Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
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.
CAPABILITIES.md ## Capabilities and modes
--list-devs invocation in
TASKS.md ## run.CAPABILITIES.md ## Capabilities and modes
TASKS.md ## run.CAPABILITIES.md ## Capabilities and modes
--pci-addr-scoped invocation in
TASKS.md ## run.CAPABILITIES.md ## Capabilities and modes
--list-rep-devs invocation in
TASKS.md ## run.TASKS.md ## test and consumed by
doca-debug ## test step 3
(read-only triple) and
doca-programming-guide ## debug.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
TASKS.md ## debug +
CAPABILITIES.md ## Error taxonomy.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:
doca-setup ## no-install)
and wants to confirm the install can see hardware before writing code.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.
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:
doca_caps --list-devs to enumerate DOCA devices.doca_caps --list-rep-devs to enumerate representor
devices.--pci-addr.## 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.
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.
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:
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".samples/ or reference/ subtree. This is a thin loader for
a documented CLI; substantive material lives on the public page and
in --help.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).doca_caps reports, version availability, error
surface, and safety posture, see CAPABILITIES.md.configure, build, modify, run, test, debug —
see TASKS.md.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.libs/<library> skill — for fine-grained,
library-specific capability questions that go beyond what
doca_caps exposes.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
日本語の概要は準備中です。原文の説明を表示しています。
Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.
日本語の概要は準備中です。原文の説明を表示しています。
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
日本語の概要は準備中です。原文の説明を表示しています。
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
日本語の概要は準備中です。原文の説明を表示しています。
Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.
日本語の概要は準備中です。原文の説明を表示しています。
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.
日本語の概要は準備中です。原文の説明を表示しています。