Can I use Jetson Video Recipe 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-recipe -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-recipe, .gemini/skills/jetson-video-recipe, .github/skills/jetson-video-recipe and .opencode/skills/jetson-video-recipe in your project.

What does Jetson Video Recipe need to run?

SKILL.md names no scripts, command-line tools or credentials: Jetson Video Recipe is instructions for the agent only.

Does Jetson Video Recipe 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 Recipe 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 Jetson Video Recipe use?

Jetson Video Recipe 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 Recipe use?

About 1.3k tokens (SKILL.md is roughly 5.2k 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 4.6k tokens, read only when the agent opens those files.

What are the alternatives to Jetson Video Recipe?

Skills that share tags, products or a category with Jetson Video Recipe: Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars), DGX Spark Memory and Thermal Ops (wshobson/agents, 40k stars), DGX Spark Training Gotchas (wshobson/agents, 40k stars) and Cosmos Policy Evaluation (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jetson Video Recipe?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 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.