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jetson-customize-clocks

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

高风险

  • 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。
  • 存在潜在风险命令,请谨慎安装。
  • 扫描发现:2 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/jetson-customize-clocks" 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/jetson-customize-clocks" 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/jetson-customize-clocks" 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/jetson-customize-clocks" 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/jetson-customize-clocks" 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: jetson-customize-clocks
description: Use to lock/cap Jetson CPU/GPU/EMC clocks, toggle EMC/CPU DVFS, or change cpufreq governors by editing BPMP DTB and nvpower.sh pre-flash. Do NOT use for live tuning or nvpmodel edits.
version: 0.0.1
license: "Apache-2.0"
metadata:
  data-classification: public
  author: "Jetson Team"
  tags:
    - clocks
    - cpu
    - gpu
    - emc
    - dvfs
    - bwmgr
    - bpmp
    - nvpower
    - cpufreq
    - devfreq
  domain: clocks

Customize Clocks

Purpose

Customize CPU, GPU, and EMC clock behavior on a Jetson target by editing files under Linux_for_Tegra/ before flashing the image. Two layers are in scope:

  • The BPMP DTB at Linux_for_Tegra/bootloader/<BPFDTB_FILE> — per-clock max-rate-custom ceilings, plus the EMC DVFS gate (bwmgr + cactmon on all SoCs; osp-controller on T26x only).
  • nvpower.sh at Linux_for_Tegra/rootfs/etc/systemd/nvpower.sh — cpufreq / devfreq governors and (optionally) per-device min / max / static rates written to sysfs at boot.

Common triggers: "lock CPU/GPU/EMC frequency", "pin GPU to Fmax", "pin EMC to MAXN", "disable/enable EMC DVFS", "disable/enable CPU DVFS", "set CPU/GPU max rate", "change cpufreq governor".

Out of scope: runtime clock tuning on a live target (no flash step), nvpmodel power-mode edits (use the sibling skill /jetson-customize-nvpmodel), and silicon-ceiling overrides (max-rate-maxn is read-only).

Prerequisites

Resolve the active profile per ../../context/target-platform-contract.md. Refuse and route in these cases:

ConditionRefuse with
No active profile, or active: NARoute to /jetson-set-target or /jetson-init-target.
Profile lacks bsp_image: blockRoute to /jetson-init-image.
<bsp_image.root_path>/Linux_for_Tegra/ missingRoute to /jetson-init-image.
<source.root_path>/Linux_for_Tegra/ missing or not a git repoRoute to /jetson-init-source.

Resolve paths:

  • <bsp_image.root_path> from bsp_image.root_path: if present, else <workspace>/Image.
  • <source.root_path> from source.root_path: if present, else <workspace>/Source.

<bsp_image.root_path> is read-only for this skill; every write (Operation 1's BPMP DTB and Operation 2's nvpower.sh) lands under <source.root_path> (the overlay tracker). This is the workflow invariant in ../../context/bsp-customization-workflow.md#workflow-invariants — hand-editing upstream silently destroys the diff trail and makes /jetson-promote-image a noop.

Instructions

  1. Resolve the prerequisites above (active profile, BSP image extracted, source overlay tracker initialized).
  2. Pick the operation from the table below.
  3. Follow the linked procedure section — Operation 1 (BPMP DTB), Operation 2 (nvpower.sh), or the MAXN recipe for both.
  4. Commit the edit inside the overlay tracker per each Operation's commit convention.
  5. Deploy with /jetson-promote-image → /jetson-flash-image. The new BPMP DTB and nvpower.sh take effect on the next boot.

Supported operations

OperationWhere the edit livesProcedure section
Lock a CPU / GPU clock to a specific rateBPMP DTB max-rate-custom on the clock node + nvpower.sh governor performance"Content edit: max-rate-custom" + "Pick the edit"
Lock EMC at its init rate (disable EMC DVFS)BPMP DTB: bwmgr.enabled = 0, cactmon.enabled = 0, plus /delete-node/ osp-controller on T26x only"Content edit: EMC DVFS disable / enable"
Re-enable EMC DVFSBPMP DTB: bwmgr.enabled = 1, cactmon.enabled = 1, restore osp-controller on T26x"Content edit: EMC DVFS disable / enable"
Pin everything to MAXN for stress runsCombine the above + nvpmodel MAXN as boot defaultsee Recipe
Lower a clock's hard ceiling without lockingBPMP DTB max-rate-custom only"Content edit: max-rate-custom"
Bound a device's rate without pinningnvpower.sh min/max via sysfs"Pick the edit"

Operation 1 — BPMP DTB edits

Follow the BPMP-DTB customization protocol in ../../references/bsp-customization-bpmp-dtb.md. The protocol owns the mechanics — pristine import on first touch, dtc decompile, recompile, sanity-check, commit. This skill supplies only the clock-specific content (which nodes and properties to edit during the protocol's "Edit the DTS" step).

