Doca Bare Metal Deployment by NVIDIA — AI Skill for Any LLM

Overview

Publisher	NVIDIA
Repository	`skills`
Skill name	`doca-bare-metal-deployment`
Stars	3.3K
Forks	397
Bundled files	7
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions

A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

Works with any LLM

AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

7 bundled files

Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

Open source

Published by NVIDIA on GitHub. Read the source before you install it.

Installation

Install the Doca Bare Metal Deployment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/NVIDIA/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/doca-bare-metal-deployment .claude/skills/doca-bare-metal-deployment

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doca Bare Metal Deployment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Doca Bare Metal Deployment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Doca Bare Metal Deployment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed

The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

Switch models mid-chat

Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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-deployment for the single-host kubelet-standalone shape; fleet/production-scale deployment is fleet-orchestration scope — route to the orchestration entry-point in doca-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 / taskset for 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 (documented systemd Restart= modes vs crash-and-investigate vs supervisor-driven restart), the bare-metal-specific version overlay on the four-way version match owned by doca-version, the cross-cutting error taxonomy (seven layers, walked in order), the observability surface (stdout/stderr discipline by launch mode; device-state introspection via devlink / sysfs / mlxconfig query; per-tenant resource visibility), and the safety policy (overlay on doca-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 systemd Restart= 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 ### isolation sub-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-state bluefield-state-classifier sub-anchor), the Command appendix (documented commands the agent may quote, each cross-linked to its public-doc source — no invented commands), and the Deferred task verbs block 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-lifecycle is still meta-policy owned by doca-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.md first 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 ### isolation sub-anchor), test, debug, bluefield-lifecycle (BFB install + RShim/TMFIFO + post-BFB recovery + the six-state bluefield-state-classifier), plus the Command appendix and the Deferred task verbs block — see TASKS.md.

Frequently asked questions

What does the Doca Bare Metal Deployment AI skill do?

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...

Why use Doca Bare Metal Deployment on TypingMind?

Because you install it once and use it with any model. Doca Bare Metal Deployment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Doca Bare Metal Deployment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA/skills/tree/main/skills/doca-bare-metal-deployment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Doca Bare Metal Deployment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Doca Bare Metal Deployment?

As many as you like. As long as a model supports skills, you can use Doca Bare Metal Deployment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Doca Bare Metal Deployment AI skill free?

Yes. It is published on GitHub by NVIDIA under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

DOCA bare-metal deployment

Audience

When to load this skill

What this skill provides

Loading order

Example questions this skill answers well

What this skill deliberately does not ship

Related skills

Bundled files

Frequently asked questions

Related AI Skills

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