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Doca Caps

OrganizationPopular
NVIDIA
doca-caps

Use this skill when the user wants to invoke the read-only doca_caps CLI to ask what DOCA sees on this host — listing DOCA devices and PCIe addresses, listing representor devices, asking which DOCA libraries are available on the current OS, checking per-device per-library capabilities, scoping output to a specific PCIe address, or capturing a side-effect-free capability snapshot for a debug session or install smoke-test. Trigger even when the user does not explicitly mention "doca_caps" or "capabilities print tool" — typical implicit phrasings include "what does DOCA actually see on this box", "is my BlueField PF visible to DOCA", "is Flow available on my RHEL host", "enumerate VF representors for pf0", "doca_caps: command not found", or "empty output for RDMA, is the tool broken". Refuse and route elsewhere for DOCA installation, library-internal capability matrices (Flow pipe creation, RDMA verbs features), streaming telemetry / DTS, or modifying the shipped binary — those belong to other skills.

Overview

PublisherNVIDIA
Repositoryskills
Skill namedoca-caps
Stars
3.3K
Forks
397
Bundled files
7
LicenseApache-2.0
Links
  • 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 Caps AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. 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.
2

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-caps .claude/skills/doca-caps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doca Caps 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 Caps 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 Caps 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 Capabilities Print Tool (doca_caps)

Where to start: This is a tool skill for invoking doca_caps, a side-effect-free CLI. Open TASKS.md and start at ## run for the documented invocations, or ## test when using doca_caps as an install smoke-test. Open CAPABILITIES.md when the question is what kinds of capability families doca_caps reports. If DOCA is not installed yet, route to doca-setup first.

Example questions this skill answers well

The CLASSES of doca_caps 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.

Audience

This skill serves external operators, developers, and AI agents who need a side-effect-free way to ask "what does DOCA see on this host?" before doing anything that changes state. Concretely:

  • An external developer who installed DOCA (or is using the public NGC DOCA container per doca-setup ## no-install) and wants to confirm the install can see hardware before writing code.
  • A platform operator deploying a DOCA service who wants a documented, read-only snapshot of which DOCA libraries this host actually supports and which capabilities each DOCA device offers.
  • An AI agent producing a capability snapshot artifact during the documented setup or programming-guide debug procedures (it's listed as the canonical first step in doca-setup ## test and doca-programming-guide ## debug).

It is not for users debugging doca_caps itself, and not a substitute for the live public Capabilities Print Tool guide.

doca_caps is shipped as a tool (a single CLI binary), not a library you link against. The skill uses the same kind: tool three-file shape as the rest of the bundle so the agent's task-verb contract (configure / build / modify / run / test / debug) is uniform across libraries, services, and tools — even when individual verbs collapse to a routing stub for a shipped read-only binary.

When to load this skill

Load this skill when the user is — or the agent needs to — invoke doca_caps on a real host with DOCA installed (or inside the public NGC DOCA container). Concretely:

  • Running doca_caps --list-devs to enumerate DOCA devices.
  • Running doca_caps --list-rep-devs to enumerate representor devices.
  • Scoping output to a specific PCIe address with --pci-addr.
  • Listing the DOCA libraries the install reports as available on the current OS.
  • Listing the available DOCA logger names.
  • Capturing a documented, side-effect-free capability snapshot as prerequisite evidence for later ## debug workflows.

Do not load this skill for general DOCA orientation, library API work, or installation. For those, use doca-public-knowledge-map, the matching libs/<library> skill, or doca-setup.

What this skill provides

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

  • CAPABILITIES.md — what doca_caps reports (the five documented capability families: devices, representors, libraries, library capabilities, loggers), version availability and execution environment, the tool's narrow error surface, its observability role inside other skills' workflows, and its read-only safety posture.
  • TASKS.md — step-by-step workflows for the in-scope task verbs: configure (route to install), build (route to install), modify (refuse), run (the documented invocations), test (capability snapshot as install smoke-test), debug (what to do when the tool reports nothing or fails), plus a Deferred task verbs block routing out-of-scope questions.

The skill assumes a host where DOCA is already installed (or the public NGC DOCA container is running) and the operator has whatever permissions the public guide requires for doca_caps to enumerate devices on their platform.

What this skill deliberately does not ship

This skill is agent guidance, not a samples or scripts bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:

  • Pre-baked example output. Output is install- and hardware-specific. A captured example pinned to one platform and one DOCA version misleads operators on a different platform / version.
  • Wrappers, parsers, or scripts in any language that consume doca_caps output. The output format is documented; if a user wants to script against it, the right answer is "read the live guide, write the parser against your installed version".
  • A samples/ or reference/ subtree. This is a thin loader for a documented CLI; substantive material lives on the public page and in --help.

Loading order

  1. Read this SKILL.md first to confirm the user's question is in scope (the user actually wants to invoke doca_caps, not learn about DOCA in general).
  2. For what doca_caps reports, version availability, error surface, and safety posture, see CAPABILITIES.md.
  3. For the documented invocations and the capability-snapshot workflow — configure, build, modify, run, test, debug — see TASKS.md.

Related skills

  • doca-public-knowledge-map — routing to the public Capabilities Print Tool guide and the rest of the public DOCA documentation set.
  • doca-setup — env preparation, install verification (doca_caps is the canonical first step there), and the I have no install yet path with the public NGC DOCA container.
  • doca-programming-guide — cross-library programming patterns, including the ## debug procedure where the saved doca_caps snapshot is consumed.
  • The matching libs/<library> skill — for fine-grained, library-specific capability questions that go beyond what doca_caps exposes.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Doca Caps AI skill do?

Use this skill when the user wants to invoke the read-only doca_caps CLI to ask what DOCA sees on this host — listing DOCA devices and PCIe addresses, listing representor devices, asking which DOCA libraries are available on the current OS, checking per-device per-library capabilities, scoping output to a specific PCIe address, or capturing a side-effect-free capability snapshot for a debug session or install smoke-test. Trigger even when the user does not explicitly mention "doca_caps" or "capabilities print tool" — typical implicit phrasings include "what does DOCA actually see on this bo...

Why use Doca Caps on TypingMind?

Because you install it once and use it with any model. Doca Caps 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 Caps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NVIDIA/skills/tree/main/skills/doca-caps. 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 Caps?

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 Caps?

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

Is the Doca Caps 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.

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