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Profile and optimize Python code using cProfile, memory profilers, and performance best practices. \n \n Covers CPU profiling with cProfile, line-by-line profiling with line_profiler, memory tracking with memory_profiler, and production profiling with py-spy \n Includes 20+ optimization patterns: list comprehensions, generators, string concatenation, dictionary lookups, NumPy vectorization, caching, multiprocessing, and async I/O \n Provides database optimization techniques including batch opera

python-executor

Modern Python project setup with uv, ruff, and ty for Python 3.11+. \n \n Replaces pip, Poetry, black, flake8, mypy, and pre-commit with faster, simpler alternatives from the Astral team \n Covers new project creation, dependency management via uv add / uv remove , and linting/formatting/type-checking workflows \n Includes migration paths from legacy tooling (requirements.txt, setup.py, flake8+black+isort, mypy/pyright) \n Provides decision tree for single-file scripts (PEP 723), simple projects

Structured input validation, exception design, and graceful failure handling for Python applications. \n \n Covers fail-fast validation patterns, meaningful exception hierarchies, and partial failure handling for batch operations \n Includes Pydantic integration for complex input validation with automatic error messages and custom exception types with context \n Demonstrates exception chaining to preserve debug trails, batch processing with per-item error tracking, and progress reporting for lon

Production-quality Python coding standards for writing clean, maintainable, modern Python code (versions 3.10-3.13).

You are an expert in Python development with uv package management.

A comprehensive guide to using CCXT in Python projects for cryptocurrency exchange integration.

Comprehensive testing strategies for Python using pytest, fixtures, mocking, and test-driven development. \n \n Covers unit, integration, functional, and performance testing with the AAA pattern (Arrange, Act, Assert) for test structure \n Includes 10 fundamental and advanced patterns: basic tests, fixtures with setup/teardown, parameterization, mocking, exception handling, async testing, monkeypatching, temporary files, custom fixtures, and property-based testing \n Provides test design princip

mojo-python-interop

Async task processing patterns for decoupling long-running work from request/response cycles. \n \n Covers core patterns including immediate job ID returns, task queue configuration with Celery, idempotency strategies, and job state management for visibility \n Includes advanced workflows: dead letter queues for failed tasks, status polling endpoints, task chaining, and parallel execution \n Provides examples for Celery, RQ, and Dramatiq, plus guidance on cloud-native alternatives like AWS SQS a