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doca-firefly

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

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  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 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:1 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-firefly" 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-firefly" 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-firefly" 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-firefly" 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-firefly" 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-firefly
description: >
  Use this skill when the user is operating the DOCA Firefly Service
  container on BlueField — picking the four PTP configuration axes
  (role / profile / domain / interface), wiring the BlueField PHC +
  host follower + consumer workload pairing, deciding whether
  PTP-grade time is even needed (vs. chrony / NTP), or debugging a
  Firefly deployment where PTP isn't syncing or the host clock isn't
  following. Trigger even when the user does not explicitly mention
  "DOCA Firefly" or "PTP" — typical implicit phrasings include
  "container green but PTP never advances past LISTENING", "Firefly
  says synced but the host clock still drifts", "sync acquired but
  offset is tens of microseconds", "my Rivermax SMPTE workload needs
  PTP", or "is chrony good enough". Refuse and route elsewhere for
  installing DOCA, host-side chrony / ptp4l config bodies, PTP
  topology / boundary-clock design, building DOCA apps that read the
  disciplined PHC, or other DOCA services (DMS, Flow-Inspector, HBN)
  — those belong to other skills.
metadata:
  kind: service
compatibility: >
  BlueField-Arm-only DOCA service container; pulled from NVIDIA NGC
  and started under the BlueField OS container runtime. Host-side
  install is irrelevant. Requires a reachable PTP master (or runs as
  the master itself) and a PTP-aware network path; the host-side
  time follower (chrony / ptp4l / phc2sys reading the BlueField PHC)
  is also operator-owned.

DOCA Firefly Service

Subsystem inventory (Run-12 correction, verified Run-13). DOCA Firefly is NOT just "a PTP daemon." The shipped doca_firefly.yaml exposes six PTP-stack subsystems via environment variables, each with its own *_STATE, *_CONFIG_FILE, and (where relevant) *_INTERFACE / *_DEVICE knobs (the count is six because the PTP Monitor subsystem ships an internal phc2sys monitor client that is distinct from the standalone PHC2SYS subsystem — both ship in the same container image):

  1. PTP (PTP_STATE, PTP_INTERFACE, PTP_CONFIG_FILE) — the ptp4l daemon (or master, depending on profile) that drives the BlueField PHC.
  2. PTP Monitor (MONITOR_STATE, MONITOR_CONFIG_FILE, MONITOR_CLIENT_TYPE, MONITOR_CLIENT_PHC2SYS_INTERFACE, MONITOR_CLIENT_CONNECTION_TIMEOUT) — the monitor server + client surface; the internal phc2sys monitor client (MONITOR_CLIENT_TYPE=phc2sys) is a real subsystem inside Firefly, not just a host-side concern.
  3. PHC2SYS (PHC2SYS_STATE, PHC2SYS_ARGS, PHC2SYS_CONFIG_FILE) — the container-internal phc2sys instance; the bundle previously framed phc2sys as host-only, which is wrong.
  4. PPS (PPS_STATE, PPS_DEVICE) — the Pulse-Per-Second output (with the additional enable_while_running and do_nothing states beyond plain enable/disable).
  5. SyncE (SYNCE_STATE, SYNCE_INTERFACE, SYNCE_CONFIG_FILE) — Synchronous Ethernet frequency distribution; orthogonal to PTP.
  6. Firefly Servo (SERVO_STATE, SERVO_CONFIG_FILE) — the proprietary Firefly servo loop (alternative to the upstream linuxptp servo).

The valid PROFILE values are exactly default / media / telco-l2 / custom (per doca_firefly.yaml comments) — the agent must not invent additional values. Subsystems configured as defined_by_profile are controlled by the active PROFILE.

Configuration-override env vars follow the pattern CONF_<SUBSYSTEM>_<section>_<key> (e.g. CONF_PTP_global_priority1, CONF_SYNCE_global_backend, CONF_MONITOR_global_telemetry_export); these are the documented surface for overriding individual config keys without shipping a full custom config file.

Configuration hierarchy: the mounted Firefly config file is mandatory and owns the primary PTP axes (role, profile, domain, interface, and transport). CONF_<SUBSYSTEM>_<section>_<key> variables are optional, documented per-key overrides of that file; they are not a second standalone configuration model.

Where to start: This skill is for operating the DOCA Firefly Service container, not for linking against a library. Firefly is the PTP / PHC2SYS / PPS / SyncE / Servo / Monitor stack that drives and observes the BlueField PTP Hardware Clock (PHC); it is not the host-side time follower, not the consumer workload, and not a programming surface. If the user wants to deploy the container, open TASKS.md and start at ## configure. If the question is what shape of service is Firefly and what PTP roles / profiles does it speak, start at CAPABILITIES.md. If DOCA is not installed on the BlueField yet, route to doca-setup first. If the user's real question is "I have a Rivermax SMPTE workload and the docs say I need PTP", the right pairing is this skill plus doca-rmax — Firefly disciplines the PHC; Rivermax reads the disciplined time.

