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Official agent skill

Jetson Video Benchmark

by NVIDIA in NVIDIA/skills

A skill your agent uses when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and…

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Jetson Video Benchmark

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

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

GitHub CLI
$ gh skill install NVIDIA/skills jetson-video-benchmark --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-benchmark .claude/skills/jetson-video-benchmark && 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-benchmark
GitHub stars
3.5k
Used in
1 other repo
Token cost
~1.6k tokens
SKILL.md length
751 words
Files
8 (incl. references)
Skills in repo
386
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and…

  • Works in 4 steps: For a request solely for PSNR or SSIM,… → Resolve codec direction before probing… → An explicit live/run/real/actual… → …
  • Measuring Jetson Video Codec SDK
  • SKILL.md covers Purpose, Terminal gates, Live preflight and Measure, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jetson Video Benchmark is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent. Also use for a video request limited to PSNR or SSIM, to apply the terminal scope response.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/benchmark-output-contract.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

  • Measuring Jetson Video Codec SDK
  • PyNvVideoCodec encode/decode throughput
  • Comparing presets
  • Testing codec-worker capacity with authenticated samples and user media

Example prompts

  • “/jetson-video-benchmark”

Requirements

  • Python 3

Workflow steps

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

  1. For a request solely for PSNR or SSIM, state that objective quality is
  2. Resolve codec direction before probing or calculating. Explicit encode or
  3. An explicit live/run/real/actual measurement requires representative input
  4. A no-media planning/expected/indicative question follows

What it can do on your machine

Read from SKILL.md and the folder at commit dfdd080. 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 Benchmark loads about 1.6k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 751 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~103
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.3k

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 dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 751 words, ~1,633 tokens.

Download SKILL.mdSave it as .claude/skills/jetson-video-benchmark/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
jetson-video-benchmark
description
Use when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent. Also use for a video request limited to PSNR or SSIM, to apply the terminal scope response.
license
Apache-2.0
metadata.author
Vinit Bansal <vinitkumarb@nvidia.com>
metadata.tags
jetson, video-codec-sdk, pynvvideocodec, benchmark, nvenc, nvdec
metadata.languages
markdown
metadata.data-classification
public

Jetson Video Benchmark

Purpose

Measure codec-stage FPS and megapixels/second for an evidenced target and exact workload. Supported live routes are encode, decode, P4/P5 comparison, and a strictly increasing worker-capacity sweep. A separate no-media path calculates clearly labeled SDK-documentation estimates; it never claims a measurement.

Terminal gates

  1. For a request solely for PSNR or SSIM, state that objective quality is outside this skill and needs a separately authorized workflow, then stop. Return only that scope response. Do not append an alternative benchmark or next step, name another tool, request media, probe, or install anything.
  2. Resolve codec direction before probing or calculating. Explicit encode or decode wording wins. Otherwise, quality, preset, bitrate, rate control, recording, or compressed-output wording implies encode; explicitly compressed input, ingest, playback, or IP/RTSP input implies decode. Treat generic camera-count wording such as "connect" or "handle", and a codec name on a camera or device, as direction-neutral. Set neutral wording aside and resolve direction on the remaining cues; a neutral cue never creates a conflict. For an encode-led camera estimate, state that NVDEC applies only when cameras already emit the named compressed codec, keep encode primary, and ask the user to confirm direction after giving the planning scenarios. If non-neutral cues conflict or are absent, present both interpretations, state that a unique encode estimate also needs an exact preset and measured validation needs representative input, then ask which direction applies and stop at that gate. Transcoding consumes separate decode and encode budgets: measure it as the supported decode and encode routes and report each budget separately, never as one combined FPS.
  3. An explicit live/run/real/actual measurement requires representative input for every requested direction: raw frames with format, geometry, and frame count for encode; a compressed elementary stream or container for decode. Each input must be one exact target-local path or user-supplied HTTP(S) URL. If any is absent, return only input_required and ask for the missing item(s), including a separate exact path or URL for every requested direction, before reading workflow references or sibling skills, probing, authentication, recipe work, or workspace creation. Never benchmark the setup smoke fixture or substitute catalog media.
  4. A no-media planning/expected/indicative question follows documented-performance-estimates.md. It requires the exact documented row and, for a scaled estimate, clock provenance; it sets measurement_performed: false. Preserve that reference byte-for-byte. Omitted preset, per-stream FPS, or stream mix does not block planning: enumerate the reference's bounded documented candidates and scenarios, disclose every assumption, and ask for the omitted values.
Show full SKILL.md (341 more words)Show less

