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Jetson Video Pipeline

NVIDIAOfficialCreated by Vinit BansalDistributed by NVIDIA

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

From the author

Use when planning, executing, and independently validating Jetson Video Codec SDK or PyNvVideoCodec encode/decode, transcode, segmentation, container decode, AV1, or concise acceptance workflows.
— the description by Vinit Bansal

AutoGineer summary

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

The Jetson Video Pipeline skill provides a structured framework for managing video encode, decode, and transcode operations on NVIDIA Jetson hardware. It focuses on using official Jetson Video Codec SDK and PyNvVideoCodec samples to perform codec tasks while ensuring each stage of the pipeline is independently verified. The skill enforces strict evidence-based reporting, requiring that every consumer validates the output bytes produced by its preceding stage. It is designed for developers needing to perform codec triage, AV1 verification, or acceptance testing, while maintaining clear boundaries regarding what constitutes proven performance or interoperability.

What it does

  • Plans and executes video codec workflows including encode, decode, and transcode.
  • Validates pipeline stages by re-hashing and re-authenticating artifacts between producers and consumers.
  • Provides structured acceptance reports for codec readiness and capability.
  • Supports container triage and AV1 verification using official NVIDIA sample contracts.
  • Enforces strict filesystem-based artifact sharing and synchronization protocols.
  • Manages dry-run planning for codec architecture and route verification.

When to use it

  • When you need to verify the correctness of a video codec pipeline on Jetson hardware.
  • When performing acceptance testing for encode/decode performance or functionality.
  • When you need to triage containerized video streams using official SDK tools.
  • When you require a reproducible, evidence-based report of codec pipeline execution.

Advantages

  • Ensures data integrity by requiring independent verification of every pipeline stage.
  • Provides a standardized, reproducible approach to codec testing.
  • Strictly adheres to official NVIDIA sample contracts and API usage patterns.
  • Clear separation between planning, execution, and validation phases.

Usage examples

“Plan an encode-decode pipeline for a 1080p H.264 file.”
The agent will outline the required stages, identify the necessary sample tools, and state the assumptions for execution without launching any processes.
“Execute an AV1 verification workflow for /data/input.ivf.”
The agent will authenticate the media, run the appropriate AV1 sample, verify the output frame count, and provide a report with checksums.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/jetson-video-pipeline/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip jetson-video-pipeline.zip -d .agents/skills

Documentation by Vinit Bansal

Jetson Video Pipeline

Purpose

Build and run direct NVIDIA sample commands, then prove each consumer used the exact bytes produced by the preceding stage. This skill owns codec workflow and evidence policy; it does not own environment installation or product-support claims.

Terminal gates

Apply these before inspecting the target, another skill, or a command:

  1. For a PSNR/SSIM-only request, state that objective quality measurement is outside this skill and requires a separately authorized workflow, return not_evaluated with reason out_of_scope, then stop. Do not name a tool or request media. Do the same for a request limited to capture, transport, AI, display, or glass-to-glass latency. For a mixed codec-plus-quality request, continue only the codec portion and report the quality portion as not_evaluated with reason out_of_scope rather than silently omitting it.

  2. Classify every other request before applying the media gate:

    • Return planned for architecture and route questions; they need no media. State assumptions and what execution would verify, then stop before target inspection, retrieval, authentication, recipe dispatch, or workspace creation.

    • A dry run with concrete commands needs a user-named path or placeholder and the metadata required by those commands. The media need not exist. Obtain recipe options through jetson-video-recipe, report planned, and do not inspect, authenticate, or launch anything.

    • Execution, verification, and performance require one exact target-local media path or one user-supplied HTTP(S) URL. Without it, return input_required, identify the intended route briefly using only stages expressible by this skill's allowlisted samples, ask for that one item, and stop before target inspection, retrieval, authentication, recipe dispatch, or workspace creation. Label every other requested transform as unresolved rather than inventing an executable route.

    Never choose catalog or synthetic media. The deterministic setup smoke fixture is allowed only for a bounded capability operation and is never representative pipeline or performance evidence.

Select and authenticate the execution surface

Steps 3–4 apply to execution; step 5 also applies to recipe-bearing dry runs. For other planning, preserve an explicit surface without claiming live eligibility; delegated surface-specific dry runs return selection_required without ranking the choices.

  1. Preserve explicit native, pynvc, and both. Map “whichever”, “best available”, “choose for me”, or otherwise delegated selection to auto, not both. For auto: zero eligible surfaces is blocked, one runs, and two is selection_required; do not rank them or consult old results.

