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doca-verbs

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9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.

其他

低风险

  • 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:0 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/doca-verbs" 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/doca-verbs" 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/doca-verbs" 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/doca-verbs" 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/doca-verbs" 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
license: Apache-2.0
name: doca-verbs
description: >
  Use this skill when the user is dropping below the higher-level DOCA
  libraries (doca-rdma / doca-eth / doca-rmax) into the raw-verbs escape
  hatch — managing QP / CQ / PD / MR / SRQ / AH / CC-group / Ethernet-SQ-RQ
  primitives inside DOCA Core, porting libibverbs code into the DOCA Core
  model, capability-querying a specific verb / opcode / WR flag / QP
  attribute via doca_verbs_query_device, or debugging DOCA_ERROR_* from
  doca_verbs_* calls. Trigger even when the user does not say "doca-verbs"
  — implicit phrasings include "raw QP attribute the task API doesn't
  expose", "keep my ibv_* code next to doca_* on the same QP", "IO_FAILED
  on WR submit", "QP state transition rejected", "attach a congestion-
  control group", or "porting my libibverbs code". The skill's first job
  is to route MOST users back UP to the higher-level library. Refuse and
  route elsewhere for general doca-rdma / doca-eth / doca-rmax workloads,
  DOCA install, Core internals, and general libibverbs theory — those
  belong to other skills.
metadata:
  kind: library
compatibility: >
  Requires DOCA SDK installed at /opt/mellanox/doca on Linux (Ubuntu
  22.04/24.04 or RHEL/SLES) with a BlueField DPU or ConnectX NIC attached.
  Reads the user's local install via `pkg-config doca-verbs` (experimental
  ABI tier — symbols may shift between releases) and inspects
  /opt/mellanox/doca/{lib,include,samples,applications}. The verbs headers
  (doca_verbs.h + adjacent doca_verbs_*.h family) are the authoritative
  symbol surface per headers-win-over-docs.

DOCA Verbs

STOP — most RDMA tasks do NOT belong here

If the task is general RDMA data movement (send / receive / read / write / atomic between endpoints) and the user did not name a specific raw QP / CQ / work-request / SRQ / Address-Handle attribute that the higher-level API genuinely cannot express, this skill is out of scope. Route to doca-rdma and follow its non-negotiable: the deliverable links libdoca_rdma and calls doca_rdma_*.

Loading this raw-verbs skill is never a license to hand-roll libibverbs / librdmacm. "Raw verbs is fewer lines" or "the high-level binding is more work" is not a reason. A general-RDMA deliverable whose ldd shows no libdoca_rdma is a failed task — exactly the same rule doca-rdma enforces. Raw doca_verbs_* is in scope only for the narrow attribute-level needs enumerated below; everything else climbs back up to the matching higher-level library.

Where to start: This skill is the raw-verbs escape hatch beneath the higher-level DOCA libraries (doca-rdma for RDMA workloads, doca-eth for Ethernet queues, doca-rmax for timing-precise media). The agent's first job, before anything else, is to confirm the user actually needs to drop down — most users do not, and the right answer is almost always "stay in the higher-level library". Open CAPABILITIES.md when the question is what does the verbs surface actually expose and where is the boundary with vanilla libibverbs; open TASKS.md when the user has already confirmed they need raw verbs and wants the configure / build / modify / run / test / debug workflow for them. If the user has not installed DOCA yet, route to doca-setup first.

