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python_developer

Guides Python development with FastAPI, Pydantic, Pytest, and PEP 8 standards.

Updated Jan 14, 2026
Search Tags:#code quality#pytest#pydantic#fastapi#python#asyncio#pep 8
One-click install
npx skills add https://github.com/jvsandhu/agentic-skills --skill python-developer-jvsandhu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python_developer
Source: https://github.com/jvsandhu/agentic-skills/tree/main/skills/python_developer
Command: npx skills add https://github.com/jvsandhu/agentic-skills --skill python-developer-jvsandhu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Python projects often suffer from inconsistent style, missing validation, poor concurrency choices, and weak test coverage. This Skill provides a structured, phase-based workflow that enforces modern Python standards from environment setup through testing. ## Core Features & Use Cases - Environment & Dependency Management: Sets up isolated virtual environments with venv or poetry and enforces Python 3.9+ type hints. - API & Application Logic: Builds asynchronous FastAPI endpoints, validates input with Pydantic models, and selects Multiprocessing or AsyncIO based on workload type. - Testing & Code Quality: Runs static analysis with Ruff or Flake8 and writes unit and integration tests with Pytest. - Use Case: When building a new FastAPI microservice, follow the workflow to scaffold the environment, define Pydantic-validated async endpoints, and verify quality with linting and Pytest before release. ## Quick Start Use the python developer skill to review my FastAPI project and bring it in line with PEP 8, type hints, and Pytest coverage.

Frequently Asked Questions about python_developer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure a Python project with FastAPI and Pydantic?▼

Start with an isolated virtual environment using venv or poetry, then build asynchronous FastAPI endpoints with async def. Validate all input data with Pydantic models and add Python 3.9+ type hints throughout for readability.

When should I use Multiprocessing vs AsyncIO in Python?▼

Use Multiprocessing for CPU-heavy tasks that are blocked by the Global Interpreter Lock, and AsyncIO for I/O-heavy tasks like network calls or file operations. Choosing based on workload type avoids concurrency bottlenecks.

What linting tools work best for Python code quality?▼

Ruff and Flake8 both perform static code analysis to enforce PEP 8 compliance. Ruff is a modern, fast option, while Flake8 is widely established; either integrates into a standard Python workflow.

Does Python's GIL affect my FastAPI application's performance?▼

Yes, the Global Interpreter Lock limits true parallelism for CPU-bound threads. For CPU-heavy work, offload to Multiprocessing; FastAPI's async endpoints already handle I/O-bound concurrency efficiently despite the GIL.

How do I write tests for a Python API with Pytest?▼

Write unit tests for individual functions and integration tests for API endpoints using Pytest. Combine this with Ruff or Flake8 linting to verify both correctness and PEP 8 style compliance before deployment.

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