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Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.

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项目 README

来源文件:README.md

抓取于 2026年8月10日

NVIDIA Agent Skills

Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.

NVIDIA Agent Skills Spec License

📖 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.


Quickstart

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 skills CLI (v1.5.16 or newer). Installing via npx skills@latest add nvidia/skills always 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.

Install One Skill Without Prompts

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.

Install for a Specific Agent

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

Keep Skills Up to Date

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.

Browse the Catalog

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.


Skill Catalog

ProductDescriptionSkills
AIQNVIDIA AI-Q Blueprint - deploy local AI-Q services and run shallow or deep research workflows as agent skills.aiq-research, aiq-deploy
CUDA-QCUDA Quantum — onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.cudaq-guide
cuDFOfficial NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.accelerated-computing-cudf
cuOptGPU-accelerated optimization — vehicle routing, linear programming, quadratic programming, installation, server deployment, and developer tools.cuopt-install, cuopt-multi-objective-exploration, cuopt-numerical-optimization-api, cuopt-numerical-optimization-formulation, cuopt-routing-api-python, cuopt-server-api-python
cuPyNumericNumPy and SciPy on multi-node multi-GPU systems — skills to help with installing cuPyNumeric, migrating existing NumPy code, and doing parallel I/Ocupynumeric-hdf5, cupynumeric-install, cupynumeric-migration-readiness, cupynumeric-parallel-data-load
DALIGPU-accelerated data loading and processing with NVIDIA DALI.dali-dynamic-mode
Data DesignerBuild declarative synthetic dataset generation pipelines with NeMo Data Designer.data-designer
DeepStreamAgentic skills for guided DeepStream development.amc-run-sample-calibration, amc-run-video-calibration, amc-setup-calibration-stack, deepstream-dev, deepstream-generate-pipeline, deepstream-import-vision-model, deepstream-profile-pipeline, deepstream-sop
Digital HealthAgent skills for the clinical ASR evaluation flywheel — term curation, synthetic clinical-speech benchmark generation, KER (Keyword Error Rate) scoring, and fine-tune guidance.digital-health-clinical-asr-setup, digital-health-clinical-asr-build, digital-health-clinical-asr-eval, digital-health-clinical-asr-finetune
DOCATeach AI agents to use the NVIDIA DOCA SDK on BlueField DPUs and ConnectX NICs — setup, libraries, services, tools, deployment, and debugging.doca-bare-metal-deployment, doca-bf3-deployment, doca-bf4-deployment, doca-collectx-deployment, doca-container-deployment, doca-debug, doca-hardware-safety, doca-programming-guide, doca-public-knowledge-map, doca-setup, doca-structured-tools-contract, doca-upgrade, doca-version, doca-aes-gcm, doca-argp, doca-comch, doca-common, doca-compress, doca-devemu, doca-dma, doca-dpa, doca-dpdk-bridge, doca-erasure-coding, doca-eth, doca-flow, doca-flow-dpa-provider, doca-gpi, doca-gpunetio, doca-mgmt, doca-pcc, doca-pcc-ztr-rttcc-algo, doca-rdma, doca-rdmi, doca-rmax, doca-sha, doca-sta, doca-telemetry, doca-telemetry-exporter, doca-urom, doca-verbs, doca-argus, doca-dms, doca-firefly, doca-urom-svc, doca-bench, doca-bench-extension, doca-caps, doca-comm-channel-admin, doca-dpa-hl-tracer, doca-flow-dpa-perf, doca-flow-grpc-server, doca-flow-perf, doca-flow-tune, doca-gpunetio-ib-write-bw, doca-gpunetio-ib-write-lat, doca-pcc-counters, doca-sha-offload-engine, doca-socket-relay, doca-spcx-cc, doca-telemetry-utils
DynamoNVIDIA Dynamo deployment bring-up on Kubernetes — pick and deploy recipes, start router modes, validate disagg NIXL/UCX/NCCL interconnect, and triage day-2 failures.dynamo-interconnect-check, dynamo-recipe-runner, dynamo-router-starter, dynamo-troubleshoot
Earth2StudioOpen-source deep-learning framework for exploring, building and deploying AI weather/climate workflows.earth2studio-create-datasource, earth2studio-create-diagnostic, earth2studio-create-prognostic, earth2studio-data-fetch, earth2studio-deterministic-forecast, earth2studio-discover, earth2studio-install
HoloHubBuild, run, debug, benchmark, and develop HoloHub applications and Holoscan Modules with validated lifecycle workflows.holohub-app-lifecycle, holohub-debug-build-run, holohub-module-lifecycle
Holoscan SDKInstall and set up the Holoscan SDK on any platform (container, Debian, Python, Conda, or source).holoscan-install-debian, holoscan-install-source, holoscan-install-wheel, holoscan-install-conda, holoscan-install-container, holoscan-setup
