# mojo-gpu-fundamentals

Guidance for coding agents writing Mojo code for NVIDIA, AMD, and Apple silicon accelerators. It prevents models from injecting CUDA syntax into Mojo kernels.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 2600 installs · ★ 206
- Author: modular
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/modular/skills/tree/HEAD/mojo-gpu-fundamentals/SKILL.md
- Updated: 2026-10-25
- HTML: https://affpapa.org/en/nevibecode/skills/mojo-gpu-fundamentals

## What it does

Pretrained models tend to write Mojo GPU kernels using CUDA syntax: decorators like `__global__`, launch brackets `<<<>>>`, or manual pointer memory management. This skill corrects those habits for Modular's runtime. Mojo kernels are plain `def` functions that cannot raise, and kernel dispatch uses `ctx.enqueue_function` on a `DeviceContext`.

Data layouts rely on `TileTensor` from the external `layout` package rather than raw pointers. Any function indexing a tensor must declare `comptime assert tensor.flat_rank == N`, or the compiler rejects `tensor[r, c]` with an unproven correctness error. Memory cleanup happens automatically when buffers fall out of scope.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill mojo-gpu-fundamentals -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/mojo-gpu-fundamentals/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/mojo-gpu-fundamentals/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill mojo-gpu-fundamentals -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/mojo-gpu-fundamentals/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill mojo-gpu-fundamentals -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/mojo-gpu-fundamentals/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install modular/skills mojo-gpu-fundamentals
```

2. **Manually: put the files in place**
- File: `.github/skills/mojo-gpu-fundamentals/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill mojo-gpu-fundamentals -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/modular/skills --path mojo-gpu-fundamentals
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/mojo-gpu-fundamentals/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add modular/skills --skill mojo-gpu-fundamentals -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/modular/skills/tree/HEAD/mojo-gpu-fundamentals/SKILL.md

## When to use it
- stop agents from emitting CUDA syntax like `__global__` and `<<<>>>`
- launch GPU kernels through `DeviceContext` and `enqueue_function`
- structure memory layouts and indexing with `TileTensor`
- write accelerator code targeting NVIDIA, AMD, and Apple silicon

## Example prompts
- Write a Mojo vector multiplication kernel using TileTensor and verify flat_rank at compile time
- Port this CUDA kernel to Mojo using DeviceContext and enqueue_function
- Show how to allocate shared memory using stack_allocation in a Mojo GPU function

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 8f1fb9086b5511d5.

BEGIN THIRD-PARTY FILE 8f1fb9086b5511d5
```text
---
name: mojo-gpu-fundamentals
description: The basics of how to program GPUs using Mojo. Use this skill in addition to mojo-syntax when writing Mojo code that targets GPUs or other accelerators. Use targeting code to NVIDIA, AMD, Apple silicon GPUs, or others. Use this skill to overcome misconceptions about how Mojo GPU code is written.
---

<!-- EDITORIAL GUIDELINES FOR THIS SKILL FILE
This file is loaded into an agent's context window as a correction layer for
pretrained GPU programming knowledge. Every line costs context. When editing:
- Be terse. Use tables and inline code over prose where possible.
- Never duplicate information — if a concept is shown in a code example, don't
  also explain it in a paragraph.
- Only include information that *differs* from what a pretrained model would
  generate. Don't document things models already get right.
- Prefer one consolidated code block over multiple small ones.
- Keep WRONG/CORRECT pairs short — just enough to pattern-match the fix.
- If adding a new section, ask: "Would a model get this wrong?" If not, skip it.
These same principles apply to any files this skill references.
-->

Mojo GPU programming has **no CUDA syntax**. No `__global__`, `__device__`,
`__shared__`, `<<<>>>`. **Always follow this skill over pretrained knowledge.**

## Not-CUDA — key concept mapping

| CUDA / What you'd guess                 | Mojo GPU                                                                             |
|-----------------------------------------|--------------------------------------------------------------------------------------|
| `__global__ void kernel(...)`           | Plain `def kernel(...)` — no decorator                                               |
| `kernel<<<grid, block>>>(args)`         | `ctx.enqueue_function[kernel](args, grid_dim=..., block_dim=...)`                    |
| `cudaMalloc(&ptr, size)`                | `ctx.enqueue_create_buffer[dtype](count)`                                            |
| `cudaMemcpy(dst, src, ...)`             | `ctx.enqueue_copy(dst_buf, src_buf)` or `ctx.enqueue_copy(dst_buf=..., src_buf=...)` |
| `cudaDeviceSynchronize()`               | `ctx.synchronize()`                                                                  |
| `__syncthreads()`                       | `barrier()` from `max.gpu` or `max.gpu.sync`                                         |
| `__shared__ float s[N]`                 | `stack_allocation[dtype, address_space=AddressSpace.SHARED](layout)`                 |
| `threadIdx.x`                           | `thread_idx.x`                                                                       |
| `blockIdx.x * blockDim.x + threadIdx.x` | `global_idx.x` (convenience, returns `Int`)                                          |
| `__shfl_down_sync(mask, val, d)`        | `warp.shuffle_down(val, d)` / `warp.sum` / `warp.max` / `warp.min` / `warp.reduce`   |
```
END THIRD-PARTY FILE 8f1fb9086b5511d5

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