build-cython-ext

What it does

This skill enables AI agents to compile Cython extensions directly within their environment. It bridges the gap between Python code and high-performance compiled binaries, allowing agents to optimize numerical or computationally intensive tasks by generating C extensions from .pyx source files.

When to use it

• Performance Optimization: Accelerate slow Python loops in data processing pipelines where standard interpretation is too sluggish.
• Native Extension Development: Create custom C libraries that interact seamlessly with existing Python codebases without manual compilation steps.
• Scientific Computing: Build specialized numerical modules for physics simulations or machine learning preprocessing that require low-level control.
• Dependency Management: Automate the build process for projects relying on Cython-optimized dependencies like NumPy or SciPy internals.

Key capabilities

• Compiles .pyx files into shared object (.so) libraries.
• Integrates with the underlying C compiler toolchain.
• Handles dependency resolution for build-time requirements.
• Generates optimized machine code from Python syntax.

Example prompts

• "Compile this Cython script to a native extension and verify it loads correctly in Python."
• "Optimize this numerical algorithm by converting the Python loops into a Cython extension."
• "Build a custom C module from this source file and expose it as a Python package."

Tips & gotchas

Ensure your environment has a compatible C compiler (like GCC or Clang) installed before attempting to build extensions, as the skill relies on native toolchains. Complex projects may require explicit configuration of include paths or linker flags if standard compilation fails.