Claude Code for Mojo Language — Workflow Guide

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The Setup

You are writing high-performance code with Mojo, the programming language from Modular that combines Python syntax with systems-level performance. Mojo is designed for AI/ML workloads, providing C/C++-level speed while being a superset of Python. Claude Code can write Python, but it generates standard CPython code instead of leveraging Mojo’s performance features.

What Claude Code Gets Wrong By Default

  1. Writes pure Python without Mojo types. Claude uses def and dynamic Python types. Mojo has fn for strict functions with typed parameters, struct instead of class for value semantics, and var/let for variable declarations with mandatory types.

  2. Uses Python data structures for performance code. Claude creates list and dict for numerical computation. Mojo has SIMD, Tensor, and Buffer types for hardware-optimized data structures that map directly to CPU/GPU registers.

  3. Ignores ownership and borrowing. Claude passes objects freely without considering memory. Mojo has an ownership model with owned, borrowed, and inout parameter conventions — these control memory safety and enable zero-copy operations.

  4. Does not use SIMD vectorization. Claude writes scalar loops for numerical operations. Mojo has first-class SIMD support: SIMD[DType.float32, 8] processes 8 float32 values in a single instruction.

The CLAUDE.md Configuration

# Mojo Project

## Language
- Language: Mojo (Python superset with systems performance)
- Compiler: Mojo compiler (AOT compilation)
- Interop: Full Python interoperability
- Target: AI/ML and high-performance computing

## Mojo Rules
- fn: strict typed functions (preferred for performance)
- def: Python-compatible dynamic functions
- struct: value types (not class) for performance
- var: mutable variable, let: immutable binding
- SIMD: vectorized operations with SIMD[type, width]
- Ownership: owned, borrowed, inout parameters
- Traits: similar to Rust traits for polymorphism

## Conventions
- Use fn over def for performance-critical code
- struct for data types, not class
- Explicit types on all fn parameters and returns
- Use SIMD for numerical loops
- @parameter for compile-time parameters
- Use Python interop for libraries not yet in Mojo
- Profile with mojo build --profile flag

Workflow Example

You want to optimize a matrix multiplication kernel. Prompt Claude Code:

“Write a high-performance matrix multiplication in Mojo. Use SIMD vectorization, tiling for cache efficiency, and parallelize across cores. Compare the approach with a naive Python implementation to show the performance difference.”

Claude Code should create a fn matmul with typed Tensor parameters, implement tiling with compile-time @parameter for tile size, use SIMD for the inner loop vectorization, add @parallelize for multi-core execution, and include a Python comparison function using def with numpy for benchmarking.

Common Pitfalls

  1. Using def everywhere instead of fn. Claude writes def functions out of Python habit. Mojo’s fn enforces strict typing and enables compiler optimizations — def is for Python compatibility, fn is for performance.

  2. Missing type annotations on struct fields. Claude defines struct fields without types. Mojo structs require explicit types for every field — the compiler cannot infer struct field types like Python class attributes.

  3. Forgetting Python interop imports. Claude tries to use Python libraries directly in Mojo. Python libraries must be imported through from python import Python and called via Python.import_module("numpy") — direct imports only work for Mojo-native packages.