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SkillAtlasSkill 详情
doca-bare-metal-deployment
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
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
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
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
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
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:
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 agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_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 againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
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.
DevOps 与部署
低风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：0 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/doca-bare-metal-deployment" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/doca-bare-metal-deployment" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/doca-bare-metal-deployment" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/doca-bare-metal-deployment" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/doca-bare-metal-deployment" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
license: Apache-2.0
name: doca-bare-metal-deployment
description: >
Use this skill for launching, supervising, debugging, OR
platform lifecycle on a BlueField — BFB install, RShim/TMFIFO,
host PF rebind, post-BFB recovery — taking a DOCA-linked binary
to a healthy run directly on hardware (host x86 + BlueField NIC
over PCIe, or BlueField Arm bare-metal). No container, no
kubelet. Covers launch mode (direct, tmux, systemd), PCI/NUMA/
CPU/IRQ binding, co-tenant isolation (cgroup-v2/netns/numactl),
a seven-layer error taxonomy, and a six-state BlueField
lifecycle classifier. Trigger even when user does not say
"bare-metal" — implicit phrasings include "binary exits 1 right
after launch", "systemd keeps restarting it", "no matching
device on the BF", "bfb-install exited 0 but DPU is dead",
"ping 192.168.100.2 works but ssh fails", "host PFs aren't
showing netdevs". Destructive firmware burn / mlxconfig set
requires explicit confirmation via doca-hardware-safety;
containers, library APIs, env prep, and build use other skills.
metadata:
kind: library
compatibility: >
No DOCA install required to read this skill (it is an overlay
loaded against any DOCA artifact skill); the validation steps
within this skill require a live DOCA install at /opt/mellanox/doca on
a host or BlueField with a built DOCA-linked binary.DOCA bare-metal deployment
Where to start: This skill is the bundle's home for operating
a DOCA-linked application binary directly on hardware — no
container, no kubelet, no static-pod manifest. It is the parallel
of doca-container-deployment
for the non-container path. If the user has a DOCA-linked binary
they built (per the canonical workflow in
doca-programming-guide)
and they want to know how to actually run it on the host or on
the BlueField Arm cores correctly, open
TASKS.md and start at
## configure. If the question is what
shape does the bare-metal runtime even have and what is the
deployment contract, start at CAPABILITIES.md.
If the user is not yet sure whether their target system shape is
the container path or the bare-metal path, route the recognition
step to doca-setup first; only return
here once bare-metal is the confirmed shape.
Audience
This skill serves external DOCA developers and operators who have a DOCA-linked application binary they built and want to run it directly on hardware — i.e., people who already have:
- a DOCA-linked application binary they built per
doca-programming-guide ## build, - a real BlueField NIC and a host that talks to it (the host x86 path — DOCA host install on the host talks to the BlueField NIC over PCIe), OR a BlueField with a console or SSH to the Arm side (the BlueField Arm bare-metal path — DOCA installed on the DPU Arm cores; the binary runs there directly), and
- a desire to RUN that binary directly on the hardware, not inside a kubelet-standalone-managed container.
It is not for:
- kernel-driver developers contributing to
mlx5_*or the BlueField OS, - DOCA library contributors (those changes go to the internal DOCA tree, not to a bare-metal deployment),
- full-Kubernetes-cluster operators managing a fleet of
BlueFields (the bundle covers
doca-container-deploymentfor the single-host kubelet-standalone shape; fleet/production-scale deployment is fleet-orchestration scope — route to the orchestration entry-point indoca-public-knowledge-map ## Deploying DOCA services at scale(DPF / Network Operator / Launch Kit), not hand-rolled static-pod loops), - fresh-laptop-no-hardware users with no DOCA install yet — those
belong on
doca-setup ## no-install.
The skill teaches the agent the bare-metal-deployment procedure
and the rules for quoting documented commands from the public DOCA
Programming Guide and the public BlueField / DPU User Manual via
doca-public-knowledge-map;
it does not invent flag names, PCI BDFs, NUMA numbers, devlink
paths, representor strings, or systemd Restart= mode names from
memory.
When to load this skill
Load this skill when the user is doing hands-on bare-metal deployment of a DOCA-linked application binary on either of the two supported host modes (host x86 or BlueField Arm), or asking a cross-cutting bare-metal question that is not specific to one library's API. Concretely:
- Launching a DOCA-linked binary for the first time on a host with a BlueField NIC in a PCIe slot, with DOCA installed on the host.
- Launching a DOCA-linked binary on the BlueField Arm cores directly (BlueField Arm bare-metal mode), with DOCA installed on the Arm side per the BlueField OS image.
- Deciding which launch mode to use (direct foreground for interactive debug; tmux/screen for long-running with manual reattach; systemd-supervised for restart-after-reboot, journald-integrated logs, and Restart= policy).
