Generated banner for Jetson Video Capability
Generated logo for Jetson Video Capability

Jetson Video Capability

NVIDIAOfficialCreated by Vinit BansalDistributed by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk

From the author

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.
— the description by Vinit Bansal

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/jetson-video-capability/download
  2. Unzip 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

  1. Classify scope and selected surface before any probe.

  2. Query only the selected surface. Preserve raw records, errors, exact GPU, release, interpreter/binary identities, argv, and evidence classification.

  3. 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.

  4. Report documentation No as the final unsupported product verdict even if an API or diagnostic operation is positive; this ends the normal availability check. Documentation Yes establishes 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 codec unknown beside them and state the retrieval failure with the verdict.

  5. For a PyNvVideoCodec encode-availability operation, request setup's full-samples profile and carry its exact interpreter into the recipe and pipeline stages. Do not select the smaller decode-performance/smoke profile.

  6. 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 No reports only operation_verified or operation_failed for 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 with jetson-video-recipe, then use jetson-video-pipeline for encode followed by independent decode of the exact output identity. A failed tuple never generalizes to the product.

Evidence rules

  • GetEncoderCaps success is capability_reported, supported=null, operation_status=not_tested.

  • GetDecoderCaps bIsSupported=1/0 records 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/-dc text is official_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

BENCHMARK.md

Skill Benchmark: jetson-video-capability

✅ 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-capability

  • 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: 5 evaluation tasks (5 positive)

  • Dataset digest: sha256:4954ec1cc526996669be4072c5d1a20bbbed003c6d01b75c541838c4a1161eb5 (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)
Overall90.0% — baseline ran, but no comparable score was available; uplift unavailable88.6% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness90.0% → 96.0% (+6.0 points)86.7% → 96.0% (+9.3 points)
Discoverability96.0% — baseline ran, but no comparable score was available; uplift unavailable93.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness55.3% → 63.0% (+7.7 points)69.4% → 75.3% (+5.9 points)
Efficiency95.0% — baseline ran, but no comparable score was available; uplift unavailable78.8% — 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 cases564,852427,829N/AN/Askill 5/5; base 6/6
claude-codecapability-av1149,20491,965+57,239+62.24%skill 1/1; base 1/1
claude-codecapability-codec-not-buffer-sharing62,36631,795+30,571+96.15%skill 1/1; base 1/1
claude-codecapability-decoders145,329153,869-8,540-5.55%skill 1/1; base 1/1
claude-codecapability-drm-boundary61,46860,589N/AN/Askill 1/1; base 2/2
claude-codecapability-encoders146,48589,611+56,874+63.47%skill 1/1; base 1/1
codexAll cases292,165227,303N/AN/Askill 5/5; base 6/6
codexcapability-av140,02148,594-8,573-17.64%skill 1/1; base 1/1
codexcapability-codec-not-buffer-sharing53,24731,946+21,301+66.68%skill 1/1; base 1/1
codexcapability-decoders123,78267,516N/AN/Askill 1/1; base 2/2
codexcapability-drm-boundary28,71118,017+10,694+59.36%skill 1/1; base 1/1
codexcapability-encoders46,40461,230-14,826-24.21%skill 1/1; base 1/1
ALL AGENTSDataset aggregate857,017655,132N/AN/Askill 10/10; base 12/12

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); 11 finding(s)
Tier 2Semantic deduplicationPASSED2 validator(s); 0 finding(s)
Tier 3Live agent evaluationPASS2 agent(s); 5 task(s)

Findings and Observations

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

  • MEDIUM QUALITY/quality_efficiency: Deeply nested references in capability-queries.md (skills/jetson-video-capability/SKILL.md)

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

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

  • MEDIUM SECURITY/Autonomous Decision Making (EA2): Excessive Agency: without asking (references/surface-selection-contract.md:28)

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

  • 6 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-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