python-parallelization

571

benchflow-ai

Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.

simd-optimization

45

rust-works

SIMD optimization patterns and learnings for x86_64 and ARM. Use when implementing SIMD code, optimizing vectorized operations, or debugging SIMD issues. Triggers on terms like "SIMD", "AVX", "SSE", "NEON", "vectorization", "intrinsics".

matlab-performance-optimizer

27

matlab

Optimize MATLAB code for better performance through vectorization, memory management, and profiling. Use when user requests optimization, mentions slow code, performance issues, speed improvements, or asks to make code faster or more efficient.

policyengine-vectorization

21

PolicyEngine

PolicyEngine vectorization patterns - NumPy operations, where/select usage, avoiding scalar logic with arrays

Python — JAX

15

jkitchin

Expert guidance for JAX (Just After eXecution) — a high-performance numerical computing library with automatic differentiation, JIT compilation, vectorization, and GPU/TPU acceleration; covers transformations (grad, jit, vmap, pmap), important details and gotchas, and differences from NumPy

weaviate

9

TerminalSkills

Weaviate is an open-source vector database with built-in vectorization modules. Learn schema definition, GraphQL and REST APIs, hybrid search combining BM25 and vectors, and self-hosted deployment with Docker.

embedding-service

6

Lin-A1

Basic text vectorization (embedding) service. Converts natural language into high-dimensional dense vectors, providing core data support for downstream tasks such as semantic search, clustering analysis, and recommendation systems.

cargo-asm

4

Onbjerg Dot Cargo Asm

Inspects generated assembly for Rust functions using cargo-asm. Use when analyzing low-level codegen, verifying SIMD/vectorization, or debugging performance at the instruction level.

gpu-optimizer

4

Mathews-Tom

Expert GPU optimization for modern consumer GPUs (8-24GB VRAM). Use this skill when you need to optimize GPU training, speed up CUDA code, reduce OOM errors, tune XGBoost for GPU, migrate NumPy to CuPy, make a model faster, manage GPU memory, optimize VRAM usage, or benchmark PyTorch. Covers mixed precision, gradient checkpointing, XGBoost GPU acceleration, CuPy/cuDF migration, vectorization, torch.compile, and diagnostics. NVIDIA GPUs only. PyTorch, XGBoost, and RAPIDS frameworks.

logo-creator

2

vlabsai

AI-powered logo generation with iterative design workflow. Generates logo variations using Nano Banana 2 (Gemini 3.1 Flash) via OpenRouter, then processes finalists with local tools for cropping, background removal, and SVG vectorization. Use when the user asks to create a logo, design a brand mark, generate logo concepts, or needs logo variations for a project. Triggers: 'create a logo', 'design a logo', 'logo for', 'brand mark', 'logo variations', 'generate logos'.

gymnasium-vector-envs

1

chuongdlb

Gymnasium vector environment API — SyncVectorEnv, AsyncVectorEnv, batched step/reset semantics, autoreset modes, and vector wrappers for parallel RL.

using-pandas

indiosmo

Idiomatic pandas usage patterns and performance best practices. Use when writing or reviewing pandas code to ensure: (1) Modern API usage (loc/iloc, method chaining, pipe), (2) Performance optimization (vectorization, dtypes, avoiding apply), (3) Proper data reshaping (tidy data, melt/pivot), (4) Correct handling of Copy-on-Write, categoricals, time series, (5) Avoiding common gotchas and antipatterns.

python-best-practices

eggboy

Python coding best practices, conventions, and architectural patterns for production-ready applications. Use when writing, reviewing, or refactoring Python code to apply modern patterns and idiomatic style. Covers: general Python conventions (PEP 8, type hints, testing with pytest/Hypothesis/Faker), FastAPI best practices (async endpoints, error handling, OpenAPI docs, dependency injection), dataframe mindset (vectorization, columnar operations, method chaining across Pandas/Polars/DuckDB/Spark), and Python data model (dunder methods, iterators, context managers, descriptors, properties). Applicable to Python 3.12+ projects using pyproject.toml and Ruff for linting.