Doca Eth logo

Doca Eth

OrganizationPopular
NVIDIA
doca-eth

Use this skill for hands-on DOCA Ethernet packet-queue work on a BlueField DPU or ConnectX NIC — bringing up a `doca_eth_rxq` or `doca_eth_txq` on a port / representor / SF, picking among the four `enum doca_eth_rxq_type` values (`_REGULAR` / `_CYCLIC` / `_MANAGED_MEMPOOL` / `_SHARED_MEMPOOL`), sizing burst or scatter-gather length against the `_cap_*` queries, submitting `doca_eth_txq_task_send` / `_lso_send` (carrying packet `doca_buf`s — no `doca_eth_frame` struct exists), or debugging DOCA_ERROR_* from an Ethernet call. Trigger on implicit phrasings: "my RX queue is up but no packets arrive", "send-task returns AGAIN at line rate", "which queue type for fixed-MTU ingress", "device open fails without sudo", or "is L3 checksum offload available here". Refuse and route elsewhere for installing DOCA, flow-rule / steering programming, host↔DPU control messaging, or RDMA data movement.

Overview

PublisherNVIDIA
Repositoryskills
Skill namedoca-eth
Stars
3.5K
Forks
425
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 Eth 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.
    https://github.com/NVIDIA/skills/tree/main/skills/doca-eth
  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-eth .claude/skills/doca-eth
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doca Eth 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 Eth 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 Eth 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 Ethernet

Where to start: This skill assumes DOCA is already installed and the user is doing hands-on packet-queue work on a host or BlueField with DOCA. Open TASKS.md if the user wants to do something (configure / build / modify / run / test / debug); open CAPABILITIES.md when the question is what can a DOCA Ethernet queue express on this version. If the user has not installed DOCA yet, route to doca-setup first. If the user is asking "how do I get packets to land on my RX queue at all", the answer lives in doca-flow — DOCA Ethernet is the queue surface; DOCA Flow is the steering surface, and they are independent libraries.

Example questions this skill answers well

The CLASSES of DOCA Ethernet questions this skill is built to answer, each with one worked example. The agent should treat the class as the load-bearing piece — the worked example is a single instance.

  • "How do I bring up an RX and TX queue on a representor or physical port?" — worked example: "set up a doca_eth_rxq plus a doca_eth_txq on a single BlueField representor for first-run testing". Answered by the queue-pair lifecycle in TASKS.md ## configure + CAPABILITIES.md ## Capabilities and modes RX / TX object table.
  • "Which RX type fits my data shape — regular, cyclic, or managed-recv?" — worked example: "line-rate ingress with fixed-size frames into a pre-allocated buffer ring". Answered by the RX-type taxonomy in CAPABILITIES.md ## Capabilities and modes
    • the capability-query rule (doca_eth_rxq_cap_is_type_supported against a doca_devinfo) in TASKS.md ## configure.
  • "How do I send a packet from user code through doca_eth_txq?" — worked example: "allocate a packet doca_buf, attach the payload, submit one send-task, wait for the completion event". Answered by the TX submission shape in CAPABILITIES.md ## Capabilities and modes
  • "My queue is up but no packets arrive — why?" — worked example: "doca_eth_rxq started cleanly but the recv callback never fires". Answered by the steering-dependency rule in CAPABILITIES.md ## Safety policy
  • "Is this Ethernet capability available on my device + my installed DOCA?" — worked example: "does this device advertise L3 checksum offload". Answered by the capability-query rule (doca_eth_txq_cap_is_l3_chksum_offload_supported against a doca_devinfo) in CAPABILITIES.md ## Capabilities and modes
  • "What does this DOCA_ERROR_* from an Ethernet call mean and which layer caused it?" — worked example: "DOCA_ERROR_AGAIN on doca_task_submit for an eth_txq send-task at high rate". Answered by the Ethernet overlay on the cross-library taxonomy in CAPABILITIES.md ## Error taxonomy

Audience

This skill serves external developers building applications that consume the DOCA Ethernet library — i.e., users whose code calls doca_eth_rxq_* / doca_eth_txq_* (directly in C/C++, or through FFI/bindings from another language) to do line-rate packet I/O on physical ports, representors, or SFs of a BlueField or ConnectX device. It is not for NVIDIA developers contributing to DOCA Ethernet itself.

Language scope. DOCA Ethernet ships as a C library with pkg-config module name doca-eth. The shipped samples are written in C. C and C++ consumers are the canonical case; the worked examples in TASKS.md assume that path. Other-language consumers (Rust, Go, Python, …) consume the same *.so through FFI or language-specific bindings; the skill's contribution in that case is to keep the queue-object split, lifecycle, capability-discovery, permission, RX-type taxonomy, and error-taxonomy guidance language-neutral, and to route the agent to the public C ABI as the authoritative surface that any wrapper will eventually call.

When to load this skill

Load this skill when the user is doing hands-on DOCA Ethernet work, in any language. Concretely:

  • Initializing a doca_eth_rxq on a doca_dev opened against a physical port, a representor, or an SF — choosing among the regular / cyclic / managed-recv RX types based on data shape before doca_ctx_start().
  • Initializing a doca_eth_txq on the same or a different doca_dev and posting send-tasks against packet doca_bufs payload buffers.
  • Reading or setting Ethernet queue properties via doca_eth_rxq_set_* / doca_eth_txq_set_* and querying device capability via doca_eth_rxq_cap_* / doca_eth_txq_cap_* (max burst size, RX-type support, max scatter-gather length, checksum-offload presence).
  • Confirming the port is up and that traffic is actually steered to the chosen RX queue — either via DOCA Flow rules (the canonical path) or via kernel-side promiscuous mode (the expedient first-run path).
  • Wiring an Ethernet queue to a higher-level data plane: GPU packet processing via DOCA GPUNetIO, custom user-space forwarding agents, or telemetry mirrors that snap a copy of every packet.
  • Debugging a DOCA_ERROR_* returned from an Ethernet call (lifecycle vs. permission vs. capability vs. send-queue-full vs. driver-below) and the per-queue progress engine events.
  • Designing or extending non-C bindings (Rust, Go, Python, …) that wrap the DOCA Ethernet C ABI — for the lifecycle, queue split, RX-type, capability, and permission rules the wrapper must honor.

