cuda-c-examples-torch

cuda-c-examples-torch is a skill for Claude Code, Codex from wenyi-li/awesome-agent-kernel-skills. It costs 19 tokens per session (4,204 once invoked), scanned A, original, no licence file.

A set of complete examples showing how PyTorch code can work together with CUDA C code for GPU computing.

In plain words
What is it for?
Use it when integrating PyTorch with CUDA C kernels or GPU operations.
Why use it?
It gives you working reference code for connecting a Python machine-learning framework with custom CUDA code.

Skill for Claude CodeCodex

Which agent this was written for is unclear — body not stored (licence); the path alone says nothing.

Good fit Use it when integrating PyTorch with CUDA C kernels or GPU operations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch
Install

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.

Any agent
npx skills add wenyi-li/awesome-agent-kernel-skills --skill cuda-c-examples-torch
Clone the repo
git clone --depth 1 https://github.com/wenyi-li/awesome-agent-kernel-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cuda-c-examples-torch

README.md
[![agentmods](https://agentmods.dev/badge/skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch/github.svg)](https://agentmods.dev/skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch)
Your own site
<a href="https://agentmods.dev/skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch"><img src="https://agentmods.dev/badge/skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch/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.

agentmods 80×15 button for cuda-c-examples-torch

Your own site · 80×15
<a href="https://agentmods.dev/skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch"><img src="https://agentmods.dev/badge/skills/wenyi-li/awesome-agent-kernel-skills/cuda-c-examples-torch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,204 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.04204
Opus 5 $0.00010 $0.02102
Sonnet 5 $0.00004 $0.00841
Haiku 4.5 $0.00002 $0.00420

Measured 7d ago against content hash 1c1a40230cda, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

cuda-c-examples-torch 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 7d 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.

kernel-generator/references/dsl-guides/cuda-c/guides/cuda-c-examples-torch/SKILL.md · 579 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 7d ago First seen · 579 lines · 19 tokens per session scan A 1c1a40230cda

Subscribe to this mod's changes

cuda-c-examples-torch is a skill published in the GitHub repository wenyi-li/awesome-agent-kernel-skills (9 stars, last pushed 3mo ago), with no licence file. It adds 19 tokens to every session and 4,204 once invoked, about $0.0001 per session on Opus 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 2026-09-03.

Related

Other skills, from other repositories

tilelang-cuda-examples-torch

A collection of runnable examples showing how to write CUDA kernels in TileLang, a Python-like language for GPU code, and use them with PyTorch, a machine-learning library.

mindspore-ai/akg · 20 tokens

accelerate

Run PyTorch training across GPUs with minimal changes.

NousResearch/hermes-agent · 13 tokens

developing-genkit-python

Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.

google/skills · 49 tokens

optimize-for-gpu

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS…

K-Dense-AI/scientific-agent-skills · 151 tokens

marimo-pair

Work inside the user's live marimo notebook from the code editor: run Python in the same kernel the user does, inspect live notebook state, and commit durable notebook changes through code mode. Use whenever you create, analyze, or improve the user's marimo notebook.

marimo-team/marimo · 57 tokens

minicpm5-deploy-transformers

Run MiniCPM5-1B or MiniCPM5-2B with Hugging Face Transformers for one-shot Python generation on GPU (bfloat16) or CPU (float32). Use when the user wants a quick Python script, no server, no extra deps, or asks for "Transformers", "AutoModelForCausalLM", "model.generate" with MiniCPM5.

OpenBMB/MiniCPM · 90 tokens