Doca Debug logo

Doca Debug

OrganizationPopular
NVIDIA
doca-debug

Use this skill when the user is debugging any DOCA symptom — a build that won't compile, a link step that can't resolve a doca_* symbol, a runtime call returning DOCA_ERROR_*, a silent service or tool, or a stack trace / valgrind / core dump — and needs the layered ladder (install → version → build → link → runtime → program → driver), verbosity controls (--sdk-log-level, DOCA_LOG_LEVEL, the doca-{lib}-trace flavor), container-debug constraints, or how to capture state for a Developer Forum post. Trigger even when the user does not say "DOCA debug" — implicit phrasings include "undefined reference to doca_*", "how do I get more logs", "packets aren't reaching the wire", "doca_caps returned nothing", or "hugepages empty in the container". Refuse and route elsewhere for library-specific debug (Flow pipe trace, RDMA QP, Comch stats), env-class pkg-config or hugepages symptoms, the DOCA_ERROR_* taxonomy and lifecycle interpretation, and performance or incident-response work — those belong to other skills.

Overview

PublisherNVIDIA
Repositoryskills
Skill namedoca-debug
Stars
3.3K
Forks
397
Bundled files
6
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.

  • 6 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 Debug 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-debug .claude/skills/doca-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doca Debug 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 Debug 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 Debug 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 debug

Where to start: This skill is the canonical layered-ladder reference both doca-setup ## debug and doca-programming-guide ## debug escalate to. If the symptom does not fit cleanly in env-class or program-class, start at TASKS.md ## debug — the full layered ladder lives there.

Example questions this skill answers well

The CLASSES of debug questions this skill is built to answer, each with one worked example.

  • "My DOCA build / link / runtime / program failed — which layer is it?" — worked example: "ld says undefined reference to doca_flow_init — which layer?" Answered by the canonical layered ladder in TASKS.md ## debug (layer 4 = Link).
  • "How do I turn up verbosity for any DOCA library or tool?" — worked example: "My DOCA Flow program is silent — where do I get more log output?" Answered by the trace-flavor / log-level surface in CAPABILITIES.md ## Observability
  • "How do I capture state for a forum question or bug report?" — worked example: "I'd like to file a forum question with reproducible context — what should I include?" Answered by the capture-a-reproducible-state workflow in TASKS.md ## test.
  • "What does this DOCA tool's output mean / which tool answers this?" — worked example: "doca_caps returned nothing for RDMA — does that mean unsupported?" Answered by the tool-vs-capability decision tree in CAPABILITIES.md ## Capabilities and modes
  • "I'm debugging inside the NGC container — what's observable?" — worked example: "hugepages is empty inside the container; is that a real problem or a container thing?" Answered by the container-specific debug constraints in CAPABILITIES.md ## Safety policy
  • "Where do I ask for help with this?" — worked example: "Where is the customer-facing DOCA forum and what should the post contain?" Answered by the Developer-Forum routing rule in TASKS.md ## debug plus doca-public-knowledge-map.

If the symptom is purely env-class, route to doca-setup ## debug; if purely program-class, route to doca-programming-guide ## debug. This skill is the cross-cutting layer both call into.

When to load this skill

Load this skill when the user is debugging anything DOCA-related — a build that won't compile, a link step that can't resolve a doca_* symbol, a runtime call that returns DOCA_ERROR_*, a packet that does not appear on the wire, a service that won't start, or a tool that returns no useful output. Concretely:

  • The user reports a symptom and needs to find the layer that caused it (install / version / build / link / runtime / program).
  • The user asks "how do I get more logs?" or "how do I turn up the verbosity?" for any DOCA library or tool.
  • The user wants to capture state for a forum question or an internal bug report (the bundle does not own the internal-bug-report channel; it routes to the public DOCA Developer Forum).
  • The user is reading a stack trace, a valgrind output, or a core dump from a DOCA program and wants to know where to look first.
  • The user is debugging inside the NGC DOCA container and needs to know what is and is not observable from inside it.

