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Doca Caps

NVIDIAOfficialCreated by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk
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From the author

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 debug 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 (doca-setup owns it; never walk the steps), library-internal capability matrices (Flow pipe creation, RDMA verbs features), streaming telemetry / DTS, or modifying the shipped binary.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The doca-caps skill provides instructions for using the doca_caps command-line tool, a read-only utility designed to report the state of DOCA on a host. It enables users to enumerate DOCA devices, identify available libraries, list representor devices, and capture capability snapshots for debugging or installation verification. This skill acts as a thin loader, directing users to specific companion files for detailed capability reporting and task-based workflows. It is strictly limited to read-only operations and does not perform DOCA installations or library-internal configuration.

What it does

  • Enumerates DOCA devices and their associated PCIe addresses.
  • Lists DOCA libraries available on the current operating system.
  • Identifies visible representor devices for specific PFs.
  • Captures side-effect-free capability snapshots for debug or smoke-test purposes.
  • Scopes capability output to specific PCIe addresses.
  • Interprets empty or missing output from the tool.

When to use it

  • When you need to verify if DOCA hardware is visible on a host.
  • When you need to confirm which DOCA libraries are supported on your current OS.
  • When you need to generate a capability snapshot to attach to a debug session.
  • When you need to perform a quick smoke-test of a DOCA installation.
  • When you need to identify representor devices for network configuration.

Advantages

  • Provides a standardized, side-effect-free way to inspect system state.
  • Helps verify installation health without modifying system configuration.
  • Facilitates consistent data collection for debugging and support.
  • Integrates cleanly with other DOCA-related workflows and skills.

Usage examples

“What DOCA devices does this host see?”
The agent invokes doca_caps --list-devs to provide a list of visible DOCA devices and their PCIe addresses.
“Is Flow available on my RHEL host?”
The agent checks the library-availability surface and invokes the tool to confirm if the Flow library is present.
“Enumerate VF representors for pf0.”
The agent uses the tool to list representor devices, specifically filtering for those associated with pf0.
“Save device and library capability output to a file.”
The agent executes the snapshot workflow to capture the current system state into a file for later debugging.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/doca-caps/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip doca-caps.zip -d .agents/skills

Documentation by NVIDIA

DOCA Capabilities Print Tool (doca_caps)

Examples

  • "What DOCA devices does this host see?" — this is in scope. Stay on doca_caps; do not start a library-internal capability matrix here.

  • "Is Flow available on my RHEL host?" — list libraries then scope per-device if needed.

Instructions

Follow Loading order below. Open the tasks companion for run / test. Open the capabilities companion for what the CLI reports. Do not open every companion file.

Where to start: This is a tool skill for invoking doca_caps, a side-effect-free CLI. Open the tasks companion and start at ## run for the documented invocations, or ## test when using doca_caps as an install smoke-test. Open the capabilities companion when the question is what kinds of capability families doca_caps reports.

Boundary: this skill never installs DOCA. doca_caps verifies an install and never performs one. A request to install DOCA — choosing packages, adding repositories, running a package manager — is out of scope here, and stays out of scope even though this skill's ## test verb uses the tool as an install smoke-test afterwards. Hand the whole request to doca-setup/SKILL.md by name. Naming the owner and then listing the steps anyway is the failure mode to avoid: do not walk the installation, not even as a summary or a caveat.

This narrows installation only. It is not a reason to answer narrowly about tooling: when the user's question reaches past doca_caps — what else can I ask this hardware, how do I measure it, how do I inspect a running pipeline — name the sibling tool that owns it (doca_bench, doca_flow_tune, pcc_counters.sh, doca_comm_channel_admin, …) and tie it to the question. Pointing at the rest of the catalog is this skill's job, not a boundary breach.

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.

  • "What DOCA devices does this host see?" — worked example: "is my BlueField PF visible to DOCA". Answered by the device enumeration in the capabilities companion

    • the --list-devs invocation in the tasks companion.

  • "Which DOCA libraries are available on this OS?" — worked example: "is Flow available on my RHEL host". Answered by the library-availability surface in the capabilities companion

    • the library-listing invocation in the tasks companion.