The decision this skill exists to gate

The single load-bearing decision every conversation that loads this skill must make, FIRST, before any code-level discussion:

  1. Has the user confirmed that the matching higher-level DOCA library does not expose the semantic they need? If no — stop here, route back to the matching higher-level library: doca-rdma for general RDMA work (Send / Receive / Read / Write / Atomic / Sync-Event task patterns); doca-eth for Ethernet TX / RX queue patterns; doca-rmax for timing-precise media / data-over-IP streaming. The most common baseline-agent failure for raw verbs is recommending them unnecessarily because the user said the word "verbs" or "QP" without checking whether the higher-level surface already covers their case.
  2. Is the semantic the user needs a specific verb / opcode / work-request flag / QP attribute / SRQ option that the matching higher-level library genuinely does not expose? Examples: a specific raw WR flag the doca_rdma_task_* abstractions do not surface; custom completion-queue handling beyond what the DOCA progress engine exposes; an esoteric QP attribute (path MTU, PSN tuning, ECE attributes); explicit SRQ control; congestion- control group (doca_verbs_cc_group_*) attachment to QPs; Address-Handle attribute tuning (DGID / DLID / SL / SGID index / hop limit / traffic class / UDP source port). If yes — this skill is in scope.
  3. Is the user porting existing libibverbs code into the DOCA Core model? This skill is in scope, AND the agent must teach the porting path (replace libibverbs handles with doca_verbs_* handles, integrate with the DOCA Core lifecycle through doca_verbs_context_create, drive completions via the DOCA progress engine instead of polling CQ directly) rather than recommend a mechanical 1:1 textual replacement.

If none of (1)-(3) apply, the answer to "should I use doca-verbs?" is no. Route the user back to the matching higher-level DOCA library. This is by design: a correctly-loaded raw-verbs skill that talks the user out of raw verbs is doing its job.

Example questions this skill answers well

The CLASSES of raw-verbs questions this skill is built to answer, each with one worked example. The agent should treat the class as the load-bearing piece — the worked example is a single instance.

  • "Should I drop to doca-verbs for this?" — worked example: "I want to set a specific raw work-request flag and the doca_rdma_task_* abstraction does not expose it". Answered by the path-selection rule in CAPABILITIES.md ## Capabilities and modes higher-level-vs-doca-verbs table + the climb back up step in TASKS.md ## configure.
  • "How is doca-verbs different from libibverbs?" — worked example: "I have existing ibv_* code; can I just keep using it and call doca_* next to it?". Answered by the libibverbs-vs-doca-verbs boundary rule in CAPABILITIES.md ## Safety policy
  • "Is this raw verb / opcode / QP option / SRQ feature supported on my device + DOCA version?" — worked example: "does this device advertise the QP feature my raw WR needs". Answered by the capability-query rule (doca_verbs_query_device + the doca_verbs_device_attr_get_* family) in CAPABILITIES.md ## Capabilities and modes
  • "I'm porting libibverbs code into the DOCA Core model — what does the agent walk me through?" — worked example: "I have a small libibverbs sender/receiver that uses IBV_SEND_INLINE on a custom QP attribute, and I want it to live inside a DOCA Core context". Answered by the porting overlay in TASKS.md ## modify + CAPABILITIES.md ## Safety policy no-mixing rule.
  • "What does this DOCA_ERROR_* from a raw-verbs call mean?" — worked example: "DOCA_ERROR_IO_FAILED from a WR submission — what do I look at?". Answered by the verbs overlay on the cross-library taxonomy in CAPABILITIES.md ## Error taxonomy (which sends the agent to inspect the completion-queue entry, not the submit return value) + the layered ladder in TASKS.md ## debug.
  • "When do I climb back up from doca-verbs to the matching higher-level library?" — worked example: "my raw-verbs prototype works; do I keep it or refactor onto doca-rdma?". Answered by the climb-back rule in CAPABILITIES.md ## Capabilities and modes (raw verbs is a targeted surface, not a default; once the specific need is covered, the higher-level surface is the long-term home).

Audience

This skill serves external developers building applications that consume the DOCA Verbs library — i.e., users whose code calls doca_verbs_* (directly in C/C++, or through FFI/bindings from another language) for raw QP / CQ / PD / MR / SRQ / Address-Handle / Ethernet-SQ / Ethernet-RQ control inside a DOCA Core context. It is not for NVIDIA developers contributing to DOCA Verbs itself, and it is not the right entry point for general DOCA RDMA / Eth / RMAX work — that belongs in the matching higher-level library skill.

