nvidia-cuda
NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Green Contexts, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink, Locality Domains, Compute Fabric Transport。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Task Scheduling, Operator API, nvMatmulHeuristics, nsight-compute, compute-sanitizer。
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/fandhe-ai/agent-reference-skills --skill nvidia-cuda- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads nvidia-cuda automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
nvidia-cuda
NVIDIA CUDA — GPU 上で汎用並列計算を行うためのプログラミングモデル・ツールチェーン・ライブラリ群。 CUDA C++/Python によるカーネル記述、PTX ISA 命令セット、Blackwell アーキテクチャ向けチューニング、 CUTLASS/CuTe による高性能 GEMM 実装をカバーする。
ディレクトリ構成
skills/nvidia-cuda/
SKILL.md
references/
programming-model/
README.md
introduction.md
programming-model.md
cuda-platform.md
intro-to-cuda-cpp.md
intro-to-cuda-python.md
writing-cuda-kernels.md
writing-tile-kernels.md
asynchronous-execution.md
understanding-memory.md
nvcc.md
advanced/
README.md
core/
README.md
advanced-host-programming.md
advanced-kernel-programming.md
driver-api.md
multi-gpu-systems.md
feature-survey.md
features/
README.md
unified-memory.md
cuda-graphs.md
stream-ordered-memory-allocation.md
cooperative-groups.md
programmatic-dependent-launch.md
green-contexts.md
locality-domains.md
compute-fabric-transport.md
lazy-loading.md
error-log-management.md
async-barriers.md
pipelines.md
async-copies.md
cluster-launch-control.md
l2-cache-control.md
memory-sync-domains.md
inter-process-communication.md
virtual-memory-management.md
extended-gpu-memory.md
dynamic-parallelism.md
graphics-interop.md
driver-entry-point-access.md
appendices/
README.md
compute-capabilities.md
environment-variables.md
cpp-language-support.md
cpp-language-extensions.md
mathematical-functions.md
device-callable-apis.md
cuda-cpp-memory-model.md
cuda-cpp-execution-model.md
ptx-isa/
README.md
introduction.md
programming-model.md
machine-model.md
syntax.md
state-spaces-types-variables.md
instruction-operands.md
abi.md
memory-consistency-model.md
instruction-set-overview.md
instructions-arithmetic.md
instructions-comparison-logic.md
instructions-data-movement.md
instructions-texture-surface.md
instructions-control-flow.md
instructions-sync-communication.md
instructions-matrix-multiply.md
instructions-video-misc.md
special-registers.md
directives.md
pragma-strings.md
release-notes.md
blackwell-tuning/
README.md
architecture.md
best-practices.md
streaming-multiprocessor.md
memory-system.md
nvlink.md
cutlass/
README.md
cute-dsl/
README.md
overview.md
functionality.md
quick-start.md
cute-dsl.md
dsl-introduction.md
dsl-code-generation.md
dsl-control-flow.md
dsl-jit-arg-generation.md
dsl-dynamic-layout.md
dsl-struct-types.md
dsl-jit-caching.md
dsl-jit-compilation-options.md
dsl-types.md
framework-integration.md
debugging.md
iket-profiling.md
primitives-api.md
task-scheduling.md
ts-introduction.md
ts-resources.md
ts-pipelines.md
ts-schedules.md
ts-patterns.md
ts-validation.md
ts-printout.md
ts-allocators.md
ts-pipeline-groups.md
ts-pdl.md
ts-api-task-scheduling.md
ts-api-resources.md
ts-api-task.md
ts-api-task-manager.md
ts-api-schedule-builder.md
ts-api-memory.md
ts-api-pipeline-group.md
ts-api-pipeline.md
ts-api-enums.md
autotuning-gemm.md
compile-with-tvm-ffi.md
dsl-ahead-of-time-compilation.md
naming-conventions.md
deprecation-policy.md
mma-intro.md
mma-wmma-programming.md
mma-wgmma-programming.md
mma-tcgen05-programming.md
limitations.md
faqs.md
api-overview.md
api-cute.md
api-cute-arch.md
api-cute-runtime.md
api-cute-nvgpu.md
api-cute-nvgpu-common.md
api-cute-nvgpu-warp.md
api-cute-nvgpu-warpgroup.md
api-cute-nvgpu-cpasync.md
api-cute-nvgpu-tcgen05.md
api-pipeline.md
api-utils.md
api-utils-sm90.md
api-utils-sm100.md
operator-api/
README.md
overview.md
tutorials.md
tutorial-000-gemm.md
tutorial-001-gemm-fused-epilogue.md
tutorial-002-bring-your-own-kernel.md
tutorial-003-host-latency-best-practices.md
tutorial-004-fake-tensors.md
tutorial-005-grouped-gemm-contiguous-offset.md
tutorial-006-block-scaled-gemm.md
tutorial-007-heuristics.md
api-reference.md
api-operator.md
api-arguments.md
api-discovery.md
api-heuristics.md
api-metadata.md
api-misc.md
cpp/
README.md
overview.md
getting-started.md
quickstart.md
ide-setup.md
build.md
build-windows-visual-studio.md
