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/skillsThe 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 --yesReplace 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-codeCodex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codexSnowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortexCursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursorKiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cliUse --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-cliNew skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills updateRun 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 --listFor non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
| Product | Description | Skills |
|---|---|---|
| Auto Ontology | Set up Auto Ontology, model and publish its semantic layer, and run validated text-to-SQL queries through it. | nvidia-ontology-setup, nvidia-ontology-management, nvidia-ontology-query |
| BioNeMo Libraries | Accelerate cheminformatics, genomic analysis, and equivariant model development with NVIDIA GPU libraries and tools. | bionemo-nvmolkit-usage, bionemo-parabricks, bionemo-genomics-workflow-acceleration |
| BioNeMo NIMs | Run and combine BioNeMo NIM microservices for biomolecular research and drug discovery through hosted APIs or local deployments. | bionemo-drug-discovery-pipeline, bionemo-msa-structure-prediction-pipeline, bionemo-openfold2-nim, bionemo-molmim-nim, bionemo-diffdock-nim, bionemo-genmol-nim, bionemo-evo2-nim, bionemo-rfdiffusion-nim, bionemo-rfd4-proteina-nim, bionemo-openfold3-nim, bionemo-proteinmpnn-nim, bionemo-msa-search-nim, bionemo-boltz2-nim |
| BioNeMo Open Models | Apply BioNeMo open models to molecular property prediction and generative protein design. | bionemo-codonfm-setup, bionemo-codonfm-embed, bionemo-codonfm-score, bionemo-codonfm-finetune, bionemo-kermt-add-cmim-pretrain, bionemo-kermt-continue-pretrain, bionemo-kermt-embed, bionemo-kermt-finetune, bionemo-kermt-infer, bionemo-kermt-monitor, bionemo-kermt-pretrain-scratch, bionemo-kermt-setup |
| CDF AnomalyGen | Run Cosmos Data Factory AnomalyGen workflows for fine-tuning, synthetic defect generation, evaluation, refinement, and pseudo-labeling. | cdf-anomalygen |
| CDF Augmentation | Author, validate, and run Cosmos Data Factory augmentation pipelines for image and video generation and transformation. | cdf-augmentation |
| CDF Auto-Labeling | Build and run Cosmos Data Factory auto-labeling pipelines for video enhancement, tracking, captioning, and question generation. | cdf-auto-labeling |
| CDF Curation and Retrieval | Author, validate, and run Cosmos Data Factory curation and retrieval pipelines. | cdf-curation-and-retrieval |
| CUDA-Q | Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance. | cudaq-guide, cudaq-importing |
| cuDF | Official 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 |
| cuOpt | GPU-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 |
| DALI | GPU-accelerated data loading and processing with NVIDIA DALI. | dali-dynamic-mode |
| Data Designer | Build declarative synthetic dataset generation pipelines with NeMo Data Designer. | data-designer |
| DeepStream | Agentic skills for guided DeepStream development. | amc-run-rtsp-calibration, 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-run-mv3dt, deepstream-sop, rtvi-cv-customize-model, rtvi-cv-scaffold-vss-service, rtvi-vlm-customize-model |
| Digital Health | Agent skills for (1) the clinical ASR evaluation flywheel — term curation, synthetic clinical-speech benchmark generation, KER (Keyword Error Rate) scoring, and fine-tune guidance; (2) the ambient healthcare agent creation with Nemotron Voice Agent. | digital-health-clinical-asr-setup, digital-health-clinical-asr-build, digital-health-clinical-asr-eval, digital-health-clinical-asr-finetune, ambient-healthcare-agent-with-nemotron-voice-agent |
| DOCA | Teach 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 |
| Dynamo | NVIDIA 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 |
| Earth2Studio | Open-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 |
| Ethos | Capture an AI agent's purpose, boundaries, and success criteria in a portable ethos.md that developers, reviewers, and evaluators share. | ethos-orchestrate, ethos-explore, ethos-write |
| FoundationPose Perception Pipeline | Set up the FoundationPose perception stack, adapt BOP datasets, and run or evaluate pose inference with TAO Deploy depth. | foundationpose-setup, foundationpose-pipeline |
| G-Assist | Check or change a machine's NVIDIA display and GPU settings such as resolution, refresh rate, V-Sync, G-SYNC, brightness, color, and GPU performance, via the G-Assist MCP server. | g-assist-mcp-skill |
| HoloHub | Build, run, debug, benchmark, and develop Holoscan applications and Holoscan Modules with validated lifecycle workflows. | holoscan-app-debug-build-run, holoscan-module-lifecycle |
| Holoscan SDK | Install 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 Bridge | Agent-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 Workflows | Agent-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-train-rl, i4h-workflow-validate, i4h-workflow-e2e, i4h-lerobot-viz |
| Jetson BSP | Agentic 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 Device | Device-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, jetson-video-benchmark, jetson-video-capability, jetson-video-pipeline, jetson-video-recipe, jetson-video-setup |
| Medical AI Skills | Agent-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, medtech-model-evidence-export, nv-generate-ct-rflow, nv-generate-mr, nv-generate-mr-brain, nv-generate-mr-brain-finetune, nv-generate-vae-finetune, nv-reason-ct, nv-reason-cxr, nv-segment-ct, nv-segment-ct-finetune, nv-segment-ctmr |
| Megatron-Core | Large-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 AutoModel | NeMo 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 Fabric | Portable skills for integrating applications with NeMo Fabric through its public SDK and building compatible third-party adapters. | nemo-fabric-integrate, nemo-fabric-build-adapter |