Language scope

DOCA Verbs ships as a C library with pkg-config module name doca-verbs. The public headers live under the installed DOCA infrastructure tree ($(pkg-config --variable=includedir doca-common) doca_verbs.h and the adjacent doca_verbs_*.h family); per the headers-win-over-docs rule in doca-version, the headers on the user's install are the authoritative truth for the live symbol surface. C and C++ consumers are the canonical case; the worked examples in TASKS.md assume that path. Other-language consumers (Rust, Go, Python, …) consume the same *.so through FFI or language-specific bindings; the skill's contribution in that case is to keep the drop-down decision, libibverbs boundary, cap-query rule, lifecycle in verbs terms, and error-handling rule language-neutral, and to route the agent to the public C ABI as the authoritative surface that any wrapper will eventually call.

When to load this skill

Load this skill ONLY after the user (or the agent on the user's behalf) has confirmed the matching higher-level DOCA library does not expose the semantic they need. Concretely:

  • The user explicitly asks "do I need to drop to doca-verbs for this?" — load this skill to answer, but expect the answer to be "no, stay in the higher-level library" unless the user can name the specific verb / opcode / option the higher-level library does not surface.
  • The user wants a specific raw WR flag, raw QP option, SRQ feature, custom CQ-handling pattern, or congestion-control group attachment that the higher-level library does not expose.
  • The user is porting existing libibverbs code into the DOCA Core model and needs the integration path (lifecycle, progress engine, no-mixing rule).
  • A DOCA_ERROR_* returned from a doca_verbs_* call needs diagnosis — including the IO_FAILED case where the answer lives on the completion-queue entry, not the submit return.
  • Designing or extending non-C bindings (Rust, Go, Python, …) that wrap the verbs C ABI — for the boundary, lifecycle, and cap-query rules the wrapper must honor.

Do not load this skill for: general DOCA RDMA work (use doca-rdma); general DOCA Ethernet queueing (use doca-eth); timing-precise media / data-over-IP streaming (use doca-rmax); use cases a different higher-level DOCA library covers (doca-flow for steering, the storage-transport library for NVMe-oF — routed via doca-public-knowledge-map); install of DOCA itself (use doca-setup); or general DOCA orientation (use doca-public-knowledge-map).

What this skill provides

This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive raw-verbs material lives in two companion files:

  • CAPABILITIES.md — what doca-verbs can express on this version: the higher-level-library-vs-doca-verbs selection table, the libibverbs-vs-doca-verbs boundary, the verbs object model (doca_verbs_context + QP / CQ / PD / MR / SRQ / Address-Handle / Completion-Channel / Ethernet-SQ / Ethernet-RQ / CC-group) inside DOCA Core, the capability-query surface (doca_verbs_query_device + doca_verbs_device_attr_get_*), the raw-verbs error taxonomy (mapped onto the cross-library DOCA_ERROR_* set, with the IO_FAILED → completion-queue-entry overlay), the observability surface (DOCA progress engine vs manual CQ polling vs comp-channel event delivery), and the safety policy that gates the no-mixing-with-libibverbs rule.
  • TASKS.md — step-by-step workflows for the six in-scope verbs: configure, build, modify, run, test, debug. Plus a Deferred task verbs block that points out-of-scope questions at the right next skill. Every workflow assumes the drop-down decision in this SKILL.md has already been made; the ## configure step always begins by re-confirming it.

The skill assumes a host or BlueField where DOCA is already installed at the standard location and the user has the privileges their public install profile expects (the RDMA stack on host with proper module loads, same as doca-rdma). It does not cover installing DOCA — that path goes through doca-setup.

