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Jetson Video Recipe

NVIDIAOfficialCreated by Vinit BansalDistributed by NVIDIA

  • Skill
  • Content generator
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.
— the description by Vinit Bansal

From the skill’s files

Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.

Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections. 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.

What it does

  • Boundaries
  • Workflow
  • Live and downstream work
  • Required outcomes
  • Limitations

When to use it

  • Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections.

Advantages

The skill’s files do not state this.

Usage examples

The skill’s files do not state this.

Install

  1. Download the skill as a ZIP:

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

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

Documentation by Vinit Bansal

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.

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.

Files

BENCHMARK.md

Skill Benchmark: jetson-video-recipe

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

  • Skill: jetson-video-recipe

  • Evaluation date: 2026-09-16

  • Evaluator version: 1.5.6

  • Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)

  • Tasks: 4 evaluation tasks (4 positive)

  • Dataset digest: sha256:e9b7cb78073e3333a346c0bb42578634e0c37bcc68d4676840a4ca503851b006 (skill-evaluator-dataset-snapshot/1)

  • Attempts per task: 3

  • Environment: k8s-sandbox

  • Tier 2 evidence: required for publication

  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • is safe to use;

  • produces correct answers;

  • is discovered and activated when needed;

  • helps the agent complete the user's goal and expected workflow; and

  • avoids wasted skill and tool usage.

Results at a Glance

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall97.6% — baseline ran, but no comparable score was available; uplift unavailable91.7% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness85.0% → 100.0% (+15.0 points)53.3% → 95.0% (+41.7 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable92.5% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness45.2% → 100.0% (+54.8 points)20.0% → 77.7% (+57.7 points)
Efficiency87.8% — baseline ran, but no comparable score was available; uplift unavailable93.0% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

AgentDataset caseWith skillWithout skillDeltaChangeCoverage
claude-codeAll cases874,034720,637+153,397+21.29%skill 4/4; base 4/4
claude-coderecipe-cq-bitrate-conflict93,636125,460-31,824-25.37%skill 1/1; base 1/1
claude-coderecipe-format-not-memory-domain268,96094,237+174,723+185.41%skill 1/1; base 1/1
claude-coderecipe-high-profile-projection-loss265,580466,484-200,904-43.07%skill 1/1; base 1/1
claude-coderecipe-live-streaming-both245,85834,456+211,402+613.54%skill 1/1; base 1/1
codexAll cases463,205317,517N/AN/Askill 4/4; base 6/6
codexrecipe-cq-bitrate-conflict28,08140,832N/AN/Askill 1/1; base 3/3
codexrecipe-format-not-memory-domain276,68714,363+262,324+1826.39%skill 1/1; base 1/1
codexrecipe-high-profile-projection-loss88,960200,795-111,835-55.70%skill 1/1; base 1/1
codexrecipe-live-streaming-both69,47761,527+7,950+12.92%skill 1/1; base 1/1
ALL AGENTSDataset aggregate1,337,2391,038,154N/AN/Askill 8/8; base 10/10

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

TierPurposeStatusEvidence
Tier 1Static validationPASSED WITH OBSERVATIONS11 validator(s); 7 finding(s)
Tier 2Semantic deduplicationPASSED2 validator(s); 0 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 4 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/jetson-video-recipe/SKILL.md)

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/jetson-video-recipe/SKILL.md)

  • LOW QUALITY/quality_correctness: No examples provided (skills/jetson-video-recipe/SKILL.md)

  • LOW QUALITY/quality_discoverability: No '## Purpose' section (skills/jetson-video-recipe/SKILL.md)

  • LOW QUALITY/quality_reliability: No prerequisites/requirements documented (skills/jetson-video-recipe/SKILL.md)

  • 2 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

DimensionQuestionScored signals
SecurityIs it safe to use?security (100%)
CorrectnessIs the answer correct?accuracy (100%)
DiscoverabilityWas the right skill loaded when needed?skill_execution (100%)
EffectivenessDid the skill help complete the task?goal_accuracy (50%) + behavior_check (50%)
EfficiencyDid it avoid wasted tool calls and token usage?skill_efficiency (50%) + token_efficiency (50%)
  • Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.

  • Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.

  • Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.

  • The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.

  • Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).

  • Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

  • security (Security): unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.

  • skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).

  • accuracy (Accuracy): final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): whether the user's goal was achieved.

  • behavior_check (Behavior Check): whether the expected workflow behavior was followed.

  • token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

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.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

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

AutoGineer is not affiliated with or endorsed by Vinit Bansal or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download jetson-video-recipe.zip