Doca Pcc Counters logo

Doca Pcc Counters

OrganizationPopular
NVIDIA
doca-pcc-counters

Use this skill when the user is invoking the DOCA PCC Counters tool — the `pcc_counters.sh` bash script under the DOCA tools directory — to arm and read the fixed firmware/hardware PCC (Programmable Congestion Control) diagnostic counters (CNP, RTT, WRED-drop, etc.) on a ConnectX / BlueField device via mst + the mlx5 debugfs `diag_cnt` interface. The script takes two positional args — `set | query` and an mst device path — with no `--help` or subcommands. Trigger even without "pcc_counters.sh" or "PCC counters": "how do I read the CNP / RTT / WRED-drop counters", "PCC counter stuck at zero", "the script says Bad Device", or "is congestion control dropping packets on this port?". Route elsewhere for writing a custom PCC algorithm (doca-pcc), factory firmware PCC config, DOCA install, or fleet-wide CC tuning.

Overview

PublisherNVIDIA
Repositoryskills
Skill namedoca-pcc-counters
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 Pcc Counters 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-pcc-counters
  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-pcc-counters .claude/skills/doca-pcc-counters
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Doca Pcc Counters 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 Pcc Counters 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 Pcc Counters 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 PCC Counters (pcc_counters.sh)

Where to start: This is a tool skill for invoking pcc_counters.sh — a small bash script that arms and reads the device's fixed set of firmware / hardware PCC diagnostic counters (CNP count, RTT-perf, WRED-drop, RTT-gen, handled events) through the mlx5 debugfs diag_cnt interface. Open TASKS.md and start at ## run for the canonical set-then-query sequence, or ## debug when the user reports "ERROR: Bad Device", "counter stuck at zero", or "the dump is empty". Open CAPABILITIES.md when the question is which counters the script reports and how it reaches them. If the user has not installed DOCA / MFT yet, route to doca-setup first.

This skill is the firmware / HW PCC counter readout surface. It is NOT the host-side control library that loads custom congestion-control kernels onto the DPA (that is doca-pcc) and it is NOT the firmware PCC algorithm configuration (that path is firmware configuration, routed via doca-public-knowledge-map). The counters this script reads are device / firmware diagnostic counters that exist regardless of whether a custom doca-pcc DPA kernel is running — do not condition them on a custom kernel being loaded.

Example questions this skill answers well

The CLASSES of pcc_counters.sh 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.

  • "How do I read the PCC diagnostic counters on this device?" — worked example: "arm and dump the CNP / RTT / WRED-drop counters for /dev/mst/mt41692_pciconf0". Answered by the fixed counter set in CAPABILITIES.md ## Capabilities and modes
  • "What is the smallest legal invocation?" — worked example: "what exactly do I type?". Answered by the two-positional-argument contract (set | query + an mst device path) in TASKS.md ## run.
  • "The script printed ERROR: Bad Device — what's wrong?" — worked example: "my device path is not matching". Answered by the device-resolution layer in CAPABILITIES.md ## Error taxonomy
  • "A counter is stuck at zero — is the device idle, the counters not armed, or genuinely no events?" — worked example: "PCC_CNP_COUNT reads 0 after query". Answered by the arm-before-read rule and the layered diagnosis in TASKS.md ## debug + CAPABILITIES.md ## Error taxonomy.
  • "Is this script on my install, and where?" — worked example: "is pcc_counters.sh present and where does the install put it". Answered by the install overlay in CAPABILITIES.md ## Version compatibility, which redirects to the canonical doca-version rules.

Audience

This skill serves operators, developers, and AI agents who need to read a ConnectX / BlueField device's firmware PCC diagnostic counters to reason about congestion-control behaviour (CNP generation, RTT requests/responses, WRED drops) on a port. Concretely:

  • A network operator confirming whether congestion-control events (CNPs, RTT, WRED drops) are occurring on a device.
  • A developer correlating a custom doca-pcc algorithm's effect with the device-level PCC diagnostic counters (the script reads the firmware counters; the custom algorithm itself is a separate surface owned by doca-pcc).
  • An AI agent producing a PCC counter snapshot as evidence for a congestion-control investigation.

It is not for users debugging the script's bash itself, not the place to learn how to write a custom PCC algorithm — that audience belongs in doca-pcc — and not the place for users who want to configure the factory firmware PCC algorithm (route via doca-public-knowledge-map).

pcc_counters.sh is shipped as a plain bash script installed under the DOCA tools directory (per install_data in tools/pcc_counters/meson.build), not a compiled binary and not a library you link against. The skill uses the bundle's kind: tool three-file shape (SKILL.md + CAPABILITIES.md

  • TASKS.md) so the agent's task-verb contract (configure / build / modify / run / test / debug) is uniform across the bundle.

When to load this skill

Load this skill when the user is — or the agent needs to — arm and read the device PCC diagnostic counters on a host or BlueField Arm with the mst tools available and debugfs mounted. Concretely:

  • Arming the diagnostic counters with set on a target mst device.
  • Reading the armed counters with query and quoting the named counter lines verbatim.
  • Capturing a counter readout as evidence for a congestion-control investigation.