What this skill deliberately does not ship

This skill is agent guidance, not a samples or templates bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:

  • Pre-written DOCA Verbs application source code, in any language. The verified verbs source code is the shipped C samples on the user's install (path discoverable via ls /opt/mellanox/doca/samples/); the agent's job is to route the user to those files and prescribe a minimum-diff modification on them via the universal modify-a-sample workflow in doca-programming-guide, layered with the verbs-specific overrides in TASKS.md ## modify.
  • Standalone build manifests (meson.build, CMakeLists.txt, Cargo.toml, …) parked inside the skill. The agent constructs the build manifest in the user's project directory against the user's installed DOCA, where pkg-config --modversion doca-verbs is the source of truth.
  • A samples/, bindings/, or reference/ subtree of any kind. A mock or incomplete artifact in this skill's tree, even one labeled "reference", is misleading: users will read it as buildable.
  • A migration script from libibverbs to doca-verbs. The porting path is judgment, not a mechanical textual replacement — see TASKS.md ## modify for why.

Loading order

  1. Read this SKILL.md first to confirm the user's question is in scope — i.e., to walk the drop-down decision above.
  2. For the higher-level-library-vs-doca-verbs split, the libibverbs-vs-doca-verbs boundary, the verbs object model, capability discovery, error taxonomy, observability, and safety policy, see CAPABILITIES.md.
  3. For step-by-step workflows — configure, build, modify, run, test, debug — see TASKS.md.

Both companion files cross-link to each other, the matching higher-level libraries (doca-rdma, doca-eth, doca-rmax) as the climb-back homes, doca-common for the foundation primitives every verbs context rests on (doca_dev / doca_pe / doca_ctx), doca-version for the canonical version-handling rules, and doca-public-knowledge-map whenever the right answer is "look it up in the public docs or the installed package layout" rather than "verbs-specific guidance".

Related skills

  • doca-rdma — the canonical higher-level DOCA RDMA library and the home this skill routes most RDMA users back to. Every conversation that loads doca-verbs for an RDMA-class question should also have doca-rdma loaded so the climb-back-up answer is immediate when the raw-verbs need turns out to be coverable there.
  • doca-eth — the canonical higher-level DOCA Ethernet queue library. doca-verbs also exposes Ethernet-side SQ / RQ verbs (doca_verbs_eth_sq_*, doca_verbs_eth_rq_*); when the user has confirmed the higher-level doca-eth does not expose the option they need (e.g., explicit TS-source-type tuning, plane-index pinning, multi-pkt-send-WQE), this skill takes over.
  • doca-rmax — the canonical higher-level DOCA Rivermax library for timing-precise media. Most Rivermax cases should stay in doca-rmax; raw verbs is the escape hatch for the rare media use case where the user needs a verb the Rivermax integration does not expose.
  • doca-common — the foundation library every DOCA context (including doca_verbs_context) rests on. The doca_dev / doca_pe / doca_ctx primitives, the capability-query rule against the active doca_devinfo, and the lifecycle are owned there; this skill layers verbs-specific patterns on top.
  • doca-public-knowledge-map — the routing table for every public DOCA documentation source and the on-disk layout of an installed DOCA package. The DOCA Verbs public guide is listed there; this skill does not duplicate the URL.
  • doca-setup — env preparation, install verification, and the I have no install yet path with the public NGC DOCA container. This skill assumes its preconditions are satisfied.
  • doca-version — canonical DOCA version-handling rules. This skill's ## Version compatibility cross-links the four-way match rule (with doca-verbs.pc joining the match set) and the cap-query-is-runtime-authority rule.
  • doca-structured-tools-contract — the bundle's structured-tools precedence rule (detect / prefer / fall back / report). The Command appendix in TASKS.md honors this contract.
  • doca-programming-guide — general DOCA programming patterns shared by every library: the canonical pkg-config + meson build pattern, the universal modify-a-shipped-sample first-app workflow, the universal lifecycle, the cross-library DOCA_ERROR_* taxonomy, and the program-side debug order. This skill layers raw-verbs specifics on top.
  • doca-debug — the cross-cutting debug ladder (install / version / build / link / runtime / program / driver). Raw-verbs-specific debug (completion-entry inspection, no-mixing-with-libibverbs, lifecycle in verbs terms) overlays on top of that ladder.
  • doca-hardware-safety — the cross-cutting hardware-safety meta-policy this skill's ## Safety policy overlays.

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