Example questions this skill answers well

The CLASSES of Firefly questions this skill is built to answer, each with one worked example. The class is the load-bearing piece; the worked example is one instance.

  • "Do I actually need Firefly, or is NTP / chrony good enough?" — worked example: "my distributed app is fine on chrony today; is there a reason to add PTP?". Answered by the PTP-vs-NTP path- selection rule in CAPABILITIES.md ## Safety policy
  • "What four PTP configuration axes do I have to decide before starting the container?" — worked example: "a SMPTE ST 2110 broadcast plant that wants Firefly in slave role on the wire-side port". Answered by the four-axis configuration table in CAPABILITIES.md ## Capabilities and modes
  • "Firefly's container is running but the host's time isn't following — what did I miss?" — worked example: "ptp4l / Firefly says it's locked but chronyc tracking on the host shows drift". Answered by the END-TO-END time-sync discipline in CAPABILITIES.md ## Safety policy
  • "PTP locks but the offset / jitter is way past spec — what's wrong with the path?" — worked example: "sync acquired but offset is in the tens of microseconds". Answered by the PTP-aware-path rule in CAPABILITIES.md ## Safety policy
  • "How does Firefly pair with a Rivermax SMPTE workload?" — worked example: "SMPTE ST 2110 video sender that needs to be PTP- locked". Answered by the Rivermax-pairing rule in CAPABILITIES.md ## Capabilities and modes
  • "My Firefly container starts but PTP never reaches SLAVE / MASTER state — was it role, domain, profile, or interface?" — worked example: "container green but the ports-state output never advances past LISTENING". Answered by the four-axis-mismatch rule in CAPABILITIES.md ## Error taxonomy

Audience

This skill serves external operators and platform teams who deploy the DOCA Firefly Service container to provide PTP-grade time synchronization to time-sensitive workloads on BlueField + the host behind it. Concretely: people running the Firefly container on BlueField Arm, choosing its PTP role / profile / domain / interface from the public Firefly guide, wiring the host-side follower (chrony with the PHC source, or ptp4l reading the PHC) so the host clock tracks the BlueField PHC, and validating the end-to-end discipline before scaling a Rivermax, 5G UPF, financial-trading, or distributed- database workload that depends on it.

It is not for NVIDIA developers contributing to Firefly itself, and it is not a programming guide for building applications on top of DOCA libraries (that is doca-programming-guide plus the matching libs/<library> skill). Firefly is a service, not a library: the operator runs a container and configures PTP via the documented config surface; they do not link against a libfirefly.so to write their own program.

Path selection up front. Use Firefly when sub-microsecond, PTP-grade time precision is required on BlueField AND the host (SMPTE ST 2110 broadcast workloads layered on Rivermax, 5G UPF time requirements, distributed systems that need PTP-grade time, anything where NTP / chrony jitter is not tight enough). Do not reach for Firefly when NTP / chrony already meets the workload's time-precision budget, when no PTP-aware switching / boundary-clock infrastructure exists in the path, or when pure software-side time precision is sufficient — in those cases the correct answer is to keep the host's existing chrony / NTP setup and route the agent away from Firefly, not to deploy it speculatively.

When to load this skill

Load this skill when the user is doing hands-on Firefly deployment work on a BlueField where DOCA is already installed. Concretely:

  • Deciding whether Firefly is the right answer for the user's time-precision requirement (vs. keeping NTP / chrony on the host).
  • Deploying the Firefly container on BlueField Arm — choosing image source per the public DOCA Firefly Service Guide, mounting the Firefly config, and starting / stopping the container.
  • Choosing the four PTP configuration axes — PTP role (master / slave / boundary clock / transparent clock), profile (the PROFILE env var accepts EXACTLY default / media / telco-l2 / custom per services/firefly/doca_firefly.yaml; these map onto industry PTP profile names: default → IEEE 1588, media → SMPTE 2059-2, telco-l2 → G.8275.1 only (G.8275.2 corresponds to the separate telco-l3 config, reached via custom) — do NOT put the industry names directly into the env var), domain number, network interface — for the user's deployment.
  • Wiring the host-side follower so the host clock tracks the BlueField PHC (chrony with the PHC source, or ptp4l / phc2sys reading the PHC) — without this step the host clock does NOT follow the Firefly-disciplined PHC, regardless of how cleanly Firefly comes up.
  • Pairing Firefly with a time-sensitive consumer workload (Rivermax SMPTE, 5G UPF, finance, distributed databases) and validating the end-to-end discipline.
  • Reading the Firefly container's logs, the PHC offset, the ports-state output, or any other documented observability surface to confirm PTP is locked.
  • Debugging a Firefly deployment where the container is healthy but PTP is not syncing, or PTP is syncing but the host clock is not following, or sync is up but jitter is past spec.

