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jetson-video-capability

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.

Install / Use

npx skills add NVIDIA/skills --skill jetson-video-capability

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Supported Platforms

Universal

Our assessment of jetson-video-capability

jetson-video-capability scores 85/100 on our quality scale, 457th of 770 Content & Media skills we index.

Its SKILL.md is 7.6 KB long, split into 5 sections and no code examples: a thorough specification that gives an agent plenty to work with.

With 3,421 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
11/20
Description
15/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 5 days ago, so jetson-video-capability is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

jetson-video-capability compared with similar skills

All 4 of these similar skills score higher than jetson-video-capability; compare them before choosing.

SkillScoreStarsUpdatedFormat
jetson-video-capability (this skill)by NVIDIA853.4k5d agoSKILL.md
Agent-Reachby Panniantong10086.0k13d agoCLAUDE.md
headroomby headroomlabs-ai10074.0ktodayCLAUDE.md
crawl4aiby unclecode10084.4k3d agoMCP Server
Scraplingby D4Vinci10084.4ktodayMCP Server

Frequently asked questions

How do I install jetson-video-capability?
Run npx skills add NVIDIA/skills --skill jetson-video-capability. The install tabs above show the steps for each supported agent.
Which AI agents does jetson-video-capability work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is jetson-video-capability safe to use?
It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is jetson-video-capability still maintained?
The repository was last updated 5 days ago, so jetson-video-capability is actively maintained.

name: jetson-video-capability license: "Apache-2.0" description: >- 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. metadata: author: "Vinit Bansal vinitkumarb@nvidia.com" tags: [jetson, video-codec-sdk, pynvvideocodec, nvenc, nvdec, capability] languages: [markdown] data-classification: public

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.

Related Skills

View on GitHub
GitHub Stars3.4k
CategoryContent
Updated5d ago
Forks412

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions