Jetson Video Capability
NVIDIAOfficialCreated by Vinit BansalDistributed by NVIDIA
- Skill
- Tool integration
- Software development
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured methodology for determining video codec support on NVIDIA Jetson platforms. It integrates data from live API queries, authenticated NVIDIA sample reports, and official documentation to provide a verified capability verdict. The skill enforces strict separation between raw API data, operational evidence, and product-support documentation, ensuring that users receive accurate information regarding codec, profile, chroma, and bit-depth support while avoiding unauthorized assumptions about DRM or performance metrics.
What it does
- Queries PyNvVideoCodec APIs for encoder and decoder capabilities.
- Extracts reports from authenticated native NVIDIA video codec samples.
- Reconciles live findings against official NVIDIA support matrices.
- Classifies capability evidence into API, sample, and documentation categories.
- Provides a structured verdict on codec support for specific hardware configurations.
- Handles scope-gating for DRM and objective quality metrics.
When to use it
- When you need to verify if a specific codec, profile, or bit-depth is supported on a Jetson device.
- When reconciling conflicting information between API outputs and official documentation.
- When preparing a technical report on hardware video encoding/decoding capabilities.
- When you need to determine if a specific video operation is documented as supported.
Advantages
- Ensures high-fidelity results by cross-referencing multiple authoritative sources.
- Prevents incorrect assumptions by strictly separating API results from product support.
- Provides a clear, standardized framework for reporting hardware capabilities.
- Maintains integrity by relying on authenticated samples and official documentation.
Usage examples
- “Check if HEVC 10-bit decoding is supported on my Jetson.”
- The agent will query the relevant API, check the official support matrix, and report whether the capability is documented and operationally verified.
- “What are the encoder capabilities for AV1 on this Jetson?”
- The agent will provide a report based on the native Video Codec SDK or PyNvVideoCodec, reconciled against official NVIDIA documentation.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/jetson-video-capability/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip jetson-video-capability.zip -d .agents/skills
Documentation by Vinit Bansal
Jetson Video Capability
Keep three authorities separate:
API query: raw fields, never operation or product-support proof.
Authenticated NVIDIA sample: report or exact operation evidence.
Applicable NVIDIA documentation: product-support verdict.
Scope gates
For a request solely for PSNR, SSIM, or other objective quality metrics, say this skill does not provide them and a separately authorized quality workflow is required; do nothing else.
For Netflix, Widevine, PlayReady, or clearly content-protected streaming, state only that this skill covers hardware encode/decode of user-supplied non-DRM bitstreams, not content-DRM playback. Do not claim whether the service works, describe Jetson certification, CDM, or secure-playback requirements, or recommend a browser, DRM module, workaround, or bypass; then stop. An unqualified “DRM” may mean Linux DRM/KMS; ask which meaning if context does not resolve it.
A bare “video SDK” is ambiguous: ask native Video Codec SDK, PyNvVideoCodec, or both before probing. An otherwise unqualified capability request uses the native-preferred fallback in surface-selection-contract.md.
Read-only discovery
Capability work depends on jetson-video-setup for a fresh, read-only
installation check. Invoke that skill through public dispatch and consume its
reported exact native package/Samples root or exact PyNvVideoCodec interpreter,
version, and loaded module path. Do not locate or import setup's files. A
missing or mismatched surface is unknown/not_ready, never codec unsupported;
route repair to setup without mutating anything here.
Engine capability queries belong here, not in setup. For PyNvVideoCodec, use
the exact selected interpreter to call the public GetEncoderCaps and
GetDecoderCaps APIs as described in
capability-queries.md. A broad encoder
catalog covers H.264, HEVC, and AV1; a broad decoder catalog covers all ten
families across four chroma formats and three bit depths (120 exact tuples).
A bounded request queries only named members of the applicable catalog set.
Preserve every scoped record, error, and GPU ordinal. Nonzero-GPU helper results
remain unknown when the public helper selects only GPU 0.
For native reports, reuse authenticated package-owned binaries or build only
the required report target in a fresh user-owned tree, then run
AppEncCuda -ec and/or AppDec -dc using the build, identity, and grammar
rules in the capability reference. A query-only request does not authorize
package installation or an encode/decode operation. If the package, source,
tool, interpreter, or runtime-library identity cannot be established, report
the result unknown and name the missing setup prerequisite.
Decision flow
Classify scope and selected surface before any probe.
Query only the selected surface. Preserve raw records, errors, exact GPU, release, interpreter/binary identities, argv, and evidence classification.
Resolve the most exact live product identity using the device-tree paths and NUL handling in capability-queries.md, then cross-check it against NVIDIA's current Video Encode and Decode Support Matrix and the release-matched SDK 13.0 NVENC or NVDEC application note. Never infer SKU from memory, engine count, capability fields, or operation behavior. Preserve source URL, retrieval date, table title, exact row/column labels, and cell value, using that reference's manual capture record. Generic family identity stays non-exact; use candidate-row consensus only when every authenticated candidate agrees.
