复制安装命令
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Where to file an issue depends on what's broken:
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md — the skill instructions consumed by the agentskill-card.md — skill identity and governance cardskill.oms.sig — detached OMS signature (verifiable against nv-agent-root-cert.pem)evals/evals.json, evals/*.json, eval/*.json, or benchmark/evals.jsonBENCHMARK.md — generated benchmark report capturing verifiable uplift dataVerify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig — detached OMS-format signature (verifiable against nv-agent-root-cert.pem)skill-card.md — skill identity and governance cardevals/evals.json, evals/*.json, eval/*.json, or benchmark/evals.jsonWhen evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
This repository adheres to the Agent Skills specification:
SKILL.md file at their root.name and description fields.skills-ref reference library.Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
name: jetson-video-pipeline
license: "Apache-2.0"
description: >-
Use when executing and verifying Jetson Video Codec SDK or PyNvVideoCodec
encode/decode, transcode, segmentation, container decode, AV1, or acceptance
workflows with exact artifact handoffs.
metadata:
author: "Vinit Bansal <vinitkumarb@nvidia.com>"
tags: [jetson, video-codec-sdk, pynvvideocodec, pipeline, nvenc, nvdec]
languages: [python]
data-classification: publicExecute official-sample codec stages and prove that every consumer used the exact artifact produced by the preceding stage. Use this skill for encode-then-decode verification, native H.264-to-HEVC transcode, PyNvVideoCodec segments, container decode triage, AV1 operation verification, or a compact customer acceptance package.
nvcodec-environment identity from jetson-video-setup is
optional. When supplied it is authoritative, and invalid or stale evidence
fails closed without local fallback. The agent may obtain it from setup's
public read-only probe; it need not be supplied in the customer's prompt.pynvc_interpreter; never scan for a venv. Before asking for that path,
invoke setup's public probe when that skill is installed and inspect its
typed result.
These read-only checks install, repair, register, and smoke-test nothing.jetson-video-recipe and one of its
validated schema-2 recipes. If its canonical public CLI is present, invoke
it; if absent, preserve dependency_required, name that skill, and tell the
user to install it and retry the stage. Recipe-free decode/segmentation
routes do not acquire that dependency.capability_report is an optional encode-request member, never a required
one. The established authority for PyNvVideoCodec encoder capabilities is the
capabilities block of the schema-1.2 nvcodec-environment artifact; an
encode request that omits capability_report is fully supported and reads
that block. When capability-owned freshness is wanted, a request may
additionally carry a schema-1.0 nvcodec-encoder-capability-report produced
by jetson-video-capability from the same environment artifact. When
supplied, that report becomes the selected Py encoder API evidence for the
check; it does not replace the environment artifact or its readiness facts.
Do not add the member to an independently constructed request merely because
the pynvc surface may be selected. It is optional on either surface, affects
Py capability classification only, and should be omitted for native; absence
never fails.advanced/decode.py, including
encode/decode, segmentation, and Py container triage, require a separately
validated full-samples venv. The default pynvc-smoke environment is a
setup-readiness proof and must block these routes before workspace creation;
return a structured jetson-video-setup dependency and provision a new
full-samples venv rather than upgrading it in place. If that skill is absent,
tell the user to install it before retrying.encode controller has one narrower consumer exception:
jetson-video-capability may bind setup's deterministic one-frame raw
fixture for an exact bounded capability smoke operation. That result is
operation evidence only, never representative pipeline or performance proof.Recipe-free decode and segmentation routes require no sibling when the selected
SDK prerequisites already exist. Add jetson-video-recipe only for a
recipe-bearing stage, jetson-video-benchmark only for requested performance,
jetson-video-setup only for installation, repair, or one read-only handoff
when registered Python authority is required, and
jetson-video-capability only for a requested support verdict or fresh
acceptance capability artifact. Use the agent runtime's installed-skill catalog
before each stage; do not scan arbitrary directories. If the sibling is
present, read its SKILL.md and invoke its
documented public entry point; pass artifacts as data and never import sibling
code. If it is absent, preserve completed stages and artifacts and say, using
the actual names: I can run <stage>, but it requires <skill>, which is not installed. Install <skill> and retry this stage. Never promote a partial
workflow to complete or require an optional sibling.
Apply the scope boundary. For a request
solely for objective quality metrics, including PSNR or SSIM, state only
that this skill does not provide them and that a separately authorized
quality workflow is required, then stop. Do not name or recommend an
external tool, and do not offer to configure or run the comparison. For a
request limited to capture, transport, AI, display, or glass-to-glass
latency, state that those stages are outside this codec skill and stop
without naming, recommending, or offering another tool or workflow.
Otherwise proceed immediately to the media gate in step 2; choose
encode_decode, native_transcode, pynvc_segments, container_triage,
av1_verify, or acceptance only after that gate clears.
