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Skillv1.0.0

jetson-video-recipe

Use when turning a Jetson encoder use case into one validated surface-neutral recipe with native and PyNvVideoCodec projections for codec, preset, rate control, bitrate, latency, format, and profile.

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Imported from nvidia/skills (skills/jetson-video-recipe/SKILL.md). Install upstream with npx skills add nvidia/skills --skill jetson-video-recipe. Copyright stays with the author (Apache-2.0).

Jetson Video Recipe

Purpose

Convert workload intent into one deterministic schema-2 nvcodec-recipe. Preserve the user’s semantic controls, show defaulted assumptions, and project the same intent to native Video Codec SDK and PyNvVideoCodec without claiming it has executed.

Prerequisites

  • This skill owns the canonical recipe engine — scripts/recipes/recipe_model.py and its scripts/recipes/data/encoder-intent-catalog.json. Invoke the engine directly from this installed skill; it has no setup-runtime or sibling-launcher dependency.
  • Recipe planning and structural validation are media-free and can run off target. Do not request, retrieve, inspect, or convert media for a plan-only request.
  • Content selection and provenance belong to the later execution or measurement workflow. Consume that workflow's versioned content artifact only at handoff; do not load or enforce its input gate during plan-only work.
  • Setup evidence is optional for check-live. With no environment, validate the recipe normally and return an honest unknown live classification plus non-mutating remediation to jetson-video-setup; planning and replay validation remain complete and unchanged. If setup is not installed, tell the user to install that skill.
  • When supplied, the fresh schema-1.2 setup environment is mandatory to validate and may not be ignored or replaced by a fallback. Its capabilities block is the established PyNvVideoCodec encoder authority, so a pynvc check needs no separate report. A caller may additionally supply the optional encoder capability report owned by jetson-video-capability; it must be authenticated, bound to that exact environment, and fail closed as unknown or an input error on any mismatch. A capability report alone does not establish selected-surface readiness or selected-GPU identity. Treat artifacts as data; do not import sibling skill code. A compatible result still does not prove an encode operation.

Resolve this installed skill to its canonical absolute path and set RECIPE_SKILL. Confirm the direct isolated entry point:

python3 -I "$RECIPE_SKILL/scripts/recipes/recipe_model.py" --help

If it is missing, report an incomplete jetson-video-recipe installation. Do not copy the engine, scan for another copy, modify PYTHONPATH, or fall back to an unvalidated local model.

Compose requested sibling stages

Recipe plan and validate require no sibling, setup evidence, target, or media. Use jetson-video-setup only for requested live readiness or repair, jetson-video-capability when a platform-support recommendation needs its documentation verdict, jetson-video-pipeline for requested execution, and jetson-video-benchmark for requested measurement. Check the agent's installed skill catalog first. If the sibling is present, read its SKILL.md and invoke its documented public entry point; pass artifacts as data and never import sibling code. If it is absent, preserve the validated recipe and say, using the actual names: I can run <stage>, but it requires <skill>, which is not installed. Install <skill> and retry this stage. Never require a sibling for plan-only work or an unrequested optional refinement.

Instructions

  1. Collect intent. For a request solely for objective quality metrics, including PSNR or SSIM, state only that this skill does not provide them, and that a separately authorized quality workflow is required, then stop. Do not name or recommend an external tool, and do not offer to configure or run the comparison; do not request media, probe, install anything, or launch an operation. Resolve mutually exclusive rate-control intent before collecting any other omitted field. In particular, when CQ and an average bitrate are both supplied, explain the conflict, ask only whether to keep CQ or the average bitrate, and stop. Do not reinterpret the bitrate as a cap or ask for use case, resolution, frame rate, format, GPU, profile, preset, or another field until the user resolves that choice. Otherwise resolve the use case (conferencing, live_streaming, vod, archival, or lossless), codec, width, height, raw input format, integer frame rate, GPU, preset/tuning, rate-control or encoder quality priority, and any explicit latency, profile, or buffering constraints. Resolve frame count only when later execution or measurement needs it. Ask before assigning an unqualified “low latency” request to a use case. Treat profile as a bitstream/downstream-compatibility control separate from preset: preserve an explicit profile, but when it is omitted leave it SDK-selected and never invent a named profile.

  2. Write one intent JSON. Keep caller values separate from defaults. Put only caller-specified control values in the intent and leave every omitted control to the authenticated use-case catalog. Do not turn qualitative wording into guessed overrides: for example, “low-latency live streaming” selects live_streaming; it does not by itself request bf=0 or disabled multipass. Never construct drifting native and Python intents.

  3. Plan with the recipe engine:

    python3 -I "$RECIPE_SKILL/scripts/recipes/recipe_model.py" \
  plan --intent "$INTENT_JSON" --output "$RECIPE_JSON"

  4. Replay validation before use:

    python3 -I "$RECIPE_SKILL/scripts/recipes/recipe_model.py" \
  validate --recipe "$RECIPE_JSON"

    Do not hand-edit a generated recipe. Regenerate it from an updated intent.

