python-bot-standards

Standardize Python backend coding practices and modular architecture for PolyFlup.

18|3|Updated Jan 2, 2026
One-click install
npx skills add https://github.com/Niller2005/PolyFlup --skill python-bot-standards
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: python-bot-standards
Source: https://github.com/Niller2005/PolyFlup/tree/main/.opencode/skill/python-bot-standards
Command: npx skills add https://github.com/Niller2005/PolyFlup --skill python-bot-standards

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coding standards, modular architecture, and common execution patterns for the PolyFlup Python backend to improve maintainability, readability, and reliability across multiple services.

Core Features & Use Cases

  • Modular architecture mapping to src.trading.*, src.data.market_data, src.config, and related packages to promote separation of concerns and testability.
  • Standardized execution patterns including deterministic function calls, centralized error handling, and consistent logging to simplify onboarding and debugging.
  • Clear guidelines for imports, formatting, testing, and security to reduce integration risk across new features and refactors.

Quick Start

Adopt these standards whenever adding or refactoring Python backend modules to ensure consistency and safety.

Frequently Asked Questions about python-bot-standards

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

FAQPage Schema
What are the Python backend coding standards for modular architecture and maintainability?

Python backend coding standards enforce modular architecture by mapping packages like src.trading, src.data.market_data, and src.config to separate concerns. This improves maintainability, readability, and reliability across multiple trading and data services.

How do I standardize Python imports and formatting for backend modules?

To standardize Python backend modules, enforce absolute imports from src.*, use 4-space indentation, and limit line length to 90 characters. These formatting rules reduce integration risk across new features and refactors.

What's the best way to handle logging and thread-safety in Python backend services?

The best way to handle logging and thread-safety is applying standardized execution patterns with centralized error handling and robust logging. This simplifies onboarding and debugging while ensuring thread-safe operations across backend services.

Does this Python backend architecture require centralized configuration for new features?

Yes, this Python backend architecture requires centralized configuration in src.config.settings. Centralizing configuration provides a documented audit trail and ensures consistency and safety when adding or refactoring modules.

When do I need type hints and an audit trail for Python backend refactoring?

You need type hints and a documented audit trail during Python backend refactoring and onboarding to ensure deterministic function calls and maintain reliability. These guidelines reduce integration risk across multiple services.