Coding Standards

Enforce Python coding standards for linting, type hints, and logging validation.

1|Updated Apr 4, 2026

Search Tags:#linting #pydantic #python #type-hints #coding-standards #code-style #loguru

Ready to run

npx skills add https://github.com/lagarcess/argus --skill coding-standards-lagarcess

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Coding Standards
Source: https://github.com/lagarcess/argus/tree/main/.agent/skills/coding-standards
Command: npx skills add https://github.com/lagarcess/argus --skill coding-standards-lagarcess

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill defines and enforces consistent Python coding practices for the Argus engine to reduce runtime errors, improve maintainability, and ensure reliable logging and validation across modules.

Core Features & Use Cases

Logging rules: mandate using loguru and structured logging instead of print or the standard logging module.
Type safety & validation: require 100% type hint coverage and Pydantic models for data crossing boundaries.
Style & organization: enforce a 90-character line limit, double-quoted strings, import ordering, and focused module layout.
Use Case: Apply these standards when reviewing pull requests to automatically detect missing type annotations, improper logging usage, or configuration validation gaps.

Quick Start

Run an analysis of the repository and report all violations of the Argus Coding Standards, listing files, line numbers, and suggested fixes.

Frequently Asked Questions about Coding Standards

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

FAQPage Schema

How do I enforce Python type hints and Pydantic model validation in my codebase?▼

Enforce Python type hints and Pydantic validation by applying coding standards that require 100% type hint coverage and Pydantic models for data crossing boundaries. This reduces runtime errors and improves maintainability across modules.

What is the best way to replace standard logging with loguru for structured logging in Python?▼

The best way to replace standard logging with loguru is to apply coding rules that mandate loguru for structured logging instead of print or the standard logging module. This ensures reliable logging across your Python modules.

How do I check for missing type annotations and improper logging in a pull request?▼

Check for missing type annotations and improper logging by running a repository analysis against Python coding standards. It reports all violations, listing files, line numbers, and suggested fixes for pull requests.

Does this coding standard support a 90-character line limit and import ordering rules for Python?▼

Yes, this Python coding standard supports a 90-character line limit and import ordering rules. It enforces style and organization by requiring double-quoted strings and focused module layout.

Why use Pydantic models for configuration validation in Python?▼

Use Pydantic models for configuration validation to ensure data crossing boundaries is strictly checked. This eliminates configuration validation gaps and reduces runtime errors in Python modules.