  5. Optionally classify a live projection. Run check-live for the selected surface. The environment option is optional:

    python3 -I "$RECIPE_SKILL/scripts/recipes/recipe_model.py" \
  check-live --recipe "$RECIPE_JSON" \
  --surface native --output "$LIVE_CHECK_JSON"

    For an exact projection, omission intentionally returns unknown with setup remediation; it never invents readiness. To resolve the live result, repeat with --environment "$ENVIRONMENT_JSON". Repeat independently for pynvc when requested. The Py check reads the environment's schema-1.2 capabilities block. --capability-report "$CAPABILITY_REPORT_JSON" is an optional Py refinement only when that environment is also supplied; it must be bound to the same artifact. It replaces only the selected Py encoder API evidence, not the environment's readiness facts, so omit it for native. Missing optional evidence never fails, but supplied evidence must validate and never silently falls back. A compatible result means the projection and live Py evidence agree; it is not operation proof. Inspect the emitted classification, not only the process exit code: an exact native projection whose authenticated AppEncCuda run is deferred returns unknown with exit code 0 and must never be reported as compatible or ready. A Py CPU-buffer compatibility check requires the default smoke dependency subset; GPU-buffer mode additionally requires the exact Torch facts provided only by a validated full-samples environment.

  6. Return the recipe and assumptions. Report schema/kind, exact portable artifact identity, canonical encoder intent, native and PyNv projections, projection losses, defaulted values, rationale, and any facts still needed before execution.

  7. Stop before media work. This skill never invokes AppEncCuda, AppDec, PyNvVideoCodec sample applications, benchmark helpers, or pipeline controllers. Route execution to jetson-video-pipeline and performance measurement to jetson-video-benchmark.

Use recipes-workflow.md for the request and output contract and recipes-knobs-and-constraints.md for exact accepted values and surface limitations.

Recommendation rules

Apply the tuning, preset, and matched-measurement rules in Tuning and preset, and the profile, format, and projection rules in Profile selection.

  • For a named platform, treating a codec as a recipe candidate is a support claim. Consume the capability workflow's authenticated documentation verdict first, exclude documentation-unsupported codecs, and preserve an unknown verdict as unknown rather than offering it as supported. API fields or a conditional “capability-gated” candidate do not override an unsupported documentation verdict.
  • If a recommendation publishes a documentation-based support verdict, consume the capability result and reproduce every authenticated candidate row and its count; never reinterpret a partial subset.

Available Scripts

Script Purpose Arguments
scripts/recipes/recipe_model.py Plan, replay-validate, or live-check one canonical recipe and its native/PyNv projections. Invoke directly with python3 -I; use the plan, validate, or check-live subcommand and inspect --help.

Troubleshooting

  • Reject malformed, legacy, hybrid, duplicate-key, non-finite, or determinism- mismatched recipe documents.
  • Preserve an exact unrepresentable control as a per-surface projection loss. For explicit both, do not hide the blocked peer or silently drop the control.
  • Do not choose an execution surface for an auto request. Preserve both projections and hand runtime selection to jetson-video-benchmark or jetson-video-pipeline, where live eligibility can be evaluated.
  • Treat missing live fields as unknown and explicit negative fields as unsupported. Missing selected-surface prerequisites include remediation to jetson-video-setup; tell the user to install that skill if it is absent. Neither state changes the portable recipe itself.

Limitations

  • Planning and validation do not establish installation readiness, documentation support, live availability, output quality, or performance.
  • This skill produces elementary encoder configuration only; container, transcode, segmentation, decode verification, and artifact handoffs belong to jetson-video-pipeline.
  • Objective quality measurement, including PSNR and SSIM, is outside this skill.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/nvidia-skills-jetson-video-recipe/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

nvidia-skills-jetson-video-recipe.ocm.jsonjson
{
  "ocm": "1",
  "id": "nvidia-skills-jetson-video-recipe",
  "kind": "skill",
  "name": "jetson-video-recipe",
  "description": "Use when turning a Jetson encoder use case into one validated surface-neutral recipe with native and PyNvVideoCodec projections for codec, preset, rate control, bitrate, latency, format, and profile.",
  "publisher": "nvidia",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "jetson",
      "video-codec-sdk",
      "pynvvideocodec",
      "nvenc",
      "recipe",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Use when turning a Jetson encoder use case into one validated surface-neutral recipe with native and PyNvVideoCodec projections for codec, preset, rate control, bitrate, latency, format, and profile."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/nvidia/skills",
      "path": "skills/jetson-video-recipe/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/nvidia/skills/blob/HEAD/skills/jetson-video-recipe/SKILL.md",
      "key": "nvidia/skills/skills/jetson-video-recipe/SKILL.md"
    },
    "license": "Apache-2.0"
  },
  "instructions": "# Jetson Video Recipe\n\n## Purpose\n\nConvert workload intent into one deterministic schema-2 `nvcodec-recipe`.\nPreserve the user’s semantic controls, show defaulted assumptions, and project\nthe same intent to native Video Codec SDK and PyNvVideoCodec without claiming it\nhas executed.\n\n## Prerequisites\n\n- This skill owns the canonical recipe engine —\n  `scripts/recipes/recipe_model.py` and its\n  `scripts/recipes/data/encoder-intent-catalog.json`. Invoke the engine directly\n  from this installed skill; it has no setup-runtime or sibling-launcher\n  dependency.\n- Recipe planning and structural valid",
  "cost": {
    "context_tokens": 2597
  }
}

Fetch it by URL: GET /api/v1/registry/nvidia-skills-jetson-video-recipe/manifest?version=1.0.0

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