Scripting

Implement Git hooks using Husky, pre-commit, and custom scripts. Enforce code quality, linting, and testing before commits and pushes.

This skill should be used when editing Python code in the erk codebase. Use when writing, reviewing, or refactoring Python to ensure adherence to LBYL exception handling patterns, Python 3.13+ type syntax (list[str], str | None), pathlib operations, ABC-based interfaces, absolute imports, and explicit error boundaries at CLI level. Also provides production-tested code smell patterns from Dagster Labs for API design, parameter complexity, and code organization. Essential for maintaining erk's dignified Python standards.

Use the Gunshi library to create command-line interfaces in JavaScript/TypeScript.

Ensure thread safety in concurrent Python code. Use when dealing with shared state, avoiding race conditions, implementing locks and synchronization primitives, and handling the GIL in multiprocessing.

Optimize memory usage in Python numerical computing. Use when reducing memory footprint, choosing efficient data types, avoiding copies, and managing large arrays.

Execute shell commands from Python. Use for running CLI tools, scripts, or system commands.

Bioinformatics library for sequence analysis. Use when working with DNA/RNA/protein sequences, FASTA files, or biological data.

Browser automation with Playwright for modern web scraping. Use this skill for scraping JavaScript-rendered pages, handling complex interactions, managing multiple browser contexts, or testing web applications. NOT needed for static HTML parsing or processing already-fetched content.

Use Python's multiprocessing module for parallel execution. Use when parallelizing CPU-bound tasks, creating process pools, sharing data between processes, and avoiding the GIL.

BioPython library for computational molecular biology. Use when working with biological sequence data, parsing bioinformatics file formats (FASTA, GenBank, PDB), performing sequence analysis, or accessing biological databases programmatically.

Create and manipulate 3D models using Blender's Python API (bpy). Use for mesh modeling, animation, rendering, and batch processing. Requires Blender installation; scripts run inside Blender or via command line.

Accelerate Python numerical functions using Numba's JIT compilation. Use when optimizing loops and array operations by compiling to machine code with @jit/@njit decorators.

Numba JIT compilation for accelerating numerical Python code. Use when optimizing loops, array operations, or numerical computations with @njit decorator for significant speedups.

Profile and benchmark Python code to measure execution time and identify bottlenecks. Use when analyzing performance, comparing implementations, or diagnosing slow code.

NumPy library for numerical computing in Python. Use for array operations, linear algebra, statistical calculations, normalization, and mathematical transformations.

Common parallel programming patterns for Python multiprocessing. Use when designing parallel algorithms with map-reduce, producer-consumer, pipeline parallelism, fork-join, and work distribution strategies.

WebAssembly runtime execution and instantiation. Use when running compiled WebAssembly modules, testing WASM execution, or interfacing with WASM instances from Python.

Generate high-quality meshes using pygmsh, a Python interface to Gmsh. Use for creating 2D/3D meshes from CAD geometry, structured meshing, and mesh refinement for FEM simulations.

File system operations in Python. Use when reading, writing, copying, or managing files and directories.

NumPy library for numerical computing in Python. Use for array operations, statistical calculations, matrix operations, and numerical analysis of network data.