Do not load this skill for:

  • "What is DOCA_ERROR_BAD_STATE?", "what error codes does DOCA return?" — that is the cross-library error taxonomy, owned by doca-programming-guide CAPABILITIES.md ## Error taxonomy. This skill consumes that taxonomy; it does not redefine it.
  • "My pkg-config cannot find doca-flow", "hugepages are not mounted", "my representor isn't visible" — those are env-class symptoms, owned by doca-setup ## debug. This skill is the canonical pointer that env-class debug ladder redirects to once the symptom escalates beyond install / version / build prerequisites.
  • Library-specific debugging (Flow pipe trace, RDMA queue-pair state, Comch channel statistics) — those live in the matching library skill (e.g. doca-flow ## debug). This skill provides the cross-cutting debug ladder; library skills layer their library-specific debug surface on top of it.

What this skill provides

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

  • CAPABILITIES.md — what kinds of debug surface DOCA exposes: the layered debug model (install / version / build / link / runtime / program), the read-only-first stance, version-availability of debug tools (e.g. doca_caps since DOCA 2.6.0), the cross-library error taxonomy (cross-link only — owned by doca-programming-guide), the observability primitives DOCA emits (stderr logs, --sdk-log-level, the doca-<lib>-trace build flavor, library counters), and the safety constraints on debug actions (read-only first, don't mutate install tree mid-investigation).
  • TASKS.md — the actual debug workflows: ## configure (set up env for high-verbosity debug), ## test (capture a reproducible state), ## debug (the canonical layered ladder, the universal entry point that every library ## debug redirects to), and the Where to ask for help routing (NVIDIA DOCA Developer Forum). Three other anchors (build, modify, run) exist for lint compliance and route to doca-programming-guide, which owns those verbs after the env / program split.

Loading order

  1. Read this SKILL.md first to confirm the user's symptom is cross-cutting debug (not env-class only, not program-class only, not library-internal only).
  2. For the layered debug model, the read-only stance, the version-availability of debug tools, and the observability surface DOCA emits, see CAPABILITIES.md.
  3. For the canonical layered debug ladder and the capture-and-report workflow, see TASKS.md. The build, modify, and run anchors in TASKS.md are stubs that route to doca-programming-guide; their substance lives there.
  4. If the user's symptom turns out to be env-class (install / build prerequisites), hand off to doca-setup ## debug. If program-class, hand off to doca-programming-guide ## debug. If library-internal, hand off to the matching library skill's ## debug (e.g. doca-flow ## debug).

The two companion files cross-link to each other and to doca-public-knowledge-map whenever the right answer is "look it up in the public docs or the installed package layout" rather than "debug-specific guidance".

Related skills

  • doca-public-knowledge-map — public DOCA documentation routing and the on-disk layout of an installed DOCA package. This skill defers all "where is X documented", "where on disk is Y", and "how do I check the installed version" questions to the knowledge-map.
  • doca-setup — env-class debug (install / build prerequisites): pkg-config failures, missing hugepages, representors not visible, header-vs-runtime version mismatches. doca-setup ## debug is the env-class layered ladder; this skill is the cross-cutting debug ladder both env and program ladders escalate to.
  • doca-programming-guide — program-class debug (lifecycle order, DOCA_ERROR_* interpretation, doca_error_get_descr() use, the validate-before-commit rule). doca-programming-guide ## debug is the program-class layered ladder; this skill picks up where it leaves off when the symptom involves cross-library tooling (gdb, valgrind, container introspection, core dumps).
  • Library skills (e.g. doca-flow, doca-dms, doca-caps) — library-specific debug overlays. Each library's ## debug builds on the cross-cutting ladder defined here, then adds its own counters, traces, and inspector tools.

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 Debug AI skill do?

Use this skill when the user is debugging any DOCA symptom — a build that won't compile, a link step that can't resolve a doca_* symbol, a runtime call returning DOCA_ERROR_*, a silent service or tool, or a stack trace / valgrind / core dump — and needs the layered ladder (install → version → build → link → runtime → program → driver), verbosity controls (--sdk-log-level, DOCA_LOG_LEVEL, the doca-{lib}-trace flavor), container-debug constraints, or how to capture state for a Developer Forum post. Trigger even when the user does not say "DOCA debug" — implicit phrasings include "undefined re...

Why use Doca Debug on TypingMind?

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

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

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 Debug?

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

Is the Doca Debug 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