For every remaining request to plan, dry-run, or execute a pipeline route,
including “plan only” or “do not run”, apply this gate
before route selection and before prerequisite, sibling, reference, or
script inspection. Do not decompose a media-gated pipeline request into a
media-free recipe subtask. If media is missing, return input_required and
stop before target probing, browsing, retrieval, authentication, dry run, or
operation launch. Ask only for the missing media at this gate; do not also
inspect controller help, describe or plan the route, list future stages or
handoffs, or request an interpreter, environment, recipe, or later-stage
field. The complete response at this terminal gate consists only of
input_required and one request for an exact target-local media path or
user-supplied HTTP(S) URL. Never choose substitute media. The
capability-smoke exception above applies only to the direct encode
controller and must not be promoted to pipeline completion.
Canonicalize and hash an exact local input. For URL input, preserve the exact user-supplied URL, then retrieve, canonicalize, and hash it only after target eligibility, authorization, and runtime-authority gates pass.
Preserve explicit native, pynvc, or both. Treat “whichever”, “best
available”, “choose for me”, and other unspecified-surface wording as
auto, never as both. Reserve both for an explicit request to run or
compare both surfaces.
After the input gate and surface classification, select exactly one runtime
authority for each selected surface. If the caller supplies a setup
environment identity, validate and bind that exact artifact to dry-run and
execute; never ignore it or substitute a local fallback. If PyNvVideoCodec
may participate and neither an environment nor exact interpreter was
supplied, invoke installed jetson-video-setup through its public read-only
probe_nvcodec.py: use --runtime pynvc for explicit Python or --runtime both for both/auto, a fresh --output, and never
--setup-candidate. Inspect the fresh artifact; only a live artifact whose
selected Py surface is installed and whose pynvc.identity.status is
verified is usable. Snapshot that exact file as the controller's portable
environment identity with exactly schema_version, kind, canonical
absolute path, size_bytes, and lowercase sha256; do not import sibling
code or pass a blocked probe as authority. If setup is absent or reports any
not-ready, unreadable, stale, binding, or launch failure, ask for and supply
the exact pynvc_interpreter only for explicit pynvc/both. For auto,
keep Py not_evaluated and continue only an eligible native surface. The
controller derives a private local binding, never accepts that binding from
a request, and revalidates it before launch. If local authentication fails,
use setup for only that exact surface when installed.
Apply the auto gate using only the selected runtime authority: zero
eligible surfaces block, one runs, and two return selection_required;
never rank the surfaces in this gate. With two eligible surfaces, this gate
is unconditional: do not search old results or benchmark to make the choice.
Ask for exactly native, pynvc, or both, then stop
before dry run or launch. Never trust a prompt's statement that a surface is
ready: establish eligibility from the supplied or freshly probed authority.
Without setup evidence or an exact local pynvc_interpreter, record
PyNvVideoCodec as not_evaluated with the retry action; do not let that
optional peer block an otherwise eligible native auto route. Explicit
pynvc or both still requires one of those two authorities.
Before any codec launch, authenticate each selected executable from the installed Video Codec SDK package or each Python sample from the selected wheel and interpreter. Use only those authenticated NVIDIA sample routes; if none can satisfy a stage, report that stage blocked.
For a multi-stage pipeline request, compose only the required siblings.
If a requested performance stage needs jetson-video-benchmark, invoke its
installed public controller; if absent, preserve completed pipeline stages
and report that the benchmark stage is dependency_required with an
install-and-retry action. Run pipeline dry_run, review the
complete recipe and sample arguments, then run execute with fresh result
and workspace paths. Invoke this skill's public controller directly:
python3 -I {baseDir}/scripts/pipeline_controller.py \
--request request.json --workspace fresh-workspace \
--output result.json
A single encode-then-independent-decode request invokes
scripts/encode_controller.py with the same three arguments. That
controller is execution-only: validate and review its recipe and request
envelope first, then invoke it once with fresh output and workspace paths;
do not claim it performed an internal dry run.
Require exact positive markers and counts, no explicit failure marker, and fresh nonempty outputs. Reopen and rehash every original handoff. An independent decoder must consume the exact producer path, size, and SHA-256 and produce the expected frames.
For native transcode, accept exactly one authenticated AppTrans completion
marker in either released form: legacy (#totFrames=N) or current
Total frame transcoded: N. Reject missing, duplicate, or mixed markers.
Preserve each segment or surface result independently. A failed peer yields an honest partial result rather than summary-level completion.
For acceptance, let the controller validate and write its nine physical
pre-seal files and return seal_pending: true; those are distinct from the
reference keys and stage rows. Keep large media/build artifacts external,
then have the agent create the checksum manifest last—the controller does
not create it.
Invoke each public script directly in isolated mode:
python3 -I {baseDir}/scripts/encode_controller.py --help
python3 -I {baseDir}/scripts/pipeline_controller.py --help
python3 -I {baseDir}/scripts/validate_representative_content_summary.py --help
| Script | Purpose | Arguments |
|---|---|---|
scripts/encode_controller.py | Execute one recipe-bound encode followed by independent decode. | --request, --workspace, and --output. |
scripts/pipeline_controller.py | Dry-run or execute the six multi-stage pipeline routes. | --request, --workspace, and --output. |
scripts/validate_representative_content_summary.py | Rehash external media and validate compact content metadata without modifying it. | Inspect --help for summary/input arguments. |
input_required, selection_required, blocked, partial, and
failed rather than claiming a complete pipeline.
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