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…
Skill by Vinit Bansal <vinitkumarb@nvidia.com>
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/nvidia-jetson-video-capability
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About
Use cases: writing, documentation
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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/jetson-video-capability
What's inside
- Skill name
jetson-video-capability- 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.
- License
- Apache-2.0
- Metadata
- author: Vinit Bansal <vinitkumarb@nvidia.com> · data-classification: public
- Instructions
- 124 lines in SKILL.md · view on GitHub
Files (7)
- BENCHMARK.md
- SKILL.md
- evals.json · evals/
- capability-queries.md · references/
- surface-selection-contract.md · references/
- skill-card.md
- skill.oms.sig
SKILL.md (first lines)
# 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](references/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](references/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](references/capability-queries.md#documentation-reconciliation), then cross-check it against NVIDIA's current [Video Encode and Decode Support Matrix](https://developer.nvidia.com/video-encode-decode-support-matrix) and the release-matched SDK 13.0 [NVENC](https://docs.nvidia.com/video-technologies/video-codec-sdk/13.0/nvenc-application-note/index.html) or [NVDEC](https://docs.nvidia.com/video-technologies/video-codec-sdk/13.0/nvdec-application-note/index.html) 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
From NVIDIA/skills, Apache-2.0.
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Signals
- Stars
- 3,718
- Forks
- 457
- Open issues
- 8
- Last commit (this folder)
- Sep 25, 2026 (1 month ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/NVIDIA/skills
- Website
- docs.nvidia.com/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
jetson-video-capability skill. Agent Mart. https://agentmart.dev/skills/nvidia-jetson-video-capability (last updated 2026-10-24)