Live preflight

Read benchmark-workflow.md completely once, then apply its preflight section. Preserve explicit native, pynvc, and both; map delegated selection to auto. Obtain fresh read-only readiness from jetson-video-setup, passing any exact interpreter already supplied or established in this conversation. Setup otherwise checks the conventional profile path. For auto, zero eligible surfaces blocks, one runs, and two returns selection_required. Python encode and comparison need a full-samples environment; decode performance may use pynvc-smoke.

Encode, compare, and encode-capacity also require a validated schema-2 recipe from jetson-video-recipe. A missing dependency stops that branch with dependency_required; an eligible peer may continue as partial.

Measure

  1. Follow that same reference for sample allowlists, builds, direct argument lists, frame accounting, option checks, marker grammar, comparisons, and worker sweeps.
  2. Show the dry-run plan, then use a fresh mode-0700 workspace. Authenticate each launcher immediately before use and launch its literal argument list.
  3. Run one excluded whole-process warmup and at least three new measured processes per variant and surface, each with a finite timeout and without -loop.
  4. Accept the sample-reported FPS only when its positive marker and processed frame count match. Apply every identity, workload, marker, and statistics check in benchmark-output-contract.md.

Report

Retain each launch's unedited output and write the compact result described by benchmark-output-contract.md. Report every repetition, recomputed mean/minimum/maximum, MP/s only when dimensions are sample-bound, partial branches, limitations, and retry reason. Label worker sweeps as codec-stage capacity bounds and preset comparisons as throughput-only.

Limitations

  • Results apply only to the evidenced target, release, clocks, sample, content, frame range, and controls; they are not a portable product ceiling.
  • Do not interpolate undocumented presets or scale across format, bit depth, chroma, codec, rate control, or tuning. Resolution scaling is allowed only as the estimate reference's explicitly labeled pixel-area heuristic.
  • Keep documented estimates per engine; use an all-engine multiplier only under the explicit aggregate-planning rule in the estimate reference.
  • The PyNvVideoCodec 2.1 encode-performance helper caps each worker at 1,000 frames; follow the common-frame rule in the workflow.
  • Preserve input_required, selection_required, dependency_required, blocked, partial, and failed; do not promote a peer's success.

© 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 7 other files (references) in skills/jetson-video-benchmark of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • evals/evals.json
  • references/benchmark-output-contract.md
  • references/benchmark-workflow.md
  • references/documented-performance-estimates.md
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit dfdd080

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 Benchmark 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 Benchmark compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jetson Video Benchmark this skillNVIDIA/skills3.5k1 repos~1.6kAutomated safety check: PassApache-2.0
Fla Triton To Gluonfla-org/flash-linear-attention5.8k—~4.2kAutomated safety check: PassMIT
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

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Works with

Categories

Questions about Jetson Video Benchmark

What does Jetson Video Benchmark do?

A skill your agent uses when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and…. Jetson Video Benchmark is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent.

When should I use Jetson Video Benchmark?

Jetson Video Benchmark fits situations like: measuring Jetson Video Codec SDK; pyNvVideoCodec encode/decode throughput; comparing presets; testing codec-worker capacity with authenticated samples and user media.

How do I install Jetson Video Benchmark in Claude Code?

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

How do I install Jetson Video Benchmark in Codex?

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

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

What does Jetson Video Benchmark need to run?

SKILL.md names no scripts, command-line tools or credentials: Jetson Video Benchmark is instructions for the agent only. Our summary lists: Python 3.

Does Jetson Video Benchmark 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 Benchmark 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 Benchmark use?

Jetson Video Benchmark 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 Benchmark use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 6.6k tokens, read only when the agent opens those files.

What are the alternatives to Jetson Video Benchmark?

Skills that share tags, products or a category with Jetson Video Benchmark: Fla Triton To Gluon (fla-org/flash-linear-attention, 5.8k stars), Megatron-LM on SLURM (NVIDIA/Megatron-LM, 18k stars), DGX Spark Memory and Thermal Ops (wshobson/agents, 40k stars) and DGX Spark Training Gotchas (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jetson Video Benchmark?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 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.