Holoscan Sensor BridgeAgent-ready skills for Holoscan Sensor Bridge devkit workflows, including demo environment bring-up, FPGA flashing for Lattice and VB1940 hardware, example application execution, QA test-plan automation, and support for configuring and using the Holoscan Sensor Bridge FPGA intellectual property (IP) core.hsb-setup, hsb-flash, hsb-app, hsb-test, hsb-ip-def, hsb-ip-packetizer, hsb-ip-create-top
Isaac for Healthcare WorkflowsAgent-ready skills for Isaac for Healthcare agentic and catheter-navigation workflows, covering task authoring, data pipelines, policy training and validation, CT-derived digital twins, DRR rendering, and interactive catheter simulation.i4h-workflow, i4h-workflow-setup, i4h-workflow-create, i4h-workflow-scene-edit, i4h-workflow-dataset-teleop, i4h-workflow-dataset-replay, i4h-workflow-dataset-mimic, i4h-workflow-dataset-annotate, i4h-workflow-dataset-convert, i4h-workflow-finetune, i4h-workflow-validate, i4h-workflow-e2e, i4h-lerobot-viz, i4h-catheter-navigation, i4h-catheter-navigation-setup, i4h-catheter-navigation-digital-twin, i4h-catheter-navigation-render-drr, i4h-catheter-navigation-viewport, i4h-catheter-navigation-smoke, i4h-catheter-navigation-e2e
Jetson BSPAgentic skills for setting up and customizing an NVIDIA Jetson Linux Board Support Package (BSP) — pick a target, prepare image and sources, customize IO (camera, PCIe, USB, pinmux, clocks, and more), then promote, flash, and validate.jetson-build-source, jetson-customize-camera, jetson-customize-clocks, jetson-customize-fan, jetson-customize-mgbe, jetson-customize-nvpmodel, jetson-customize-pcie, jetson-customize-pinmux, jetson-customize-uphy, jetson-customize-usb, jetson-derive-carrier, jetson-download-bsp, jetson-flash-image, jetson-generate-kb, jetson-init-image, jetson-init-source, jetson-init-target, jetson-link-docs, jetson-optimize-memory, jetson-print-bsp-info, jetson-promote-image, jetson-quick-start, jetson-set-target, jetson-validate-image
Jetson DeviceDevice-side agent skills for working with a live NVIDIA Jetson after boot — diagnostics, memory auditing, headless setup, inference memory tuning, LLM serving and benchmarking, packaging guidance, and speculative decoding.jetson-diagnostic, jetson-headless-mode, jetson-inference-mem-tune, jetson-llm-benchmark, jetson-llm-serve, jetson-memory-audit, jetson-package, jetson-print-device-info, jetson-speculative-decoding
Medical AI SkillsAgent-ready medical AI skills built on MONAI for DICOM handling, NVIDIA-hosted medical imaging model workflows, segmentation, synthesis, and evidence-oriented evaluation.dicom-metadata-extract, dicom-series-preflight, dicom-series-to-volume, nv-generate-ct-rflow, nv-generate-mr, nv-generate-mr-brain, nv-generate-mr-brain-finetune, nv-generate-vae-finetune, nv-reason-cxr, nv-segment-ct, nv-segment-ct-finetune, nv-segment-ctmr
Megatron-CoreLarge-scale distributed training — model parallelism, pipeline parallelism, and mixed precision.mcore-create-issue, mcore-linting-and-formatting, mcore-run-on-slurm, mcore-split-pr, mcore-testing
NeMo AutoModelNeMo AutoModel - PyTorch-native distributed training for LLMs/VLMs with Hugging Face support, recipes, launchers, and validation workflows.nemo-automodel-distributed-training, nemo-automodel-launcher-config, nemo-automodel-model-onboarding, nemo-automodel-recipe-development
NeMo MBridgeNeMo MBridge - PyTorch-native bridge between Hugging Face and Megatron-Core for checkpoint conversion, training recipes, and NVIDIA GPU performance workflows.nemo-mbridge-mlm-bridge-training, nemo-mbridge-multi-node-slurm, nemo-mbridge-perf-activation-recompute, nemo-mbridge-perf-cpu-offloading, nemo-mbridge-perf-cuda-graphs, nemo-mbridge-perf-expert-parallel-overlap, nemo-mbridge-perf-hierarchical-context-parallel, nemo-mbridge-perf-megatron-fsdp, nemo-mbridge-perf-memory-tuning, nemo-mbridge-perf-moe-comm-overlap, nemo-mbridge-perf-moe-dispatcher-selection, nemo-mbridge-perf-moe-hardware-configs, nemo-mbridge-perf-moe-long-context, nemo-mbridge-perf-moe-optimization-workflow, nemo-mbridge-perf-moe-vlm-training, nemo-mbridge-perf-parallelism-strategies, nemo-mbridge-perf-sequence-packing, nemo-mbridge-perf-tp-dp-comm-overlap, nemo-mbridge-recipe-recommender, nemo-mbridge-resiliency
NeMo PlatformNeMo Platform brings NVIDIA NeMo libraries together under one CLI, Python SDK, and web UInemo-evaluator-plugin, nemo-data-designer-plugin
NeMo RelaySkills to help get started and use NeMo Relay - a runtime for instrumenting and controlling AI agents across harnesses, applications, and frameworks.nemo-relay-install, nemo-relay-get-started, nemo-relay-instrument-calls, nemo-relay-instrument-context-isolation, nemo-relay-instrument-typed-wrappers, nemo-relay-plugin-adaptive-tuning, nemo-relay-plugin-build, nemo-relay-plugin-observability, nemo-relay-migrate-from-flow, nemo-relay-debug-runtime-integration