  • "Does this device support library X capability Y?" — worked example: "does this device support Flow hairpin?". Answered by the per-device per-library capability matrix in the capabilities companion

    • the --pci-addr-scoped invocation in the tasks companion.

  • "What representors are visible to DOCA?" — worked example: "enumerate VF representors for pf0". Answered by the representor enumeration in the capabilities companion

    • the --list-rep-devs invocation in the tasks companion.

  • "I want a snapshot of state to attach to my debug session." — worked example: "save device + library + capability output to a file". Answered by the snapshot workflow in the tasks companion and consumed by doca-debug/SKILL.md step 3 (read-only triple) and doca-programming-guide/SKILL.md.

  • "doca_caps returned nothing for capability Y — what does that mean?" — worked example: "empty output for RDMA". Answered by the empty-output interpretation rules in the tasks companion + the capabilities companion.

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/SKILL.md) 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/SKILL.md and doca-programming-guide/SKILL.md).

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/SKILL.md, the matching libs/<library> skill, or doca-setup/SKILL.md.

What this skill provides

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

  • the capabilities companion — 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.

  • the tasks companion — 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 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 the capabilities companion.

  3. For the documented invocations and the capability-snapshot workflow — configure, build, modify, run, test, debug — see the tasks companion.

Related skills

  • doca-public-knowledge-map/SKILL.md — routing to the public Capabilities Print Tool guide and the rest of the public DOCA documentation set.

  • doca-setup/SKILL.md — 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/SKILL.md — 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.

Files

skill-card.md

Description: <br>

Guides an agent to invoke the read-only doca_caps CLI to report what DOCA sees on a host — enumerating DOCA devices and PCIe addresses, listing representor devices and available DOCA libraries, checking per-device per-library capabilities, and capturing side-effect-free capability snapshots for debug or install smoke-tests. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 AND CC-BY-4.0 <br>

Use Case: <br>

External operators, developers, and AI agents use this skill to get a documented, side-effect-free way to ask what DOCA sees on a host — confirming an install can see hardware, listing which DOCA libraries and capabilities a device supports, and capturing a read-only capability snapshot as evidence before performing any state-changing work. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Shell commands, Configuration instructions, Analysis] <br> Output Format: [Markdown with inline bash code blocks] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-5-5) <br>

  • Codex (openai/openai/gpt-6.1-sol) <br>

Evaluation Tasks: <br>

4 evaluation tasks (3 positive, 1 negative), one attempt per task, each attempt run in its own isolated k8s-sandbox pod. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Is it safe to use? Checks unsafe operations, secret leakage, and unauthorized access. <br>

  • Correctness: Is the answer correct? Checks final-answer correctness against the reference answer. <br>

  • Discoverability: Was the right skill loaded when needed, decoys avoided, and the workflow executed. <br>

  • Effectiveness: Did the skill help complete the task — equal-weight mean of goal completion and expected workflow adherence. <br>

  • Efficiency: Did it avoid wasted tool calls and token usage — tool-call productivity plus uncached token usage. <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • skill_efficiency: Tool-call productivity (legacy wire id; routing is scored under Discoverability). <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • token_efficiency: Actual uncached prompt plus completion usage (50% of Efficiency). <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall90.1% — baseline ran, but no comparable score was available; uplift unavailable94.4% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)75.0% → 100.0% (+25.0 points)
Correctness65.0% → 90.0% (+25.0 points)90.0% → 100.0% (+10.0 points)
Discoverability98.3% — baseline ran, but no comparable score was available; uplift unavailable95.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness25.0% → 75.0% (+50.0 points)62.5% → 100.0% (+37.5 points)
Efficiency87.3% — baseline ran, but no comparable score was available; uplift unavailable76.9% — baseline ran, but no comparable score was available; uplift unavailable

Skill Version(s): <br>

259da15 (source: git SHA, committed 2026-10-28) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: Requires DOCA SDK ≥ 2.6.0 installed at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or RHEL/SLES) with a BlueField DPU or ConnectX NIC; runs identically on the host or on the BlueField Arm side. Invokes /opt/mellanox/doca/tools/doca_caps and reads `pkg-config doca-common` to confirm the install. The validation steps require a live DOCA install.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/doca-caps · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-caps.zip