Do not load this skill for general DOCA orientation, install of DOCA itself, library-API questions, or non-PTP time topics. For those, route via doca-public-knowledge-map, doca-setup, or the matching libs/<library> skill.

What this skill provides

This is a thin loader. Substantive material lives in two companion files:

  • CAPABILITIES.md — Firefly's architecture (container that drives the BlueField PHC and speaks PTP on the wire), the four PTP configuration axes (role / profile / domain / interface, with transport as a fifth knob), the deployment shape (container on BlueField Arm per the public Container Deployment Guide), the pairing surface (Rivermax + host-side time-sync follower), the observability surface (container logs + PHC offset + ports state), the error taxonomy (four-axis-mismatch / host-follower / PTP-aware- path / container-runtime), and the safety policy (PTP-vs-NTP path selection, END-TO-END discipline, smoke-before-scale).
  • TASKS.md — step-by-step workflows for the in-scope Firefly verbs: configure, build, modify, run, test, debug, plus a Deferred task verbs block routing out-of-scope questions and a Command appendix of recurring commands.

The skill assumes a BlueField where DOCA is already installed and the operator has the privileges the public Firefly Service Guide expects to pull, run, and configure containers on BlueField Arm. 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 templates or sample-config bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:

  • Pre-baked Firefly configuration files (full PTP config blocks, ready-to-run role / profile / domain bundles) intended to be copy-pasted into production. PTP configuration is deployment- specific (per the user's profile, domain plan, interface naming, and upstream PTP topology); the safe answer for an external operator is to derive the config from the public Firefly Service Guide against their own deployment. The agent's job is to prescribe the procedure and the four-axis decision, not to ship a config the user might run unmodified.
  • Container image names, tags, or registry paths. The authoritative image source is the public DOCA Firefly Service Guide reachable through doca-public-knowledge-map ## DOCA services; Firefly's image tag is version-bound and changes between DOCA releases. Inventing or memorizing a tag is the canonical hallucination failure mode for a service skill.
  • Host-side chrony stanzas or ptp4l / phc2sys config files. Those are host-environment-specific and live on the host, not inside the Firefly container. The skill names that the host-side follower must be wired and what its source must be (the BlueField PHC); the chrony / ptp4l config bodies belong to the host operator and to upstream Linux PTP documentation.
  • A samples/, templates/, or reference/ subtree of any kind. A mock or incomplete artifact in this skill's tree, even one labeled "reference", is misleading: operators will read it as production-ready.

Loading order

  1. Read this SKILL.md first to confirm the user's question is in scope and that Firefly is the right answer at all (vs. keeping NTP / chrony on the host).
  2. For Firefly's deployment shape, the four PTP configuration axes, the Rivermax + host-follower pairing surface, the error taxonomy, the observability surface, and the END-TO-END safety policy, see CAPABILITIES.md.
  3. For step-by-step workflows — configure, build, modify, run, test, debug — see TASKS.md.

Related skills

  • doca-public-knowledge-map — the routing table to the public DOCA Firefly Service Guide and the rest of the public DOCA documentation set. The Firefly URL is listed under ## DOCA services.
  • doca-setup — env preparation and install verification on the BlueField where the Firefly container will run, including the I have no install yet path via the public NGC DOCA container. This skill assumes its preconditions are satisfied on BlueField Arm.
  • doca-version — canonical DOCA version-handling rules. Firefly's container tag is version-bound; this skill's ## Version compatibility cross-links the four-way match rule and adds the container-tag-lags-host-package overlay.
  • 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 patterns. Firefly is service-shaped not library- shaped, so the build / modify / first-app pattern there does not apply directly, but the cross-library debug discipline (frontend- before-backend, env-before-program) remains useful when Firefly reports an error that originated in the container runtime or in a DOCA library it called.
  • doca-rmax — the canonical paired workload. SMPTE ST 2110 Rivermax streams depend on a Firefly-disciplined PHC; Firefly is the time-source side and Rivermax is the timing-precise data-plane side. The two skills load together for any broadcast-style deployment, and they do NOT collapse into one another — Firefly does not stream media; Rivermax does not discipline the PHC.
  • doca-dms — sibling service skill. The agent reading both skills should see the same service-skill shape (container, BlueField Arm, deployment pattern, smoke-before-scale, env preconditions, config schema) layered on top of a different per-service domain (DMS = device management via gNMI / gNOI; Firefly = time synchronization via PTP).
  • doca-debug — the cross-cutting debug ladder (install / version / build / link / runtime / program / driver). Firefly-specific debug (PTP not syncing, host clock not following, jitter past spec) overlays on top of that ladder.

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