NeMo RetrieverNeMo Retriever - deploy NeMo Retriever Library locally, extract information from corpus of data, and answer questions against the corpus.nemo-retriever
NeMo-RLRLHF training on Ray — GRPO, DPO, and SFT for LLMs and VLMs with FSDP2 and Megatron-Core.launch-nemo-rl, nemo-rl-auto-research, nemo-rl-brev-etiquette, nemo-rl-docs, nemo-rl-session-memory
NemoClawSecure agent sandboxing — run OpenClaw inside NVIDIA OpenShell with managed inference, policy management, remote deployment, sandbox monitoring.nemoclaw-user-guide
NemotronAuthor end-to-end model development, customization, evaluation, and deployment pipelines using the NVIDIA AI stack.nemotron-customize, nemotron-retrieval-recipes, nemotron-policy-generator
Nemotron SpeechDeploy and operate NVIDIA Nemotron Speech (Riva) NIMs — ASR, TTS, and NMT, cloud-hosted via build.nvidia.com or self-hosted on your own GPU.nemotron-speech, nemotron-asr-finetune
Physical AIPhysical AI skills for simulation, synthetic data generation, training, validation and deployment and more.omniverse-cad-to-simready, omniverse-realtime-viewer, omniverse-usd-performance-tuning, physical-ai-infrastructure-setup-and-resilient-scaling, physical-ai-neural-reconstruction, physical-ai-defect-image-generation, physical-ai-video-data-augmentation, physical-ai-people-attribute-search
PhysicsNeMoNVIDIA PhysicsNeMo - Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods.physicsnemo-discover, physicsnemo-shard-tensor
Portfolio OptimizationGPU-accelerated Mean-CVaR portfolio optimization with NVIDIA cuOpt — CVaR optimization, efficient frontier, scenario generation, backtesting, and rebalancing.portfolio-optimization
RAG BlueprintRAG pipeline — deploy, configure, troubleshoot, and manage retrieval augmented generation with Docker Compose or Helm.rag-blueprint, rag-eval, rag-perf
Skill Card GeneratorReads an agent skill's source files and produces a skill card plus a review table. Use when a skill directory exists and a governance card needs to be generated or updated.skill-card-generator
TAO ToolkitNVIDIA TAO Toolkit - fine-tune and optimize 100+ pretrained vision AI models with your own data using low-code microservices, then export production-ready models for edge or cloud deployment.tao-analyze-changenet-rca, tao-finetune-huggingface-model, tao-port-huggingface-model, tao-run-automl, tao-run-automl-deft-pipeline, tao-run-deft-aoi, tao-run-inference-service, tao-train-single-step, paidf-anomalygen, tao-analyze-gaps-visual-changenet, tao-analyze-gaps-vlm-bcq, tao-convert-dataset-format, tao-generate-image-grounding, tao-generate-referring-expressions, tao-generate-video-reasoning-annotations, tao-mine-aoi-images, tao-route-visual-changenet-samples, tao-validate-dataset-format, tao-finetune-clip, tao-finetune-cosmos-embed, tao-finetune-cosmos-reason, tao-train-action-recognition, tao-train-bevfusion, tao-train-centerpose, tao-train-deformable-detr, tao-train-depth-anything-v2, tao-train-dino, tao-train-fast-foundation-stereo, tao-train-foundation-stereo, tao-train-grounding-dino, tao-train-image-classification, tao-train-mask-auto-encoder, tao-train-mask-auto-label, tao-train-mask-grounding-dino, tao-train-mask2former, tao-train-metric-learning-recognition, tao-train-nvdinov2, tao-train-nvpanoptix3d, tao-train-ocdnet, tao-train-ocrnet, tao-train-oneformer, tao-train-optical-inspection, tao-train-pointpillars, tao-train-pose-classification, tao-train-reid, tao-train-rtdetr, tao-train-segformer, tao-train-sparse4d, tao-train-visual-changenet, tao-run-on-brev, tao-run-on-docker, tao-run-on-kubernetes, tao-run-on-local-docker, tao-run-on-slurm, tao-run-platform, tao-setup-nvidia-gpu-host, tao-launch-workflow, tao-list-capabilities
TileGymTile-based GPU programming — adding new kernels, cross-framework conversion, and performance optimization.tilegym-adding-cutile-kernel, tilegym-converting-cutile-to-julia, tilegym-converting-cutile-to-triton, tilegym-cutile-autotuning, tilegym-cutile-python, tilegym-improve-cutile-kernel-perf, tilegym-monkey-patch-kernels-to-transformers
Video Search and SummarizationVSS Blueprint — deploy profiles, search and summarize video, generate analysis reports, manage alerts and incidents, query VIOS sensors, and use the RTVI VLM microservice.vss-ask-video, vss-deploy-dense-captioning, vss-deploy-detection-tracking-2d, vss-deploy-detection-tracking-3d, vss-deploy-profile, vss-deploy-video-embedding, vss-generate-video-calibration, vss-generate-video-report, vss-manage-alerts, vss-manage-video-io-storage, vss-query-analytics, vss-search-archive, vss-setup-behavior-analytics, vss-setup-video-analytics-api, vss-summarize-video

