# tilegym-converting-cutile-to-julia

Translates cuTile Python GPU kernels into cuTile.jl for Julia with 1-based indexing and column-major memory layout. Useful when porting workloads to Blackwell GPUs.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1500 installs · ★ 3735
- Author: TileGym Team
- License: not stated · policy=link-only · description only; the file stays with the author
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/tilegym-converting-cutile-to-julia/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/tilegym-converting-cutile-to-julia

## What it does

The skill converts `@ct.kernel` Python routines into standalone Julia functions using `cuTile.jl`. It changes zero-based indices to one-based offsets, handles row-major to column-major array conversions, and resolves type differences. In Julia, functions like `ct.full()` do not exist, so the skill inserts `fill`, `zeros`, or `ones`.

Kernels live in a standalone `julia/` directory with an isolated `Project.toml`, avoiding any Python runtime bridge. Testing relies on standard `Test` and `NNlib.jl`. The workflow also runs `validate_cutile_jl.py` to spot common bugs like unbroadcasted `max.(a, b)` calls before compiling.

Hardware limitations are strict. You need a Blackwell GPU with compute capability 10+ and a CUDA 13.1+ driver. Without this exact setup, compiling or executing the generated Julia kernels will fail.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tilegym-converting-cutile-to-julia/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tilegym-converting-cutile-to-julia/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tilegym-converting-cutile-to-julia/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tilegym-converting-cutile-to-julia/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tilegym-converting-cutile-to-julia
```

2. **Manually: put the files in place**
- File: `.github/skills/tilegym-converting-cutile-to-julia/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tilegym-converting-cutile-to-julia
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tilegym-converting-cutile-to-julia/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tilegym-converting-cutile-to-julia -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/NVIDIA/skills/tree/HEAD/skills/tilegym-converting-cutile-to-julia/SKILL.md

## When to use it
- translate Python @ct.kernel code to cuTile.jl functions
- convert tensor indices from 0-based to 1-based
- remap row-major memory layouts to Julia column-major arrays
- validate cuTile.jl syntax with validate_cutile_jl.py before running tests

## Example prompts
- Port this cuTile Python kernel into julia/kernels/matmul.jl
- Run validate_cutile_jl.py on julia/kernels/softmax.jl and fix any IRError risks
- Convert this @ct.kernel function to cuTile.jl using standard Julia layout

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