The edited .dtb lands in the <source.root_path>/Linux_for_Tegra/ overlay tracker. /jetson-promote-image's channel A walks the tracker and copies the file into bsp_image. Do not edit <bsp_image.root_path>/Linux_for_Tegra/bootloader/<bpmp-dtb> directly — that's the promote output, not an input.

Resolve the SKU-correct BPMP DTB

Per the protocol's "Resolving the active BPMP DTB" section, read BPFDTB_FILE from the active flash conf. For the common Thor / single-SKU conf shapes this is the static BPFDTB_FILE=... line in the per-board .conf and the value is authoritative as-is.

For SKU-multiplexed conf shapes (Orin AGX devkit conf chain that selects a different BPMP DTB per board_sku/board_FAB via update_flash_args_common — see ../../context/bsp-customization-software-layers.md#per-board-conf-dispatch--update_flash_args_common), walk the dispatch chain with board_sku=<module.sku> and board_FAB=<module.revision or empty> from the active profile, and read BPFDTB_FILE from the dispatch output — not from the static line of the per-board .conf. Static and dispatched values match for non-multiplexed confs; the dispatch is mandatory only when the conf chain conditionally overrides BPFDTB_FILE.

List effective max rates (inspection)

Inspect both layers of the runtime ceiling — see references/clock-control-model.md#effective-runtime-ceiling — before deciding on a max-rate-custom value.

Inspection cookbook (BPMP-side decompile + grep; nvpmodel-side awk over the boot default mode) is in references/bpmp-dtb-clock-edits.md#inspection-cookbook.

For the nvpmodel layer see /jetson-customize-nvpmodel.

This step does not mutate state — it's a precondition for sizing the edit in the "Content edit: max-rate-custom on a named clock node" step.

Content edit: max-rate-custom on a named clock node

During the "Edit the DTS" step of the protocol, modify the property inside the named clock node — never lateinit. max-rate-custom must be strictly below the clock's hard cap (max-rate-maxn if defined, otherwise the live max_rate from a running target of the same chip / SKU).

DTS edit form, semantics, and the nvpmodel ↔ BPMP clock-node mapping live in references/bpmp-dtb-clock-edits.md.

Then hand control back to the protocol — its "Recompile", "Sanity-check the recompiled blob", "Stage in the overlay tracker", and "Cleanup" steps cover the rest. Commit-message convention per the protocol: <BPMP_BASENAME>: jetson-customize-clocks — <clock-node> max-rate-custom = <value>.

Content edit: EMC DVFS disable / enable

Default behavior (EMC DVFS on) requires no edit. Disabling EMC DVFS is a multi-node edit applied inside the same "Edit the DTS" step of the protocol, not a bwmgr toggle:

#EditScope
1bwmgr.enabled = <0x00>All SoCs, mandatory
2cactmon.enabled = <0x00>All SoCs, mandatory
3/delete-node/ osp-controllerT26x (Thor) mandatory — T23x (Orin) has no such node, skip

Detection: dtc -I dtb -O dts <bpmp-dtb> | grep -c osp-controller — zero hits ⇒ T23x path. Full DTS snippets, the surviving-paths failure modes, and the re-enable procedure are in references/emc-dvfs-disable.md.

Apply the protocol's "Recompile" through "Cleanup" steps once the multi-node edit is in place. Commit-message convention: <BPMP_BASENAME>: jetson-customize-clocks — EMC DVFS disable (bwmgr + cactmon[+ osp-controller]).

Disabling raises idle power; intended for stress / performance tests, not production rootfs.

Re-run + idempotency

Per the protocol's "Re-runnability" section, re-running this skill with the same target value produces a no-op commit. Re- running with a different value rewrites the same property — git log -- $BPMP_REL shows the per-run history. To return a clock to its max-rate-maxn ceiling, edit the DTS to remove the max-rate-custom line and recompile.