Getting Help & Contributing

Where to file an issue depends on what's broken:

  • Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
  • Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
  • Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
  • Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.

Per-product source repo links:

ProductIssuesDiscussionsContributingSecurity
AIQIssuesDiscussionsContributingSecurity
CUDA-QIssuesDiscussionsContributingSecurity
cuDFIssuesDiscussionsContributingSecurity
cuOptIssuesDiscussionsContributingSecurity
cuPyNumericIssues—Contributing—
DALIIssues—Contributing—
Data DesignerIssuesDiscussionsContributingSecurity
DeepStreamIssues—ContributingSecurity
Digital HealthIssues—ContributingSecurity
DOCAIssues—ContributingSecurity
DynamoIssuesDiscussionsContributingSecurity
Earth2StudioIssuesDiscussionsContributing—
HoloHubIssues—ContributingSecurity
Holoscan SDKIssues—ContributingSecurity
Holoscan Sensor BridgeIssues—Contributing—
Isaac for Healthcare WorkflowsIssues—ContributingSecurity
Jetson BSPIssues—ContributingSecurity
Jetson DeviceIssues—ContributingSecurity
Medical AI SkillsIssues—ContributingSecurity
Megatron-CoreIssuesDiscussionsContributing—
NeMo AutoModelIssuesDiscussionsContributingSecurity
NeMo MBridgeIssuesDiscussionsContributingSecurity
NeMo PlatformIssuesDiscussionsContributingSecurity
NeMo RelayIssuesDiscussionsContributingSecurity
NeMo RetrieverIssuesDiscussionsContributingSecurity
NeMo-RLIssuesDiscussionsContributingSecurity
NemoClawIssuesDiscussionsContributingSecurity
NemotronIssuesDiscussionsContributingSecurity
Nemotron SpeechIssues—ContributingSecurity
Physical AIIssues—ContributingSecurity
PhysicsNeMoIssuesDiscussionsContributingSecurity
Portfolio OptimizationIssuesDiscussionsContributingSecurity
RAG BlueprintIssuesDiscussionsContributingSecurity
Skill Card GeneratorIssues—ContributingSecurity
TAO ToolkitIssuesDiscussionsContributingSecurity
TileGymIssues—ContributingSecurity
Video Search and SummarizationIssuesDiscussionsContributingSecurity

For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.


Verifying Skills

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 agent
  • skill-card.md — skill identity and governance card
  • skill.oms.sig — detached OMS signature (verifiable against nv-agent-root-cert.pem)
  • A Tier-3 evaluation dataset — accepted at evals/evals.json, evals/*.json, eval/*.json, or benchmark/evals.json
  • BENCHMARK.md — generated benchmark report capturing verifiable uplift data

Verify 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.


