“Python”

Deliver Python backends across async FastAPI and Django or Flask service styles while keeping API design, validation, auth, and service behavior explicit.

Python refactoring for readability, maintainability, and performance.

Async Python for high-performance services.

Python backend patterns - FastAPI, Django, Flask, project structure, async.

Call Gala from Python — PyO3 bridge, Jupyter kernel, PyTorch integration, NumPy arrays

Performs comprehensive code reviews for Python files following PEP 8 and Google Python Style Guide standards.

Maintainable Python for Catalyst: tooling discipline, idiomatic patterns, separation of concerns, SOLID boundaries, thin decorators, pragmatic use of advanced…

Transform sequential Python code into parallel/concurrent implementations.

Load for Python authoring, refactoring, packaging, or testing.

Guiding principles for writing clear, concise, human readable and maintainable Python code.

Python 部署文档生成流程。用于梳理 Python 服务、任务、CLI 和脚本的构建、依赖、配置、迁移、发布顺序和回滚方案。

Write and evaluate effective Python tests using pytest.

Run vulture to detect dead code in Python projects, then carefully evaluate each finding before acting.

Expert Python 3.9+ engineer — FastAPI, Django, Flask, async/await, uv package manager, type hints, ruff linting, mypy type checking, pytest, pydantic, and…

Type hints that actually help — Optional, generics, Protocol, and the mutable-default and import-cycle traps around them.

Deep, whole-codebase review of a Python project that writes a new REVIEW.md, a verified and prioritized list of fixes and improvements.

Use when writing Python code with the Anthropic SDK: creating messages, streaming responses, batch processing, token counting, file uploads, model listing, or…

Use the DataSciencePython tutorial/example collection through modern self-contained helpers for Python data-science resources, statsmodels logistic…

Python FastAPI development with uv package manager, modular project structure, SQLAlchemy ORM, and production-ready patterns.

Python asyncio and concurrent programming patterns for high-performance applications.

Create a minimal uv-managed Python project after collecting user inputs.

Run Python code quality checks (ruff, black, mypy, pytest).

Review Python diffs for idiomatic style, type annotations, exception handling, and import hygiene (.py files only).

Python rules for typing at boundaries, error handling, data shapes, and test honesty.