Operation 2 — nvpower.sh edits

Edits nvpower.sh, which runs at boot via nvpower.service to set cpufreq / devfreq governors and rates.

The per-script file

The script this Operation edits has the relative path:

Linux_for_Tegra/rootfs/etc/systemd/nvpower.sh

It lives in two roots; the Operation walks both:

RoleLocationSkill writes?
Detection + pristine source<bsp_image.root_path>/Linux_for_Tegra/rootfs/etc/systemd/no — read-only
Overlay edit target + git commit<source.root_path>/Linux_for_Tegra/rootfs/etc/systemd/yes

Subsequent sub-steps refer to the per-script file to mean the overlay copy under <source.root_path>. The <bsp_image.root_path> copy is read once during the pristine-import step below, then never touched again.

Overlay edit recipe (apply before editing nvpower.sh)

Follow the canonical Off-skill edits recipe in the workflow doc — pristine import + customization commit pair, both gated by the preview gate. nvpower.sh is a single file with no propagation set; one pristine commit + one customization commit covers the entire change.

Concrete substitutions for this skill:

  • <rel>/<file> is rootfs/etc/systemd/nvpower.sh.
  • Suggested pristine-import message: import pristine: rootfs/etc/systemd/nvpower.sh, body Source: <bsp_image.root_path>/Linux_for_Tegra/ (BSP <bsp_image.version>).
  • Suggested customization-commit header: jetson-customize-clocks: nvpower.sh <summary>, body lines like set_cpufreq_governor: desired_cpufreq_gov "schedutil" -> "performance".

Pick the edit

Function locations (set_cpufreq_governor, set_devfreq_governor), common-edit recipes (pin to Fmax, static rate, min/max bounds), and the nvidia-l4t-init package-upgrade caveat live in references/nvpower-sh-edits.md.

Deploy

The customization commit in the overlay tracker does not reach the device on its own. The Deploy chain:

  1. /jetson-promote-image — copies every tracked file in the overlay into <bsp_image.root_path>/Linux_for_Tegra/. Diff-aware (skip byte-identical); uses sudo cp -p for rootfs/* destinations.
  2. /jetson-flash-image — flashes the updated bsp_image to the device. nvpower.service runs the new script on the next boot.
  3. (Alternate, no flash) Copy <source.root_path>/Linux_for_Tegra/rootfs/etc/systemd/nvpower.sh directly to the running target's /etc/systemd/nvpower.sh, then sudo systemctl restart nvpower.service (or reboot).

Editing <source.root_path>/... without committing — or editing <bsp_image.root_path>/... directly — does nothing for /jetson-promote-image and is silently lost on the next /jetson-init-image re-extract.

Recipe — pin everything to MAXN for stress / performance runs

Combines Operations 1 + 2. Operation 1's BPMP edits all flow through one round of the protocol (a single decompile / multi-node edit / recompile / commit cycle — don't round-trip the protocol twice for the same .dtb):

  1. BPMP DTB (the "Content edit: max-rate-custom on a named clock node" step content): leave max-rate-custom unset on every CPU / GPU / EMC clock; remove existing max-rate-custom lines that lower the ceiling.
  2. BPMP DTB (the "Content edit: EMC DVFS disable / enable" step content): pin EMC at its init rate — bwmgr.enabled = 0, cactmon.enabled = 0, plus /delete-node/ osp-controller on T26x (skip on T23x).
  3. Apply both content edits inside one protocol "Edit the DTS" invocation, then run the remaining protocol steps (recompile, sanity-check, single customization commit covering both content edits).
  4. nvpower.sh (Operation 2): set desired_cpufreq_gov="performance" and desired_devfreq_gov="performance" unconditionally; remove the GPU/nvjpg skip in set_devfreq_governor. Applies via Operation 2's overlay edit recipe (the "Overlay edit recipe (apply before editing nvpower.sh)" step) — a separate overlay-tracker pristine + customization commit pair on the rootfs script, distinct from the BPMP-DTB protocol's commit.
  5. Set the boot-default nvpmodel mode to MAXN via /jetson-customize-nvpmodel — the per-clock nvpmodel cap clamps below max-rate-maxn regardless of BPMP DTB content.

Deploy /jetson-promote-image → /jetson-flash-image picks up the new BPMP DTB (via the overlay tracker) and the edited nvpower.sh (via the same overlay tracker) on the next flash.

