CUDA-Q is a programming platform for hybrid quantum-classical systems that combines quantum processing units with GPUs and CPUs. Developers and researchers use its C++ and Python tools, compiler, runtime, and hardware backends to build and test quantum applications. The catalogue skills support development with CUDA-Q.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/cuda-quantum --skill cudaq-guidegit clone --depth 1 https://github.com/NVIDIA/cuda-quantumWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/cuda-quantum/cudaq-guide)<a href="https://agentmods.dev/skills/nvidia/cuda-quantum/cudaq-guide"><img src="https://agentmods.dev/badge/skills/nvidia/cuda-quantum/cudaq-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/cuda-quantum/cudaq-guide"><img src="https://agentmods.dev/badge/skills/nvidia/cuda-quantum/cudaq-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01209 |
| Opus 5.5 | $0.00010 | $0.00484 |
| Sonnet 5.5 | $0.00005 | $0.00242 |
| Haiku 4.5 | $0.00003 | $0.00121 |
Grade A, and why
cudaq-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed f38a46aae3e6
- 1mo ago Changed 84632924a3be
- 1mo ago First seen · 117 lines · 26 tokens per session scan A 0ae9429f906b
cudaq-guide is a skill published in the GitHub repository NVIDIA/cuda-quantum (1,163 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 1,209 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
cudaq-guide
Use for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.
cudaq-importing
Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
cudaq-guide
CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.
quantum-algorithms-expert
Develop production-ready quantum algorithms for optimization, simulation, and machine learning on near-term quantum devices. Use when the user mentions quantum computing, Qiskit or Cirq, quantum circuits, QAOA, VQE, Grover or Shor, or quantum machine learning on NISQ devices.
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
cuopt-developer
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.