| NeMo MBridge | NeMo 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 Relay | Skills 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 Retriever | NeMo Retriever - deploy NeMo Retriever Library locally, extract information from corpus of data, and answer questions against the corpus. | nemo-retriever, nemo-retriever-mcp |
| NeMo-RL | RLHF 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 |
| NemoClaw | Secure agent sandboxing — run OpenClaw inside NVIDIA OpenShell with managed inference, policy management, remote deployment, sandbox monitoring. | nemoclaw-user-guide |
| Nemotron | Author end-to-end model development, customization, evaluation, and deployment pipelines using the NVIDIA AI stack. | nemotron-customize, nemotron-retrieval-recipes, nemotron-policy-generator |
| Nemotron Speech | Deploy 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 |
| Nemotron Voice Agent | Create or refine NVIDIA voice agents (Cascaded or Omni) with Pipecat or LiveKit — speech customization, model selection, cloud or local deployment, and iteration. | nemotron-voice-agent-builder |
| Nsight | Agent-facing assets for Nsight developer workflows, including skills that help agents work with NVIDIA developer tools. | compute-sanitizer-diagnose-cuda-bugs |
| NVFlare | Agent skills for converting training code to federated workflows, diagnosing jobs, collecting federated statistics, and running Auto-FL with NVIDIA FLARE. Install the complete NVFlare skill set together so each workflow has its required shared references. | nvflare-autofl, nvflare-autofl-report, nvflare-convert-huggingface, nvflare-convert-lightning, nvflare-convert-pytorch, nvflare-diagnose-job, nvflare-fed-stats, nvflare-orient, nvflare-shared |
| NVIDIA App | NVIDIA App MCP: drivers, games, laptops, overlay. Check drivers, manage and optimize games, configure laptop features. | nvidia-app |
| NVIDIA Broadcast | Control NVIDIA Broadcast through its MCP gateway: apply audio and video effects, process local media, select devices, and change camera resolution. | nvidia-broadcast |
| NVSHMEM | Install, build, run, tune, and debug the NVSHMEM library and applications. | nvshmem-install, nvshmem-get-started, nvshmem-docs, nvshmem-troubleshoot-and-report-bugs, nvshmem-tune-performance, nvshmem-enable-tma, nvshmem-configure-nic-pe-mapping, nvshmem-select-remote-transport, nvshmem-collect-performance-data |
| ovnewton | Connect Newton physics to ovstage and Omniverse simulations. | ovnewton-usage |
| Physical AI | Physical AI skills for simulation, synthetic data generation, training, validation and deployment and more. | isaac-mission-control-showcase, 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-physics-selection |
| Physical AI Orchestration | Build, run and monitor physical AI data factory pipelines for image and video generation and transformation. | paidf-orchestration-write-dag, paidf-orchestration-setup, physical-ai-event-video-generation, physical-ai-image-attribute-augmentation |
| PhysicsNeMo | NVIDIA 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 Optimization | GPU-accelerated Mean-CVaR portfolio optimization with NVIDIA cuOpt — CVaR optimization, efficient frontier, scenario generation, backtesting, and rebalancing. | portfolio-optimization |
| RAG Blueprint | RAG pipeline — deploy, configure, troubleshoot, and manage retrieval augmented generation with Docker Compose or Helm. | rag-blueprint, rag-eval, rag-perf |
| RTX Remix | Mod and remaster classic games with RTX Remix: manage projects and USD layers, and replace textures and models through the Toolkit MCP server. | rtx-remix-modding |
| Skill Card Generator | Reads 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 Toolkit | NVIDIA 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-deft-aoi-cosmos3, tao-run-deft-cr-its-mining, tao-run-deft-object-detection, tao-run-deft-od-aoi, tao-run-deft-pas, tao-run-dinov3-ssl-deft, tao-run-inference-service, tao-train-single-step, tao-validate-recipe-transfer, paidf-cosmos-predict, tao-analyze-detection-kpi, tao-analyze-gaps-od-map, tao-analyze-gaps-visual-changenet, tao-analyze-gaps-vlm-bcq, tao-convert-dataset-format, tao-finetune-anomalygennext, tao-generate-anomalies, tao-generate-image-embeddings, tao-generate-image-grounding, tao-generate-od-defects, tao-generate-referring-expressions, tao-generate-video-reasoning-annotations, tao-mine-aoi-images, tao-mine-nearest-neighbors, tao-mine-od-images, tao-prepare-anomalygennext-inputs, tao-route-visual-changenet-samples, tao-validate-dataset-format, tao-finetune-clip, tao-finetune-cosmos-embed, tao-finetune-cosmos-reason, tao-finetune-kumo-anomaly, tao-finetune-kumo-forecast, tao-finetune-video-clip, tao-train-action-recognition, tao-train-bevfusion, tao-train-centerpose, tao-train-codetr, tao-train-deformable-detr, tao-train-depth-anything-v2, tao-train-dino, tao-train-dinov3, 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-data-io, tao-run-on-brev, tao-run-on-docker, tao-run-on-kubernetes, tao-run-on-slurm, tao-run-on-virtualenv, tao-setup-nvidia-gpu-host, tao-artifacts, tao-launch-workflow, tao-list-capabilities, tao-setup, tao-token-efficient-execution |
| TileGym | Tile-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 Summarization | VSS Blueprint — build and deploy a vision AI stack (dense captioning, detection, search, alerting, summarization) for an agent or application. | vss-build-vision-ai |
| Warp | GPU-accelerated simulation, robotics, and machine learning — evaluate Warp candidates, optimize compile times, and debug gradients. | warp-compile-time-optimizer, warp-debug-gradients, warp-eval |
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:
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 againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/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_filesA 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.
- ✅ 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
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. 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 againstnv-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, orbenchmark/evals.json
When 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:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference 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.