NVIDIA Agent Skills
Official, NVIDIA-verified skills for AI 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, including CUDA-X libraries, 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/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.
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.
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-python --yesReplace cuopt-numerical-optimization-api-python 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-python --agent claude-codeCodex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api-python --agent codexCursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api-python --agent cursorKiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api-python --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-python \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cliBrowse the Catalog
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.
Skill Catalog
<!-- skills-table-start -->| Product | Description | Skills |
|---|---|---|
| AIQ | NVIDIA AI-Q Blueprint - deploy local AI-Q services and run shallow or deep research workflows as agent skills. | aiq-research, aiq-deploy |
| CUDA-Q | CUDA Quantum — onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications. | cudaq-guide |
| 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 |
| cuFOLIO | GPU-accelerated Mean-CVaR portfolio optimization with NVIDIA cuOpt — CVaR optimization, efficient frontier, scenario generation, backtesting, and rebalancing. | cufolio |
| cuOpt | GPU-accelerated optimization — vehicle routing, linear programming, quadratic programming, installation, server deployment, and developer tools. | cuopt-developer, cuopt-install, cuopt-numerical-optimization-api-c, cuopt-numerical-optimization-api-cli, cuopt-numerical-optimization-api-python, cuopt-numerical-optimization-formulation, cuopt-routing-api-python, cuopt-routing-formulation, cuopt-server-api-python, cuopt-server-common, cuopt-skill-evolution, cuopt-user-rules |
| cuPyNumeric | NumPy and SciPy on multi-node multi-GPU systems — skills to help with installing cuPyNumeric, migrating existing NumPy code, and doing parallel I/O | cupynumeric-hdf5, cupynumeric-install, cupynumeric-migration-readiness, cupynumeric-parallel-data-load |
| 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. | deepstream-dev, deepstream-import-vision-model |
| Digital Health | Agent 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 |
| 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-data-fetch, earth2studio-deterministic-forecast, earth2studio-discover, earth2studio-install |
| 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, covering demo environment bring-up, FPGA flashing for Lattice and VB1940 hardware, example application execution, and QA test-plan automation. | hsb-setup, hsb-flash, hsb-app, hsb-test |
| 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, 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-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 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](skills/nemo-mbridge-perf-parallelism-stra |
…