Do not load this skill for general DOCA orientation, install of DOCA itself, flow-rule programming (use doca-flow), host ↔ DPU control messaging (use doca-comch), or RDMA data movement (use doca-rdma). For DOCA documentation orientation, use doca-public-knowledge-map.

What this skill provides

This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive Ethernet- specific material lives in two companion files:

  • CAPABILITIES.md — what a DOCA Ethernet queue can express on this version: the RX / TX object split, the RX-type taxonomy (regular / cyclic / managed-recv), the send-task / frame submission surface, the capability-query surface (doca_eth_rxq_cap_* / doca_eth_txq_cap_*), the Ethernet error taxonomy (mapped onto the cross-library DOCA_ERROR_* set), the observability surface (per-queue progress engine events, capability snapshots), and the safety policy that gates the steering / permission / port-state preconditions.
  • TASKS.md — step-by-step workflows for the six in-scope Ethernet verbs: configure, build, modify, run, test, debug. Plus a Deferred task verbs block that points out-of-scope questions at the right next skill, and a Command appendix of the recurring commands the agent reaches for.

The skill assumes a host or BlueField where DOCA is already installed at the standard location and the user has the privileges their public install profile expects (typically sudo or mlnx-group membership to open a doca_dev against a port). 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 samples or templates bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:

  • Pre-written DOCA Ethernet application source code, in any language. The verified Ethernet source code is the shipped C samples at /opt/mellanox/doca/samples/doca_eth/<name>/. The agent's job is to route the user to those files and prescribe a minimum-diff modification on them via the universal modify-a-sample workflow in doca-programming-guide, layered with the Ethernet-specific overrides in TASKS.md ## modify.
  • Standalone build manifests (meson.build, CMakeLists.txt, Cargo.toml, …) parked inside the skill. The agent constructs the build manifest in the user's project directory against the user's installed DOCA, where pkg-config --modversion doca-eth is the source of truth.
  • A samples/, bindings/, or reference/ subtree of any kind. A mock or incomplete artifact in this skill's tree, even one labeled "reference", is misleading: users will read it as buildable.

Loading order

  1. Read this SKILL.md first to confirm the user's question is in scope.
  2. For the Ethernet capability matrix, RX-type taxonomy, send surface, capability-query rules, error taxonomy, observability, and safety policy, see CAPABILITIES.md.
  3. For step-by-step workflows — configure, build, modify, run, test, debug — see TASKS.md.

Both companion files cross-link to each other, doca-version for the canonical version-handling rules, doca-flow for the steering side that the RX queue depends on, and doca-public-knowledge-map whenever the right answer is "look it up in the public docs or the installed package layout" rather than "Ethernet-specific guidance".

Related skills

  • doca-public-knowledge-map — the routing table for every public DOCA documentation source and the on-disk layout of an installed DOCA package. The Ethernet URL slug is DOCA-Ethernet.
  • doca-setup — env preparation, install verification, port-state checks (devlink dev show, ip link), permission and group-membership requirements for opening a doca_dev. This skill assumes its preconditions are satisfied.
  • doca-version — canonical DOCA version-handling rules. This skill's ## Version compatibility cross-links the four-way match rule and adds only the Ethernet-specific overlay (RX-type availability windows, checksum-offload device-conditional support).
  • 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 programming patterns shared by every library: the canonical pkg-config + meson build pattern, the universal modify-a-shipped-sample first-app workflow, the universal lifecycle, the cross-library DOCA_ERROR_* taxonomy, and the program-side debug order. This skill layers Ethernet specifics on top.
  • doca-flow — the steering surface that decides which packets land on which doca_eth_rxq. DOCA Ethernet does not program steering itself; an empty RX queue almost always means a missing or wrong Flow rule, not an Ethernet bug.
  • doca-debug — the cross-cutting debug ladder (install / version / build / link / runtime / program / driver). Ethernet-specific debug (RX-type mismatches, send-queue-full retries, steering-empty-queue symptoms) overlays on top of that ladder.

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 Eth AI skill do?

Use this skill for hands-on DOCA Ethernet packet-queue work on a BlueField DPU or ConnectX NIC — bringing up a `doca_eth_rxq` or `doca_eth_txq` on a port / representor / SF, picking among the four `enum doca_eth_rxq_type` values (`_REGULAR` / `_CYCLIC` / `_MANAGED_MEMPOOL` / `_SHARED_MEMPOOL`), sizing burst or scatter-gather length against the `_cap_*` queries, submitting `doca_eth_txq_task_send` / `_lso_send` (carrying packet `doca_buf`s — no `doca_eth_frame` struct exists), or debugging DOCA_ERROR_* from an Ethernet call. Trigger on implicit phrasings: "my RX queue is up but no packets ar...

Why use Doca Eth on TypingMind?

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

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

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 Eth?

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

Is the Doca Eth 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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