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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when the user is writing their first DOCA app or asking a library-agnostic programming question — picking a shipped sample to copy and modify, wiring pkg-config + meson (or FFI from Rust / Go / Python against the public C ABI), walking the Core lifecycle (create → configure → start → connect → use → stop → destroy), or decoding a DOCA_ERROR_*. Trigger on implicit phrasing: "write my first DOCA program", "meson line for doca_rdma_*", "DOCA_ERROR_BAD_STATE on first call", "call DOCA from Rust", "built clean but nothing on the wire". Refuse install / hugepages / pkg-config (doca-setup), docs or version lookup (doca-public-knowledge-map), and library-internal API work (matching library skill).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Follow Loading order below. Open the tasks companion for how-to and the capabilities companion for coverage. Do not open every companion file.
Where to start: Read ## Audience to confirm the user
is consuming DOCA, not contributing to it. Then jump to the H2
that matches the verb (## modify for first-app derivation,
## build for the canonical build pattern, ## test for the test
loop, ## debug for the program-class debug ladder). Docker / NGC
is the build half, not the runtime half. The NGC container can
compile and link. Flow pipe creation, RDMA QP ops, and any call
that needs a doca_dev on a port need a real BlueField or
ConnectX. Do not promise the app will work entirely inside docker.
These are the CLASSES of program-class questions the skill is built to answer, each with one worked example. Library-specific overlays (Flow / DMS / Caps / …) live in the matching library skill; this skill answers the library-agnostic shape.
doca_rdma_*?"
Answered by the pkg-config doca-<library> pattern in
doca_flow_pipe_* calls in my
program?" Answered by the Core order create → configure → start →
connect → use → stop → destroy in ## Capabilities and modes
(cfg-create / init implement configure and finish before start).DOCA_ERROR_* came back — what does it mean and what do I do?"
— worked example: "My code got DOCA_ERROR_BAD_STATE." Answered
by the cross-library doca_error_get_descr() rule in
If the question is env-class (install / build env / hugepages /
devices), route to doca-setup. If it is
library-specific (Flow pipe topology, RDMA QP setup, DMS service
deploy), layer the matching library skill on top.
This skill serves external developers building applications that
consume DOCA libraries — i.e., users whose code calls one or more
doca_<library>_* symbols (directly in C/C++, or through FFI /
bindings from another language). It is programming with DOCA,
not programming of DOCA: it is not for NVIDIA developers
contributing to DOCA itself, and it does not assume access to the
DOCA source tree, internal NVIDIA tooling, or any non-public
information. The only inputs it ever points the agent at are the
ones any external user has: the public docs at
docs.nvidia.com/doca/sdk/,
the public catalog at
catalog.ngc.nvidia.com, the
public GitHub repos under
github.com/NVIDIA /
github.com/NVIDIA-DOCA, the
public developer forum, and the on-disk /opt/mellanox/doca tree
that the public DOCA install (or the public NGC DOCA container,
nvcr.io/nvidia/doca/doca) puts on the user's host. Where to find
and how to install questions are routed elsewhere — see Related
skills below.
Language scope. DOCA itself is a C library family; every shipped
sample in /opt/mellanox/doca/samples/ and every shipped reference
application in /opt/mellanox/doca/applications/ is C. C and C++
consumers are the canonical case for every prescriptive workflow in
this skill. Other-language consumers (Rust, Go, Python, …) consume the
same *.so libraries through FFI or language-specific bindings against
the public C ABI; the skill keeps the lifecycle, capability, error,
observability, and safety guidance language-neutral, and routes the
language-specific build / FFI work back to the consumer's own toolchain
without authoring wrappers.
Load this skill when the user has DOCA installed and the env-class
preconditions are already satisfied (i.e.,
doca-setup has produced a clean install
where pkg-config doca-<library> resolves, hugepages are mounted, and
devices are visible), and is now asking a question about how to
actually program against DOCA in a library-agnostic way:
pkg-config + meson build pattern any DOCA application
follows, regardless of which library it consumes.doca-setup because it
is a programming verb, not an env verb.create → configure (including permissions and task set_conf) → start → connect → use → stop → destroy) and how it manifests across libraries. Configuration MUST
finish before doca_ctx_start(). Connect is its own step after
start; do not write connect/use as one token.DOCA_ERROR_* pattern, doca_error_t, and
doca_error_get_descr() — the cross-library shape, not Flow- or
RDMA-specific overlays.Do not load this skill for:
pkg-config not find doca-flow?
How do I mount hugepages? I don't have DOCA installed yet — can I
use a container?" — env-class questions belong in
doca-setup, which owns install
verification, env preparation, env-class debugging, and the
no-install → NGC container fallback path for any user on macOS,
Windows, or Linux without DOCA.doca-public-knowledge-map.doca-flow).This is a thin loader. The body keeps only the orientation needed to pick the right next file. The substantive material lives in two companion files:
DOCA_ERROR_* taxonomy,
the program-side observability surface (DOCA logging, capability
snapshots), and the program-side safety policy that every library
skill inherits.configure, build, modify, run, test, debug. The
## modify verb owns the universal derive a custom first app from
a sample pattern that every DOCA library skill extends with
library-specific overrides; the ## build verb owns the canonical
pkg-config doca-<library> build pattern in two language tracks
(C/C++ direct, non-C via FFI).This skill assumes doca-setup has already
produced a clean install. It does not cover env preparation; that
is doca-setup's job, including the no-install → NGC container
(nvcr.io/nvidia/doca/doca) fallback for users on macOS, Windows, or
Linux without DOCA.
This skill is agent guidance, not a samples or templates bundle. It deliberately does not contain — and pull requests should not add:
meson.build, CMakeLists.txt,
Cargo.toml, setup.py, go.mod, …) 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-<library> is the source of truth.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.configure, build,
modify, run, test, debug — see the tasks companion.If the user is asking for a first app in a specific library, walk
through ## modify for the universal
copy-and-edit pattern, then hand off to the library skill (e.g.
doca-flow) for the library-specific values
to swap.
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 preparation, install
verification, env-class debugging, and the I have no install yet
procedure with the NGC container (nvcr.io/nvidia/doca/doca) as
the universal Stage-1 fallback for any user on macOS, Windows, or
Linux without DOCA. This skill assumes doca-setup's preconditions
are already satisfied.doca-flow — DOCA Flow on BlueField.
Extends this skill's ## modify (universal first-app derivation)
with the Flow-specific list of fields to swap.doca-container-deployment
— shipping an application that already builds: container manifests,
rollout, and service-readiness verification. This skill ends once the
program compiles and runs locally; it does not cover deploying it.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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`.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.