build-clang-host-compiler.md
functionality.md
terminology.md
fundamental-types.md
programming-guidelines.md
gemm-heuristics.md
efficient-gemm.md
pipeline.md
profiler.md
gemm-performance-measurement.md
dependent-kernel-launch.md
blackwell.md
blackwell-sm100-gemm.md
blackwell-cluster-launch-control.md
code-organization.md
cute.md
cute-quickstart.md
cute-layout.md
cute-layout-algebra.md
cute-tensor.md
cute-algorithms.md
cute-mma-atom.md
cute-gemm-tutorial.md
cute-predication.md
cute-tma-tensors.md
cutlass-3x.md
cutlass-3x-design.md
cutlass-3x-backwards-compatibility.md
gemm-api-3x.md
cutlass-2x.md
layout-2x.md
gemm-api-2x.md
tile-iterator-concept.md
utilities.md
grouped-scheduler.md
implicit-gemm-convolution.md
samples/
README.md
async-copy-pipeline.md
cooperative-groups-reduction.md
cuda-graph-explicit-nodes.md
cuda-graph-stream-capture.md
cute-dsl-gemm.md
cutlass-cpp-device-gemm.md
cutlass-operator-api-gemm.md
explicit-memory-transfer.md
multi-gpu-peer-to-peer.md
shared-memory-tiled-matmul.md
stream-ordered-allocation.md
stream-overlap.md
tile-kernel-vector-add.md
unified-memory.md
vector-add-kernel.md
scripts/
README.md
install.md
nvcc-build.md
binary-utilities.md
sanitize.md
debug.md
nsight-systems.md
nsight-compute.md
gpu-query.md
cutlass-build.md
探索手順
タスクからカテゴリを引き、カテゴリの README.md で目的のページを特定する:
- 下記マッピング表でタスクに対応するカテゴリを探す
- そのカテゴリの
references/{category}/README.md（advanced/cutlass/はサブディレクトリの README.md）を参照して目的のページを特定する - 該当ページの
.mdを Read して詳細を確認する
GB10 Grace Blackwell 実機のハードウェア仕様・DGX OS・PXE/fleet 運用・playbook 起動手順は dgx-spark スキル、
リモート接続管理・VS Code 起動等のデスクトップアプリ操作は nvidia-sync スキルを参照。
本スキルは CUDA C++/Python の書き方・PTX 命令・CUTLASS API を担当する。
タスク → カテゴリ マッピング
| タスク | カテゴリ | 参照 README |
|---|---|---|
| CUDA とは何か・GPU 階層モデル・CUDA プラットフォームの全体像を知りたい | programming-model | references/programming-model/README.md |
| CUDA C++/Python でカーネルを書く、nvcc でビルドする、非同期実行・ストリームを扱う | programming-model | references/programming-model/README.md |
| advanced カテゴリ全体の構成（core / features / appendices）を確認したい | advanced | references/advanced/README.md |
| Driver API・マルチ GPU・advanced host/kernel programming を調べたい | advanced/core | references/advanced/core/README.md |
| CUDA Graphs / Cooperative Groups / Unified Memory / VMM / IPC / Green Contexts / Locality Domains / Compute Fabric Transport 等の機能 API を調べたい | advanced/features | references/advanced/features/README.md |
| Compute Capability・環境変数・C++ 言語拡張・メモリ/実行モデルの技術付録を調べたい | advanced/appendices | references/advanced/appendices/README.md |
| PTX 命令セット・state space・レジスタ・memory consistency model を調べたい | ptx-isa | references/ptx-isa/README.md |
| Blackwell アーキテクチャ・SM occupancy・NVLink・メモリシステムのチューニングを知りたい | blackwell-tuning | references/blackwell-tuning/README.md |
| CUTLASS/CuTe 全体のカテゴリ構成（CuTe DSL / Operator API / C++）を確認したい | cutlass | references/cutlass/README.md |
| CuTe DSL (Python) で GEMM・MMA・レイアウト・Task Scheduling (TS) を書きたい | cutlass/cute-dsl | references/cutlass/cute-dsl/README.md |
| CUTLASS Operator API（Python、コンパイル済みカーネル呼び出し・nvMatmulHeuristics による operator ランキング）を使いたい | cutlass/operator-api | references/cutlass/operator-api/README.md |
| CUTLASS C++ / CuTe C++ でテンプレートベースの GEMM カーネルを実装したい | cutlass/cpp | references/cutlass/cpp/README.md |
| 典型的な使い方を知りたい（vector add, tiled matmul, GEMM, CUDA Graphs 等） | samples | samples/README.md |
| インストール・nvcc ビルド・nsight プロファイリング・compute-sanitizer コマンドを知りたい | scripts | scripts/README.md |
FAQ
What does the nvidia-cuda skill do?
NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Green Contexts, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink, Locality Domains, Compute Fabric Transport。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Task Scheduling, Operator API, nvMatmulHeuristics, nsight-compute, compute-sanitizer。
How do I install nvidia-cuda?
Run npx skills add https://github.com/fandhe-ai/agent-reference-skills --skill nvidia-cuda in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nvidia-cuda/ (personal) or .claude/skills/nvidia-cuda/ (project).
Which AI agents can use nvidia-cuda?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is nvidia-cuda safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains nvidia-cuda?
It is published by fandhe-ai in fandhe-ai/agent-reference-skills. It has 333 recorded installs.
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