Limitations

  • Image-build-time only. All edits land under <source.root_path>/Linux_for_Tegra/ and reach the device only via /jetson-promote-image → /jetson-flash-image. Live-target tuning is out of scope.
  • max-rate-custom only lowers the ceiling. It must be strictly below max-rate-maxn; raising the silicon cap is not supported.
  • Effective ceiling is two-layer. The runtime ceiling is min(BPMP cap, active-nvpmodel-mode cap). The nvpmodel cap is owned by /jetson-customize-nvpmodel; this skill does not edit it.
  • SoC-conditional EMC DVFS gate. Disabling EMC DVFS requires editing different node sets on T23x (bwmgr + cactmon) vs T26x (bwmgr + cactmon + delete osp-controller). Mis-detection produces undefined behavior.
  • T23x GPU cap is multi-node. The GPU clock is split across nafll_gpusys and every nafll_gpcX; the cap binds only when applied to all of them.
  • nvpower.sh is package-managed. It ships in nvidia-l4t-init; package upgrades clobber in-place edits. Long-lived setups should prefer a systemd drop-in or sibling helper.
  • ODMDATA wins. When an ODMDATA token covers a property, the token overrides direct BPMP DTS edits at flash time. Direct BPMP DTS edits are the fallback for properties no NVIDIA token reaches.
  • max-rate-maxn and lateinit are off-limits. max-rate-maxn is the silicon ceiling (read-only). lateinit is for boot-time clock init, not ceiling overrides — never touch either.
  • BPMP DTB may be SKU-multiplexed. On compound / dispatched flash confs (Orin AGX devkit chain), BPFDTB_FILE is selected by board_sku / board_FAB via update_flash_args_common. Reading the static BPFDTB_FILE= line is wrong when the chain conditionally overrides it; resolve via the dispatch instead.

Troubleshooting

ErrorCauseSolution
max-rate-custom set but clock still ramps to max-rate-maxn on T23x GPUOnly nafll_gpusys was capped; the nafll_gpcX partitions still run at max-rate-maxn and dominate the effective ceiling.Apply the same max-rate-custom to nafll_gpusys and every nafll_gpcX node enumerated by grep -nE '^\s*nafll_gpc[0-9]+\s*:' <decompiled.dts>.
EMC DVFS disable appears to apply but EMC still scales on T26xOnly bwmgr.enabled = <0x00> was set; osp-controller survives and re-issues frequency changes via the QoS path.Add edits #2 (cactmon.enabled = <0x00>) and #3 (/delete-node/ osp-controller) inside the same "Edit the DTS" step. Verify osp-controller via dtc -I dtb -O dts <bpmp-dtb> | grep -c osp-controller → expect 0.
EMC DVFS disable rejected on T23x with "node not found" for osp-controllerT23x (Orin) BPMP DTBs do not contain osp-controller; edit #3 must be skipped on T23x.Detect SoC family with the grep -c osp-controller step; only apply #3 when the count is ≥1.
BPMP refuses to load DTB after edit: max-rate-custom >= max-rate-maxnmax-rate-custom was set to or above the silicon ceiling.Lower max-rate-custom strictly below max-rate-maxn. If max-rate-maxn is absent from the node, query the live cap on a running target: cat /sys/kernel/debug/bpmp/debug/clk/<clock>/max_rate.
osp-controller re-appears after status = "disabled"status = "disabled" does not remove the node from the device tree; BPMP still walks it.Replace with /delete-node/ osp-controller; — the node must not exist for BPMP to skip the path.
Edits to nvpower.sh lost after apt upgradenvpower.sh is owned by the nvidia-l4t-init deb and gets overwritten on upgrade.For long-lived test setups, package edits into a systemd drop-in or a sibling helper file referenced by nvpower.sh, rather than editing nvpower.sh in place.
/jetson-promote-image is a no-op after editing the BPMP DTBThe edit was applied to <bsp_image.root_path>/Linux_for_Tegra/, which is /jetson-promote-image's output — not its input.Move the edit to <source.root_path>/Linux_for_Tegra/bootloader/<BPFDTB_FILE> (the overlay tracker) and commit through the BPMP-DTB protocol.
Cap appears to apply on first boot then resets after a power-mode changeThe active nvpmodel mode's per-clock cap clamps below max-rate-custom.Inspect both layers; if nvpmodel is binding, raise (or remove) the nvpmodel cap via /jetson-customize-nvpmodel. The BPMP cap alone is not the runtime ceiling.

References

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