Does this skill support modern Python tooling?

Yes, it is optimized for modern tools like uv for package management, ruff for linting, and mypy for static type analysis.

Does it enforce specific coding styles?

Yes, it enforces the use of pathlib for file operations, f-strings for formatting, and comprehensive type hints for all function signatures.

Is this skill suitable for academic research?

Absolutely; it is specifically designed to enforce the rigor and replicability required for academic and scientific computing where correctness is paramount.

What is the fail-fast philosophy in this skill?

It ensures code fails immediately upon encountering invalid configurations or missing data instead of using silent defaults, preventing hidden errors in research data.

How does this skill handle testing?

It enforces a TDD workflow that uses existing project fixtures and real production data while strictly prohibiting the creation of unnecessary mock data or new databases.

Advanced Python Development

Name: Advanced Python Development
Author: nicsuzor

bynicsuzor

0•

Data Science & ML

Enforces rigorous, production-grade Python standards with a fail-fast philosophy and strict type safety for academic and research environments.

This skill equips Claude with a high-standard Python development framework designed for academic and research contexts where data integrity and replicability are critical. It mandates a fail-fast approach that avoids silent errors by removing default fallbacks, enforces strict type-checking with MyPy, and implements a Test-Driven Development (TDD) workflow that prioritizes real-world data over mocks. By integrating modern tools like uv, ruff, and Pydantic, it ensures that code is not only functional but maintainable and robust against silent failures in complex pipelines.

Key Features

01Strict type safety and Pydantic validation for all data structures

02TDD workflow focusing on real production data and existing infrastructure

03Fail-fast philosophy to prevent silent data corruption in research

04Integration with modern Python tools including uv, ruff, and mypy

05Standardized docstring enforcement and modern Python idiom adoption

060 GitHub stars

Use Cases

01Refactoring legacy Python code to meet modern type-safe and fail-fast standards

02Building reproducible research pipelines and data processing scripts

03Developing production-grade Python APIs using FastAPI and Pydantic

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nicsuzor/aops-dist python-dev

For use in Claude.ai and ChatGPT

Download Skill