Official agent skill

Cudaq Guide

A skill your agent uses for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Cudaq Guide

skills CLI

$ npx skills add NVIDIA/skills --skill cudaq-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cudaq-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cudaq-guide .claude/skills/cudaq-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "cudaq-guide" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide into .claude/skills/cudaq-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cudaq-guide", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill cudaq-guide -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cudaq-guide --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cudaq-guide .agents/skills/cudaq-guide && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "cudaq-guide" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide into .agents/skills/cudaq-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cudaq-guide", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill cudaq-guide -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cudaq-guide --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cudaq-guide .cursor/skills/cudaq-guide && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "cudaq-guide" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide into .cursor/skills/cudaq-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cudaq-guide", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/cudaq-guide

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill cudaq-guide -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cudaq-guide --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cudaq-guide .gemini/skills/cudaq-guide && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "cudaq-guide" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide into .gemini/skills/cudaq-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cudaq-guide", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills cudaq-guide

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill cudaq-guide -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cudaq-guide .github/skills/cudaq-guide && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "cudaq-guide" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide into .github/skills/cudaq-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cudaq-guide", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill cudaq-guide -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills cudaq-guide --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cudaq-guide .opencode/skills/cudaq-guide && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "cudaq-guide" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cudaq-guide into .opencode/skills/cudaq-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cudaq-guide", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: cudaq-guide
GitHub stars: 3.5k
Token cost: ~1.3k tokens
SKILL.md length: 408 words
Files: 9 (incl. references)
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

A skill your agent uses for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.

Simulation targets
SKILL.md covers Purpose, Prerequisites, Instructions and Routing by Argument, plus 4 more sections
Calls pip; reaches nvidia.github.io
@cudaq.kernel authoring guidance

What it does

Cudaq Guide is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `BENCHMARK.md`, `evals/EVAL.md` and `evals/config.yml`). Compatibility notes: Python 3.10+, C++ 20

It sits in AI & LLM Engineering. It works with CUDA, NVIDIA AI Platform, Python and C++. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Simulation targets
@cudaq.kernel authoring guidance

Example prompts

“/cudaq-guide”

Requirements

Python 3
Compatibility (from SKILL.md): Python 3.10+, C++ 20

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- nvidia.github.io
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Compatibility
Python 3.10+, C++ 20
From compatibility in the SKILL.md frontmatter.

Context cost

Cudaq Guide loads about 1.3k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 26 tokens; SKILL.md has 408 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~26
When it runs · the whole SKILL.md, loaded when a task matches: ~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~8.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 408 words, ~1,258 tokens.

Download SKILL.mdSave it as .claude/skills/cudaq-guide/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.

name: cudaq-guide
description: Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.
compatibility: Python 3.10+, C++ 20
title: CUDA-Q Guide
version: 1.1.2
author: CUDA-Q Team <cuda-quantum@nvidia.com>
tags: cuda-quantum, quantum-computing, onboarding, getting-started, authoring, kernels, nvidia
tools: Read, Glob, Grep
license: Apache-2.0
metadata.author: CUDA-Q Team <cuda-quantum@nvidia.com>
metadata.tags: cuda-quantum, quantum-computing, onboarding, getting-started, nvidia
metadata.languages: python, c++
metadata.domain: quantum

CUDA-Q Guide

Purpose

Guide users through CUDA-Q installation, basic kernels, GPU simulation targets, QPU access, built-in applications, multi-GPU execution, and Python @cudaq.kernel authoring. For Qiskit-to-CUDA-Q ports, route to the cudaq-importing skill instead.

Prerequisites

Python 3.10+ for Python CUDA-Q workflows.
CUDA Toolkit and an NVIDIA GPU for GPU-accelerated targets on Linux.
CPU-only simulation is available through qpp-cpu; macOS is CPU-only.
C++ workflows require Linux or WSL and C++20.
QPU workflows require provider-specific credentials and accounts.

Instructions

Invoke with /cudaq-guide [argument].
If no argument is given, display the onboarding menu and ask which topic the user wants.
Use the routing table below to choose the relevant reference file.
Read local CUDA-Q documentation files when the answer depends on a specific CUDA-Q version or backend behavior.
Do not answer Qiskit porting questions from this skill; use cudaq-importing.