  2. Obtain a fresh read-only readiness result from jetson-video-setup through public skill dispatch. For Python, pass any exact user-supplied or current-conversation interpreter. Otherwise select the profile before dispatch: decode-performance may use pynvc-smoke; encode, segmentation, advanced/decode.py, pipeline, and encode-benchmark work require full-samples. Setup checks that profile's conventional path; never scan for a venv. If only the smoke profile is ready for full-samples work, return dependency_required before workspace creation and direct the user to provision a separate full-samples venv; never upgrade the smoke venv in place. Native eligibility requires one installed, package-verified SDK and one package-owned Samples root. Python eligibility requires that exact interpreter, an importable PyNvVideoCodec distribution loaded from its environment, and a clean pip check. The result must match the requested Jetson and GPU. Preserve the setup reason when a candidate is ineligible. The requested pipeline supplies its own operation proof.

  3. Recipe-bearing encode and transcode work requires jetson-video-recipe; an acceptance request containing a performance stage also requires jetson-video-benchmark. Invoke either through public skill dispatch and pass its result as data. If a needed sibling is absent, preserve completed stages and say: I can run <stage>, but it requires <skill>, which is not installed. Install <skill> and retry this stage.

    “Preserve” means retain each completed stage's status and current path/size/SHA-256 identities in the user-facing partial result; it does not create hidden resumable state. On retry in the same request, reopen and rehash those artifacts and skip only unchanged complete stages. With a changed/missing identity, or a later request that does not supply the prior evidence, use a new workspace and rerun the stage.

Execute

  1. Choose the smallest route: encode_decode, native_transcode, pynvc_segments, container_triage, av1_verify, or acceptance.

  2. Read pipeline-workflow.md for stage order, content handling, retry policy, and reporting. Read official-sample-contract.md completely once; it covers sample allowlists, direct arguments, marker grammar, and frame-layout checks. Read buffer-sharing-and-synchronization.md completely once for filesystem boundaries. For a custom in-process CUVID/CUDA/NVENC graph, also read in-process-codec-boundaries.md completely once.

  3. For execution, canonicalize the exact media, record its provenance and identity, and use a new mode-0700 workspace with fresh outputs. For a dry run, use only the supplied identity and metadata, state assumptions, and show planned commands and handoffs without opening the media or authenticating launchers.

  4. For execution, authenticate each selected native sample or wheel member, launch its literal argument list directly, and retain unedited logs. Require the reference's exit, marker, count, error, freshness, and layout checks.

  5. Reopen and rehash every producer output before and after its independent consumer. Preserve successful branches, report a failed peer as partial, and apply the reference's single-retry rule.

  6. Emit the applicable io_contract directly in every plan, result, and producer/consumer boundary. Do not depend on a contract module or infer external sharing from a device-memory mode.

Route requirements

RouteRequired proof
encode_decodeOne validated recipe; direct AppEncCuda→AppDec or wheel-owned basic encode→advanced decode; exact raw and decoded byte counts.
native_transcodeH.264 input, exact HEVC native projection, AppTrans output, exactly one accepted transcode marker, then AppDec over the same hash; require the AppTrans and AppDec frame counts to be equal and positive, and to equal the known input count when available.
pynvc_segmentsWheel-owned schedule/config; every declared segment is fresh and nonempty; decode and rehash every segment independently.
container_triageEligible Jetson, exact local/retrieved container, intrinsic libavformat demux in AppDec or wheel-owned advanced decode, fresh decoded output.
av1_verifyExact AV1 native recipe; host and video-memory modes; AppDec consumes each exact IVF output and reports the expected frame count.
acceptanceA concise reproducible report covering requested readiness, capability, recipe, codec, and benchmark stages with per-stage status and identities.

Report

Use the statuses and concise report defined in pipeline-workflow.md. Include the selected runtime, exact media and recipe identities, literal commands, producer and consumer results, decoded layout/size validation, logs, limitations, and retry reason. Add compact JSON or a checksum manifest when useful or requested.

Limitations

  • Codec work does not prove capture, transport, inference, display, quality, or end-to-end latency.

  • API fields and inventory are not operation proof or product support.

  • Container demux is allowed only inside an authenticated released NVIDIA sample.

  • Apply every deterministic acceptance check in the references.

  • Codec/API capability and benchmark results do not prove buffer interoperability, zero copy, or synchronization compatibility.

Files

LICENSE

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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.

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Attribution and licence

Created by
Vinit Bansal
Publisher
NVIDIA (Official)
Distributed by
NVIDIA
Source
NVIDIA/skills · skills/jetson-video-pipeline · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
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