Roadmap

  • ✅ Public skills catalog with NVIDIA-verified skills across multiple products
  • ✅ Automated sync pipeline with skills mirrored from product repos daily
  • ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
  • ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
  • ✅ Skills universal evaluation criteria and task-specific criteria
  • ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
  • ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
  • ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
  • 🔲 Syndication to additional MCP hubs and partner channels

Repository Structure

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 card
  • A Tier-3 evaluation dataset — accepted at evals/evals.json, evals/*.json, eval/*.json, or benchmark/evals.json

When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.


Standards & Compatibility

This repository adheres to the Agent Skills specification:

  • Skills are portable directories with a SKILL.md file at their root.
  • Metadata uses YAML frontmatter with required name and description fields.
  • Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
  • Validate your skill using the skills-ref reference library.

License

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.

DevOps 与部署数据与 AI

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:3 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/NVIDIA/skills.git
  3. 将 "skills/vss-manage-alerts" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/NVIDIA/skills.git
  3. 将 "skills/vss-manage-alerts" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/NVIDIA/skills.git
  3. 将 "skills/vss-manage-alerts" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/NVIDIA/skills.git
  3. 将 "skills/vss-manage-alerts" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/NVIDIA/skills.git
  3. 将 "skills/vss-manage-alerts" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: vss-manage-alerts
description: Use for VSS alert workflows — real-time monitoring, Alert-Bridge subscriptions, Slack notifications, incident queries, camera onboarding. Not for non-alert analytics.
license: Apache-2.0
metadata:
  version: "3.2.0"
  author: "NVIDIA Video Search and Summarization Team <vss-team@nvidia.com>"
  github-url: "https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization"
  tags: "nvidia blueprint operational"

Purpose

Operate the VSS alert pipeline (mode detection, Alert-Bridge subscriptions, Slack notifications, queries, camera onboarding, verifier-prompt customization).

Prerequisites

  • Active VSS deployment reachable on $HOST_IP (see vss-deploy-profile and references/).
  • NGC credentials in $NGC_CLI_API_KEY and $NVIDIA_API_KEY for any image pulls.
  • curl, jq, and Docker available on the caller.

Instructions

Follow the routing tables and step-by-step workflows below. Each section that ends in workflow, quick start, or flow is intended to be executed top-to-bottom. Detailed reference material lives in references/ and helper scripts live in scripts/ — call them via run_script when the skill points to a script by name.

Examples

Runnable end-to-end scenarios live under evals/ (each *.json manifest); inline curl blocks appear in each workflow below. Replay with nv-base validate <this-skill-dir> --agent-eval.

Limitations

Requires the matching VSS profile/microservice deployed and reachable. NGC-hosted models/NIMs are subject to rate-limits, GPU-memory needs, and license terms; concurrency and storage limits depend on host hardware and the profile's compose file.

Troubleshooting

  • Connection refused → microservice not running: probe /docs or /health, redeploy via vss-deploy-profile.
  • HTTP 401/403 on NGC pulls → missing/expired NGC_CLI_API_KEY: docker login nvcr.io and re-export the key.
  • OOM / model load failure → insufficient GPU memory: use a smaller variant or docker compose down to free GPUs.

VSS Alert Management

The alerts profile runs in one of two modes (chosen at /vss-deploy-profile -p alerts -m {verification,real-time}) — see The Two Modes table below. This skill routes by deployed mode + user intent (monitoring vs subscription CRUD vs Slack webhook).

When to Use

  • Start/stop a real-time alert on a sensor ("Start real-time alert for boxes dropped on warehouse_sample")
  • Create/list/stop realtime subscription rules on Alert Bridge
  • Set up or manage Slack incident notifications
  • List or query detected incidents / alerts; check verdicts (confirmed/rejected/unverified)
  • Add a new camera to the alerts pipeline; customize VLM-verifier prompts (CV mode)

Deployment prerequisite

Requires the VSS alerts profile on $HOST_IP in either verification (CV) or real-time (VLM) mode.

# Either vss-rtvi-cv (CV mode) OR vss-rtvi-vlm (VLM mode) must be present.
curl -sf --max-time 5 "http://${HOST_IP}:8000/docs" >/dev/null \
  && docker ps --format '{{.Names}}' \
     | grep -qE '^(vss-rtvi-cv|vss-rtvi-vlm)$'

If the probe fails, ask which mode to deploy and hand off to /vss-deploy-profile -p alerts -m <mode> (decline → stop; pre-authorized autonomous deploy → run directly with verification by default). If it passes, detect the mode per Step 1.


