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
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-capabilityEvaluation date: 2026-09-16
Evaluator version:
1.5.6Agents: 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-sandboxTier 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
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 90.0% — baseline ran, but no comparable score was available; uplift unavailable | 88.6% — baseline ran, but no comparable score was available; uplift unavailable |
| 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% — baseline ran, but no comparable score was available; uplift unavailable | 93.0% — baseline ran, but no comparable score was available; uplift unavailable |
| Effectiveness | 55.3% → 63.0% (+7.7 points) | 69.4% → 75.3% (+5.9 points) |
| Efficiency | 95.0% — baseline ran, but no comparable score was available; uplift unavailable | 78.8% — 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.
| Agent | Dataset case | With skill | Without skill | Delta | Change | Coverage |
|---|---|---|---|---|---|---|
| claude-code | All cases | 564,852 | 427,829 | N/A | N/A | skill 5/5; base 6/6 |
| claude-code | capability-av1 | 149,204 | 91,965 | +57,239 | +62.24% | skill 1/1; base 1/1 |
| claude-code | capability-codec-not-buffer-sharing | 62,366 | 31,795 | +30,571 | +96.15% | skill 1/1; base 1/1 |
| claude-code | capability-decoders | 145,329 | 153,869 | -8,540 | -5.55% | skill 1/1; base 1/1 |
| claude-code | capability-drm-boundary | 61,468 | 60,589 | N/A | N/A | skill 1/1; base 2/2 |
| claude-code | capability-encoders | 146,485 | 89,611 | +56,874 | +63.47% | skill 1/1; base 1/1 |
| codex | All cases | 292,165 | 227,303 | N/A | N/A | skill 5/5; base 6/6 |
| codex | capability-av1 | 40,021 | 48,594 | -8,573 | -17.64% | skill 1/1; base 1/1 |
| codex | capability-codec-not-buffer-sharing | 53,247 | 31,946 | +21,301 | +66.68% | skill 1/1; base 1/1 |
| codex | capability-decoders | 123,782 | 67,516 | N/A | N/A | skill 1/1; base 2/2 |
| codex | capability-drm-boundary | 28,711 | 18,017 | +10,694 | +59.36% | skill 1/1; base 1/1 |
| codex | capability-encoders | 46,404 | 61,230 | -14,826 | -24.21% | skill 1/1; base 1/1 |
| ALL AGENTS | Dataset aggregate | 857,017 | 655,132 | N/A | N/A | skill 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
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | PASSED WITH OBSERVATIONS | 11 validator(s); 11 finding(s) |
| Tier 2 | Semantic deduplication | PASSED | 2 validator(s); 0 finding(s) |
| Tier 3 | Live agent evaluation | PASS | 2 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>
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | security (100%) |
| Correctness | Is the answer correct? | accuracy (100%) |
| Discoverability | Was the right skill loaded when needed? | skill_execution (100%) |
| Effectiveness | Did the skill help complete the task? | goal_accuracy (50%) + behavior_check (50%) |
| Efficiency | Did 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_efficiencywire 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