Routing by Argument

Argument	Action	Reference
`install`	Walk through Python or C++ installation and validation.	references/onboarding.md
`test-program`	Build and run a Bell-state kernel.	references/onboarding.md
`gpu-sim`	Select GPU, multi-GPU, tensor-network, or CPU targets.	references/onboarding.md
`qpu`	Guide provider selection and credential-safe QPU setup.	references/onboarding.md
`applications`	Summarize CUDA-Q application areas and notebooks.	references/onboarding.md
`parallelize`	Choose `mgpu`, `mqpu`, async dispatch, or distributed observe.	references/onboarding.md
`author`	Author CUDA-Q Python kernels, select execution APIs, and debug compiler issues.	references/authoring.md
(none)	Print the menu below and ask which topic to explore.	This file

text

CUDA-Q Getting Started

CUDA-Q is NVIDIA's unified quantum-classical programming model for CPUs, GPUs, and QPUs.
Supports Python and C++. Docs: https://nvidia.github.io/cuda-quantum/latest/

Choose a topic:
  /cudaq-guide install         Install CUDA-Q
  /cudaq-guide test-program    Write and run a Bell-state kernel
  /cudaq-guide gpu-sim         Accelerate simulation on NVIDIA GPUs
  /cudaq-guide qpu             Connect to real QPU hardware
  /cudaq-guide applications    Explore what you can build
  /cudaq-guide parallelize     Run across GPUs or QPUs
  /cudaq-guide author          Author @cudaq.kernel Python code

Reference Files

references/onboarding.md: installation, test program, GPU targets, QPU providers, application areas, parallelization modes, examples, and platform troubleshooting.
references/authoring.md: execution APIs, kernel-language constraints, silent-failure pitfalls, recurring coding patterns, resource metrics, debugging, and validation.

Show full SKILL.md (154 more words)Show less

Limitations

Guidance targets CUDA-Q Python/C++ workflows, with authoring details focused on decorator-mode Python APIs used in CUDA-Q 0.14 and 0.15.
GPU and multi-GPU support depends on local CUDA-Q, CUDA Toolkit, driver, MPI, and hardware availability.
QPU access and target options are provider-specific and may change; verify against local docs before giving operational steps.

Troubleshooting

Import error after pip install cudaq: check Python 3.10+ and supported OS.
No GPU detected: verify CUDA Toolkit and nvidia-smi; fall back to qpp-cpu.
Kernel compile error: read references/authoring.md and check the restricted kernel-language subset.
Version-specific behavior differs: compare cudaq.__version__ with the latest documentation, then review relevant documentation or source changes when debugging an installed version that is not the latest release.
QPU submission fails: verify provider credentials are set as environment variables or through a secrets manager, never hardcoded.
Documentation lookup fails: retry transient MCP or repository lookup once, then fall back to local docs or official CUDA-Q documentation.

Files

SKILL.md and 8 other files (references) in skills/cudaq-guide of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/EVAL.md
evals/config.yml
evals/evals.json
references/authoring.md
references/onboarding.md
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 0e0d506

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Works with

Questions about Cudaq Guide

What does Cudaq Guide do?

A skill your agent uses for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance. Cudaq Guide is an agent skill from NVIDIA/skills, published by the product's own GitHub organization.kernel authoring guidance.

When should I use Cudaq Guide?

Cudaq Guide fits situations like: simulation targets; @cudaq.kernel authoring guidance.

How do I install Cudaq Guide in Claude Code?

Run `npx skills add NVIDIA/skills --skill cudaq-guide -a claude-code`. Or copy the skill folder (skills/cudaq-guide in NVIDIA/skills) into .claude/skills/cudaq-guide in your project. Claude Code loads it when a task matches its description.

How do I install Cudaq Guide in Codex?

Run `npx skills add NVIDIA/skills --skill cudaq-guide -a codex`. Or copy the skill folder (skills/cudaq-guide in NVIDIA/skills) into .agents/skills/cudaq-guide in your project. Codex loads it when a task matches its description.

Can I use Cudaq Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill cudaq-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cudaq-guide, .gemini/skills/cudaq-guide, .github/skills/cudaq-guide and .opencode/skills/cudaq-guide in your project.

What does Cudaq Guide need to run?

Going by SKILL.md and its folder, Cudaq Guide needs the command-line tools its instructions call (pip). Our summary lists: Python 3. Compatibility (from SKILL.md): Python 3.10+, C++ 20.

Does Cudaq Guide access the network?

SKILL.md names 1 domain. In commands or code: nvidia.github.io; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Cudaq Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cudaq Guide use?

Cudaq Guide is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cudaq Guide use?

About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7k tokens, read only when the agent opens those files.

What are the alternatives to Cudaq Guide?

Skills that share tags, products or a category with Cudaq Guide: Cutlass Skill (slowlyC/agent-gpu-skills, 169 stars), Make Op Verify (CVCUDA/CV-CUDA, 2.7k stars), Review Op Support (CVCUDA/CV-CUDA, 2.7k stars) and Review Op Test Coverage (CVCUDA/CV-CUDA, 2.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cudaq Guide?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Cudaq Guide

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Compatibility

Context cost

Safety

SKILL.md

CUDA-Q Guide

Purpose

Prerequisites

Instructions

Routing by Argument

Menu

Reference Files

Limitations

Troubleshooting

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Cudaq Guide