The Two Modes (Deploy-Time Choice)

ModeDeploy flagEnv (.env)What runsWhat is available
CV (verification)-m verificationMODE=2d_cvRT-CV (Grounding DINO) + Behavior Analytics + alert-bridge VLM verifier + rtvi-vlmBoth static CV pipeline (Workflow A) and dynamic VLM real-time alerts (Workflows B/D)
VLM (real-time)-m real-timeMODE=2d_vlmalert-bridge + rtvi-vlmOnly dynamic VLM real-time alerts (Workflows B/D) and alert-bridge backend. No static CV pipeline.

Switching modes uses the vss-deploy-profile teardown + deploy flow with the other -m flag (VLM → CV adds the CV pipeline; CV → VLM tears it down). rtvi-vlm runs in both modes.


Step 1 — Detect the Currently Deployed Mode

Before running any alert workflow, check which mode is live. Use CV-only containers as the signal — vss-rtvi-vlm is not a reliable mode signal because it runs in both modes.

# CV verification mode (vss-behavior-analytics + vss-rtvi-cv are CV-only)
docker ps --format '{{.Names}}' | grep -qx vss-behavior-analytics && echo "mode=CV"

# VLM real-time mode (no CV pipeline; vss-rtvi-vlm still runs)
docker ps --format '{{.Names}}' | grep -qx vss-behavior-analytics || \
  docker ps --format '{{.Names}}' | grep -qx vss-rtvi-vlm && echo "mode=VLM"

If vss-behavior-analytics is present → CV mode (which also has vss-rtvi-vlm). If only vss-rtvi-vlm is present (and no CV pipeline) → VLM mode. If neither matches, the alerts profile is not deployed — direct the user to the vss-deploy-profile skill.

Alternative signal (preferred when docker ps isn't accessible): check the profile's generated.env:

grep -E '^MODE=' deploy/docker/developer-profiles/dev-profile-alerts/generated.env
# MODE=2d_cv   → CV mode (full superset)
# MODE=2d_vlm  → VLM real-time mode (vss-rtvi-vlm only; no vss-rtvi-cv)

Step 2 — Route by Deployed Mode

Deployed modeUser asks about…Action
VLM real-timeSlack webhook setup/status/test/stopWorkflow E — references/alert-notify.md
VLM real-timerule CRUD, or a realtime alert on a sensor with a detection condition, or stop/delete a named alert (by alert_type/condition or rule ID)Workflow D — references/alert-subscriptions.md (incl. two-step stop/confirm)
CV verificationsubscription/rule CRUD or Slack/notification setupRefuse — see canonical refusal text below
CV or VLMgeneric start/stop monitoring without a detection conditionWorkflow B (VLM) — call the VSS Agent; rtvi-vlm runs in both modes
CV or VLMincident lookup / what happened (recent alerts, time-range, casual "any alerts today?")Workflow C (Query) — works on both; always run the query, never answer from memory
CVstatic CV alert onboarding / verdict-prompt customizationWorkflow A (CV) — onboard RTSP via vss-manage-video-io-storage; pipeline auto-picks it up
VLMa CV / behavior-analytics / PPE-rule alert needing the static CV pipelineRedeployment required — confirm first, then vss-deploy-profile -m verification

Always confirm before triggering a redeploy. A mode switch stops all currently-running monitoring and restarts services.

Intent precedence (first match wins)

  1. Workflow E (Slack) — Slack-specific keywords (slack, webhook + slack, bot token, slack channel). notify alone is not sufficient.
  2. Workflow D (Subscriptions) — sensor plus a detection condition, rule CRUD keywords (rule, subscription, rule ID), or stopping/deleting a named alert by type/condition ("stop the PPE alert", "delete the collision rule"). A named alert_type/condition = an existing rule → D's two-step stop protocol (GET /api/v1/realtime → yes/no confirm → delete), never Workflow B.
  3. Workflow B (VLM monitoring) — generic start/stop on a sensor with no detection condition and no alert-type qualifier ("start/stop real-time alert for sensor X"). A stop that names a type ("stop the PPE alert") is a rule stop → Workflow D.
  4. Workflow C (Query) — incident lookup / what happened (show/list incidents, recent alerts, time-range queries, and casual "any alerts…?" / "any alerts so far today?" / "what's been triggered?" phrasings). Bare alerts (without rule/subscription/active rules) means incidents → Workflow C, never Workflow D.
  5. Workflow A (CV) — CV deployment handling for anything not matched above.

alerts vs alert rules (C vs D) — pick exactly one, never both: what happened / has been triggered (incidents) → Workflow C (POST /generate). What rules/subscriptions are configured or active → Workflow D (the bare GET /api/v1/realtime, no /incidents). Bare alerts = incidents (C); alert rules / subscriptions / active rules = inventory (D). Never answer from memory; run the one correct call — full endpoint detail in Workflow C below.

