Jetson Video Recipe
NVIDIAOfficialCreated by Vinit BansalDistributed by NVIDIA
- Skill
- Content generator
- Software development
- Apache-2.0
- Medium risk
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/jetson-video-recipe/downloadUnzip 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
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_countis required before raw encode or measurement, but may remain unknown until an authenticated decoder/transcoder reports it for compressed input. Ifuse_case,width, orheightis missing, returninput_requirednaming exactly the missing items and stop; apply the documented defaults for every other omitted item. Leave omitted profile SDK-selected.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.
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, returnfailedwith the exact defect and do not emit a recipe.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
failedwith the exact reason and do not claim a recipe.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.automeans 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
unrepresentablein 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
- ATTRIBUTION.mdAdded by AutoGineer921 B
- BENCHMARK.md7.4 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md5.0 KiB
- evals/evals.json3.7 KiB
- references/recipes-knobs-and-constraints.md12.4 KiB
- references/recipes-workflow.md5.4 KiB
- skill-card.md4.1 KiB
- skill.oms.sig5.0 KiB
skill-card.md
Description: <br>
Use when turning a Jetson encoder use case into one surface-neutral recipe with native Video Codec SDK and PyNvVideoCodec projections. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
CC-BY-4.0 AND Apache 2.0 <br>
Use Case: <br>
Developers and engineers creating deterministic encoder configuration recipes for NVIDIA Jetson video encoding workflows using Video Codec SDK and PyNvVideoCodec. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [No] <br> Credential Type(s): [None] <br>
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>
Known Risks and Mitigations: <br>
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>
Reference(s): <br>
Agent Skills <br>
Skill Output: <br>
Output Type(s): [Configuration instructions, Files] <br> Output Format: [JSON] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [Canonical sorted JSON with absolute path, byte count, and SHA-256] <br>
Evaluation Agents Used: <br>
Claude Code (
aws/anthropic/bedrock-claude-opus-4-8) <br>Codex (
openai/openai/gpt-5.5) <br>
Evaluation Tasks: <br>
4 evaluation tasks (4 positive) across 2 agents, 3 attempts per task, in isolated k8s-sandbox pods. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Security: Whether the skill is safe to use: checks for unsafe operations, secret leakage, and unauthorized access. <br>
Correctness: Whether the final answer is correct against the reference answer. <br>
Discoverability: Whether the right skill was selected, decoys were avoided, and the expected workflow executed. <br>
Effectiveness: Whether the skill helped complete the user's goal (50% goal completion + 50% expected workflow adherence). <br>
Efficiency: Whether wasted tool calls and token usage were avoided (50% tool-call productivity + 50% token efficiency). <br>
Underlying evaluation signals used in this run: <br>
security: Unsafe operations, secret leakage, and unauthorized access. <br>skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>accuracy: Final-answer correctness against the reference answer. <br>goal_accuracy: Whether the user's goal was achieved. <br>behavior_check: Whether the expected workflow behavior was followed. <br>skill_efficiency: Tool-call productivity. <br>token_efficiency: Actual uncached prompt plus completion usage. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 97.6% | 91.7% |
| Security | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Correctness | 85.0% → 100.0% (+15.0 points) | 53.3% → 95.0% (+41.7 points) |
| Discoverability | 100.0% | 92.5% |
| Effectiveness | 45.2% → 100.0% (+54.8 points) | 20.0% → 77.7% (+57.7 points) |
| Efficiency | 87.8% | 93.0% |
Skill Version(s): <br>
74bce4d (source: git SHA, committed 2026-09-16) <br>
Ethical Considerations: <br>
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>
(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>
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