- Binding the DOCA process to the right PCIe function, the right representor, the right NUMA node, and the right CPU set — and pinning IRQs to match — without inventing the addresses or the flag names.
- Setting up per-tenant isolation (cgroup-v2 cpu / memory / io
controllers, network namespaces for multi-tenant deployments,
numactl/tasksetfor CPU + NUMA binding) so multiple DOCA processes co-tenant on the same BlueField without crushing each other. - Diagnosing a bare-metal launch that is misbehaving — won't start, starts and exits immediately, runs but can't find the device, attaches to the device but the workload errors, OOMs or is signal-killed, is in a restart loop under a supervisor, or is being interfered with by a co-tenant.
- Cross-cutting questions: "should I run this in tmux or as a systemd unit", "what is the smoke-before-bulk loop for a binary on bare metal", "my binary works in a container on the BlueField but not when I run it directly on the Arm — what changed".
Do not load this skill for the container-path equivalent
(those questions go to
doca-container-deployment);
for full-Kubernetes-cluster operations (out of scope per the
bundle's non-goals); for library-API questions (route to the
matching libs/<library> skill); for env-preparation questions
including hugepages, IOMMU, pkg-config, and devlink mode flips
(use doca-setup); for any
hardware-state-changing operation including mlxconfig writes
and BFB reflashes (route to
doca-hardware-safety for the
cross-cutting meta-policy); or for cross-library programming
questions (use
doca-programming-guide).
What this skill provides
This is a thin loader. Substantive material lives in two companion files:
CAPABILITIES.md— the bare-metal deployment runtime contract for a DOCA-linked binary: the two host modes (host x86 vs BlueField Arm bare-metal), the three launch modes (direct, tmux/screen, systemd-supervised), the hardware-resource-binding surface (PF / VF / representor enumeration; NUMA topology discovery; CPU pinning rationale; IRQ affinity rules), the per-tenant isolation surface (cgroup-v2 cpu / memory / io, network namespaces,numactl/taskset), the restart and recovery semantics (documentedsystemdRestart=modes vs crash-and-investigate vs supervisor-driven restart), the bare-metal-specific version overlay on the four-way version match owned bydoca-version, the cross-cutting error taxonomy (seven layers, walked in order), the observability surface (stdout/stderr discipline by launch mode; device-state introspection viadevlink/sysfs/mlxconfigquery; per-tenant resource visibility), and the safety policy (overlay ondoca-hardware-safety: smoke-before-bulk for binaries; failed bare-metal process is HIGH-STAKES; do not invent PCI addresses, NUMA numbers, representor names, devlink paths, or systemdRestart=mode names; confirm tenant-isolation primitives BEFORE the workload starts).TASKS.md— step-by-step workflows for the in-scope bare-metal verbs:configure,build,modify,run(with an explicit### isolationsub-anchor covering cgroup-v2 / namespaces / numactl per-tenant primitives),test,debug,bluefield-lifecycle(the BFB-install → RShim/TMFIFO → post-BFB-recovery operational sequencing ladder, with the six-statebluefield-state-classifiersub-anchor), theCommand appendix(documented commands the agent may quote, each cross-linked to its public-doc source — no invented commands), and theDeferred task verbsblock routing container-path / cluster / library-API / env-prep / hardware-state-change / cross-library questions out to their owning skills. (The change-application discipline for any mutating burn invoked from## bluefield-lifecycleis still meta-policy owned bydoca-hardware-safety, loaded alongside.)
The skill assumes a host or BlueField target where:
- DOCA is already installed and healthy (per
doca-setup ## test), - the user has a DOCA-linked application binary they built (per
doca-programming-guide ## build), - the user has the host-OS permissions to enumerate devices, reserve hugepages, write systemd units (if they choose that launch mode), and bind processes to NUMA nodes.
It does not cover installing DOCA — that path goes through
doca-setup — and it does not cover
building the binary — that path goes through
doca-programming-guide.
Loading order
- Read this
SKILL.mdfirst to confirm the user's question is in scope (bare-metal launch of a DOCA-linked binary on host x86 or BlueField Arm; NOT the container path, NOT a full cluster, NOT a library-API question). - For the runtime contract (two host modes, three launch modes, hardware-binding surface, per-tenant isolation, version overlay, seven-layer error taxonomy, observability surface, bare-metal safety overlay), see CAPABILITIES.md.
- For step-by-step workflows —
configure,build(routing stub),modify(routing stub),run(with### isolationsub-anchor),test,debug,bluefield-lifecycle(BFB install + RShim/TMFIFO + post-BFB recovery + the six-statebluefield-state-classifier), plus theCommand appendixand theDeferred task verbsblock — see TASKS.md.
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