Disambiguation (B vs D): if a sensor is named with start/monitor language but the detection condition is unclear, ask:

"Do you want me to (a) create a persistent alert rule on Alert Bridge that keeps running until you delete it, or (b) start a one-time monitoring session via the VSS Agent?"

Stop routing (B vs D): "Stop the <type> alert" (names an alert_type/condition like PPE, collision, fire) = stop a subscription rule → Workflow D (find via GET /api/v1/realtime, then the two-step stop/confirm protocol in references/alert-subscriptions.md; do not call POST /generate). A bare "stop real-time alert / stop monitoring on <sensor>" with no type qualifier = Workflow B.

If a prompt mixes workflows ("start monitoring and send to Slack"), ask one clarifying question to split execution order.

CV-mode refusal text for D and E intents

When the deployed mode is CV verification and the user asks for an alert-subscription or Slack/notification intent, refuse with this message verbatim:

"Alert subscriptions and Slack notifications are only supported in VLM real-time mode. Your current deployment is <CV verification | not deployed>. To use these features, redeploy with /vss-deploy-profile -p alerts -m real-time (note: switching tears down current CV monitoring)."

No auto-redeploy. The user decides whether to switch modes.


Prereq for Either Mode: Sensor Must Be in VIOS

Both modes require the camera registered in VIOS first (via the vss-manage-video-io-storage skill):

  • RTSP URL / IP camera → add it with POST /sensor/add (that skill's Section 6); record the sensorId / name.
  • Named existing sensor → confirm it appears in GET /sensor/list before proceeding.

On CV, adding the RTSP is the entire onboarding step (pipeline auto-picks it up). On VLM, it is a prerequisite to Workflow B.


The Agent /generate Endpoint

All VLM-flow actions and all query actions go through the VSS Agent's natural-language endpoint:

AGENT="http://<AGENT_ENDPOINT>"   # default http://localhost:8000 on the alerts profile

curl -s -X POST "$AGENT/generate" \
  -H "Content-Type: application/json" \
  -d '{"input_message": "<natural-language request>"}' | jq .

Endpoint resolution: use the agent endpoint from the active VSS deployment context. If unavailable, ask the user. Do not discover via filesystem.

Availability check: curl -sf --connect-timeout 5 "$AGENT/docs".

Do not call the rtvi-vlm microservice endpoints directly — always go through the agent. The agent internally dispatches to rtvi_vlm_alert, rtvi_prompt_gen, and video_analytics_mcp.get_incidents.


Workflow A — CV Mode (-m verification / MODE=2d_cv)

CV alerts are deployment-driven, not request-driven — there is no agent call to "create" one.

  1. Check if the sensor is in VIOS via vss-manage-video-io-storage's GET /sensor/list (idempotent — don't blindly POST /sensor/add).
  2. If missing, onboard via that skill's POST /sensor/add. The CV pipeline auto-picks up the stream once registered and online.
  3. Confirm online: curl -s "http://<VST_ENDPOINT>/vst/api/v1/sensor/<sensorId>/status" | jq .
  4. Alerts land in Elasticsearch (Behavior Analytics → alert-bridge verification per alert_type_config.json). Query with Workflow C.

A static-CV-pipeline alert on a VLM-only deployment is a mode mismatch — see the routing table above.


Workflow B — VLM Real-time Monitoring (CV or VLM mode)

Generic start / stop intents through the VSS Agent for a named sensor without a detection condition (if a condition is present, route to Workflow D). rtvi-vlm runs in both modes.

# start: input_message = "Start real-time alert for sensor <id>"
# stop:  input_message = "Stop real-time alert for sensor <id>"
curl -s -X POST "$AGENT/generate" -H "Content-Type: application/json" \
  -d '{"input_message": "<start|stop> real-time alert for sensor <id>"}' | jq .

Under the hood: rtvi_prompt_gen → rtvi_vlm_alert action="start". Every chunk is captioned; a chunk whose VLM response contains yes/true (case-insensitive) publishes an incident to mdx-vlm-incidents. Prompts must force a Yes/No answer. A static-CV-pipeline request on a VLM-only deployment is a mode mismatch — see the routing table.