Report documentation
Noas the final unsupported product verdict even if an API or diagnostic operation is positive; this ends the normal availability check. DocumentationYesestablishes documented support; live availability additionally needs the matching authenticated operation. Missing, unretrievable, or conflicting documentation remains unknown. When documentation is unknown, do not present positive capability fields as available options: label each affected codecunknownbeside them and state the retrieval failure with the verdict.For a PyNvVideoCodec encode-availability operation, request setup's
full-samplesprofile and carry its exact interpreter into the recipe and pipeline stages. Do not select the smaller decode-performance/smoke profile.Run an exact operation only when the user requests live availability and documentation is positive or unknown, or explicitly requests a diagnostic despite a negative verdict. A diagnostic under documentation
Noreports onlyoperation_verifiedoroperation_failedfor that exact tuple and never changes the unsupported product verdict. A tuple is the exact surface, GPU, codec, profile, chroma, bit depth, dimensions, input format, and control set tested. Resolve one recipe withjetson-video-recipe, then usejetson-video-pipelinefor encode followed by independent decode of the exact output identity. A failed tuple never generalizes to the product.
Evidence rules
GetEncoderCapssuccess iscapability_reported,supported=null,operation_status=not_tested.GetDecoderCapsbIsSupported=1/0records raw API true/false and whether returned limits apply; neither value is the documentation verdict.Missing enums, calls, fields, prerequisites, or nonzero-GPU authority are unknown, not unsupported.
Native
-ec/-dctext isofficial_sample_report; preserve raw values and never rewrite them as API or product claims.When reporting Main10 support, note that NVENC can convert verified 8-bit input internally and that P010 is the exact no-input-bit-depth-conversion path; a live claim still requires an authenticated 10-bit operation.
AV1 output is operation evidence only after the pipeline's package-owned decoder consumes the exact artifact and reports the expected frame count.
For a VP9 encode question, report only that VP9 remains a decoder family and no released NVENC route exists. Do not add a generic list of other encoders or offer a diagnostic encode operation.
Presets, tuning, package presence, throughput, and successful concurrent streams do not establish codec support or NVENC/NVDEC engine count.
Codec/API capability, NVENC/NVDEC availability, and successful bounded operations do not establish DMA-BUF, NvSciBuf, CUDA-memory sharing, zero copy, or cross-stage synchronization compatibility.
For a Python API query, create a short task-local program from this Markdown and the installed public SDK, run it with the exact selected interpreter, and keep its raw output with the result. Installation belongs to setup and operation validation belongs to pipeline. This skill owns engine queries, classification, documentation reconciliation, and the compact direct report above.
Files
- ATTRIBUTION.mdAdded by AutoGineer933 B
- BENCHMARK.md7.6 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md7.6 KiB
- evals/evals.json3.5 KiB
- references/capability-queries.md9.4 KiB
- references/surface-selection-contract.md6.0 KiB
- skill-card.md4.5 KiB
- skill.oms.sig5.0 KiB
skill-card.md
Description: <br>
Use when Jetson codec, profile, chroma, bit-depth, dimension, engine-count, or operational support must be reconciled from live APIs, authenticated NVIDIA samples, and NVIDIA documentation; also applies the content-DRM scope. <br>
This skill is ready for commercial/non-commercial use. <br>
Owner
NVIDIA <br>
License/Terms of Use: <br>
Apache-2.0 <br>
Use Case: <br>
Developers and engineers use this skill to query, classify, and reconcile Jetson hardware video codec capabilities from live APIs, authenticated NVIDIA samples, and official NVIDIA documentation. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <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>
Skill Output: <br>
Output Type(s): [Analysis] <br> Output Format: [Markdown] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <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>
5 evaluation tasks (5 positive) with 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 answer is correct against the reference answer. <br>
Discoverability: Whether the right skill was loaded when needed: skill selection, decoy avoidance, and workflow execution. <br>
Effectiveness: Whether the skill helped complete the task: goal completion (50%) and expected workflow adherence (50%). <br>
Efficiency: Whether wasted tool calls and token usage were avoided: tool-call productivity (50%) and token efficiency (50%). <br>
Underlying evaluation signals used in this run: <br>
security: Unsafe operations, secret leakage, and unauthorized access. <br>accuracy: Final-answer correctness against the reference answer. <br>skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <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 (legacy wire id; routing scored under Discoverability). <br>token_efficiency: Actual uncached prompt plus completion usage. <br>
Evaluation Results: <br>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 90.0% | 88.6% |
| Security | 100.0% → 100.0% (±0.0 points) | 100.0% → 100.0% (±0.0 points) |
| Correctness | 90.0% → 96.0% (+6.0 points) | 86.7% → 96.0% (+9.3 points) |
| Discoverability | 96.0% | 93.0% |
| Effectiveness | 55.3% → 63.0% (+7.7 points) | 69.4% → 75.3% (+5.9 points) |
| Efficiency | 95.0% | 78.8% |
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-capability · 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-capability.zip