Do not load this skill for general DOCA orientation, custom-PCC algorithm design, the host-side doca-pcc library API, the factory firmware PCC algorithm, or DOCA / MFT install. For those, route to doca-public-knowledge-map, doca-pcc, or doca-setup.

What this skill provides

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

  • CAPABILITIES.md — what pcc_counters.sh does: the exact two-operation surface (set arms the device's diagnostic counters by writing counter IDs + params to debugfs; query reads the diag_cnt/dump and prints the named counters), the FIXED firmware / HW counter set it knows (PCC_CNP_COUNT, the MAD_RTT_PERF_CONT_*, the *_EVENT_WRED_DROP family, HANDLED_*_EVENTS, the DROP_RTT_PORT*/RTT_GEN_PORT* families), how it resolves an mst device to a PCI address (mst status -v + lspci) and reaches /sys/kernel/debug/mlx5/<pci>/diag_cnt/, the install-availability overlay that redirects to doca-version, the layered error taxonomy (script-not-present / bad-device / not-armed-before-query / debugfs-or-permission / counter-stuck-at-zero / cross-cutting), and the safety policy that flags set as a privileged debugfs write and any CC tuning decision derived from a reading as high-stakes.
  • TASKS.md — step-by-step workflows for the in-scope task verbs: configure (route to install + confirm mst / debugfs / sudo), build (route to install; nothing to compile — it is a script), modify (refuse — do not patch the shipped script), run (the set-then-query sequence), test (confirm the dump contains the named counters with finite values), debug (the layered diagnosis ladder), plus a Deferred task verbs block and a Command appendix.

The skill assumes a host or BlueField where DOCA / MFT is already installed (mst tools present, debugfs mounted, sudo available) and the target device is visible to mst status -v.

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:

  • Invented flags or subcommands. pcc_counters.sh has exactly two operations (set, query), takes exactly two positional arguments, and has NO --help, --version, list, snapshot, watch, or diff. Do not invent any.
  • Pre-baked example counter values. Counter values are device-, firmware-, and traffic-state-specific; a captured value will mislead an operator on a different device.
  • Wrappers, parsers, or rewritten copies of the script. The script is the contract; modifying or re-implementing it is out of scope.
  • A specific congestion-control tuning recommendation derived from a counter reading. That is a high-stakes domain question — the skill prescribes how to capture the counters; it refuses to translate a delta into a CC parameter change without the user's own domain analysis.
  • A samples/ or reference/ subtree. This is a thin loader for a documented script; substantive material lives in the script and the public PCC documentation.

Loading order

  1. Read this SKILL.md first to confirm the user's question is in scope (reading the device's firmware PCC diagnostic counters; not designing or loading a custom algorithm).
  2. For what the script does, the fixed counter set, the debugfs mechanism, the install-availability overlay, the layered error surface, and the safety posture, see CAPABILITIES.md.
  3. For the documented invocations — configure, build, modify, run, test, debug, plus the Command appendix — see TASKS.md.

Related skills

  • doca-pcc — the host-side library for writing and loading custom congestion-control kernels onto the DPA. It is a SEPARATE surface: the firmware PCC diagnostic counters pcc_counters.sh reads exist independently of any custom doca-pcc kernel, but an operator tuning a custom algorithm may read these counters to observe device-level CC behaviour. Conflating the script (firmware counter readout) with the library (custom algorithm load/control) is the most common PCC first-touch error.
  • doca-public-knowledge-map — routing to the public DOCA / PCC documentation set, including the firmware PCC algorithm configuration.
  • doca-version — canonical DOCA version-handling rules. The ## Version compatibility section in CAPABILITIES.md is a concise overlay that redirects here.
  • doca-setup — env preparation, install verification, mst tools, debugfs, and the I have no install yet path with the public NGC DOCA container. This skill assumes its preconditions are satisfied.
  • doca-debug — the cross-cutting debug ladder. The PCC counter readout slots in as a read-only device-state evidence source before any congestion-control tuning recommendation is made.
  • doca-programming-guide — general DOCA programming patterns shared across the bundle.

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 Pcc Counters AI skill do?

Use this skill when the user is invoking the DOCA PCC Counters tool — the `pcc_counters.sh` bash script under the DOCA tools directory — to arm and read the fixed firmware/hardware PCC (Programmable Congestion Control) diagnostic counters (CNP, RTT, WRED-drop, etc.) on a ConnectX / BlueField device via mst + the mlx5 debugfs `diag_cnt` interface. The script takes two positional args — `set | query` and an mst device path — with no `--help` or subcommands. Trigger even without "pcc_counters.sh" or "PCC counters": "how do I read the CNP / RTT / WRED-drop counters", "PCC counter stuck at zero"...

Why use Doca Pcc Counters on TypingMind?

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

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

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 Pcc Counters?

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

Is the Doca Pcc Counters 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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