Workflow D — Alert Subscriptions (VLM real-time mode only)

Create / list / delete persistent realtime alert rules on Alert Bridge. Route here when the prompt has rule keywords (rule, subscription, a rule ID) or when it pairs a specific sensor with a specific detection condition (e.g. "Set up a realtime alert on warehouse-dock-1 for PPE violations", "Watch sensor entrance-1 for tailgating", "Stop rule 496aebd1-…").

Not here: generic start/stop without a condition (→ Workflow B) or Slack operations (→ Workflow E).

Load and follow references/alert-subscriptions.md as the authoritative playbook for subscription CRUD. VLM real-time mode only; refuse with the canonical refusal text on CV.


Workflow E — Slack Notifications (VLM real-time mode only)

Use when the user explicitly mentions Slack or the webhook relay (start/stop webhook server, check status/health, send a test message, set Slack channel/token). The word notify alone is not enough.

alert-notify (port 9090) ≠ vss-alert-bridge (/api/v1/realtime). Do NOT touch vss-alert-bridge for Slack ops.

Routes here: "Set up Slack notifications", "Check if alert-notify is running", "Send a test alert to Slack". Does not route here: "Notify me when someone enters the zone" (→ D/B), "Alert and notify on my phone" (ambiguous — ask).

Load and follow references/alert-notify.md. Code lives in scripts/alert-notify/. VLM real-time mode only.


Workflow C — Query / List Alerts (works on either mode)

Both CV- and VLM-generated alerts land in Elasticsearch and are queryable via the agent's video_analytics_mcp.get_incidents tool. POST natural-language requests to $AGENT/generate — "Show me recent alerts for sensor X", "List confirmed alerts from the last hour", "Show collision incidents from Camera_02 between <ISO> and <ISO>".

Casual phrasings route here too. Questions like "Any alerts so far today?", "Any alerts today?", "What's been triggered?", or "Anything detected lately?" are incident queries — issue a POST /generate (e.g. {"input_message": "List alerts from today"}) and summarize the result. Never answer these from memory and never reply "no alerts" without running the query. A bare "alerts" question is always an incident lookup (Workflow C), not a subscription-rule listing (Workflow D).

Do NOT list subscription rules for an incident query. The bare GET /api/v1/realtime (no /incidents) lists rules (Workflow D) and is wrong for "what happened" — never call/probe it or load the Workflow D playbook for an incident query.

Empty result is a valid answer. If no incidents match (e.g. a freshly deployed system with no activity yet), report that none were found / the count is 0 for the requested period and STOP — do not fall back to listing rules or hunting other endpoints.

For richer / non-natural-language filtering (sensor-level, time-series, counts) use the vss-query-analytics skill (VA-MCP on port 9901).

Verdict interpretation & CV verifier prompts (CV mode only)

CV alerts carry a VLM verification verdict (confirmed / rejected / unverified); VLM real-time incidents have no separate verdict (the trigger is itself a Yes/No VLM answer). CV-path verifier prompts are customizable via alert_type_config.json (restart alert-bridge to apply). See references/cv-verifier-prompts.md for the verdict table, field meanings, and the prompt-customization rules.


Cross-Skill Links

TaskSkill
Deploy, redeploy, or switch alert modevss-deploy-profile — -p alerts -m {verification,real-time}
Add an RTSP/IP camera, list sensors, snapshots, clipsvss-manage-video-io-storage (Section 6 for Add Sensor)
Time-range incident / occupancy / PPE metrics from Elasticsearchvss-query-analytics (VA-MCP :9901)
Detailed incident report from an alertvss-generate-video-report
Subscriptions / Slack sub-workflowsreferences/alert-subscriptions.md, references/alert-notify.md (code in scripts/alert-notify/)

Gotchas

  • alert-notify (port 9090) ≠ vss-alert-bridge. Slack ops → Workflow E (alert-notify); never route Slack to vss-alert-bridge's /api/v1/realtime.
  • Workflow scope by mode: A is CV-only; B and C work on either mode; D and E are VLM real-time only (refuse on CV with the canonical text).
  • Don't use vss-rtvi-vlm as a mode signal — it runs in both modes. Use vss-behavior-analytics (CV-only) or the MODE env var.
  • A mode switch tears down the current deployment — running VLM streams and un-persisted CV alert state are lost.
  • Always go through $AGENT/generate — never call rtvi-vlm directly. The VLM trigger is a "yes"/"true" token match (case-insensitive); rtvi_prompt_gen enforces the Yes/No pattern, so don't hand-craft prompts that break it.
  • Sensor must already be in VIOS for either mode (use vss-manage-video-io-storage for RTSP-only inputs).

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