Write idiomatic MLX code for machine learning on Apple Silicon, implementing arrays, neural networks, training loops, lazy evaluation, unified memory, Metal GPU acceleration, and PyTorch migrations.
A Claude Code skill for writing correct, idiomatic Apple MLX code on Apple Silicon.
In Claude Code, run:
/plugin marketplace add tkwn2080/mlx-dev-skill
The skill will be automatically available after adding the marketplace.
Clone and copy to your personal skills directory:
git clone https://github.com/tkwn2080/mlx-dev-skill.git
cp -r mlx-dev-skill/skills/mlx-dev ~/.claude/skills/
Then restart Claude Code.
When working with MLX, Claude will automatically:
mx.eval() at loop boundariesmx.array, slices create copies)__call__() not forward() for nn.Modulemx.compile()at[] syntax, gather/scattermx.compile() patterns, state capture, shapeless modevalue_and_grad, custom vjp, control flowmlx-dev-skill/
├── .claude-plugin/
│ └── plugin.json # Plugin metadata
├── skills/
│ └── mlx-dev/
│ ├── SKILL.md # Main skill entry point
│ ├── references/
│ │ ├── array-indexing.md
│ │ ├── compilation.md
│ │ ├── dtypes.md
│ │ ├── error-decoder.md
│ │ ├── gradients.md
│ │ ├── memory-management.md
│ │ ├── neural-networks.md
│ │ ├── pytorch-migration.md
│ │ └── random.md
│ └── scripts/
│ └── check_memory.py
├── README.md
└── LICENSE
The skill includes a memory debugging script:
# Show current memory stats
uv run python ~/.claude/skills/mlx-dev/scripts/check_memory.py
# Monitor continuously
uv run python ~/.claude/skills/mlx-dev/scripts/check_memory.py --watch
# Log to CSV for analysis
uv run python ~/.claude/skills/mlx-dev/scripts/check_memory.py --watch --log memory.csv
uv add mlx)MIT
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