python-type-safety

Configure mypy or pyright for strict static type checking of Python code.

4|Updated Mar 3, 2026
One-click install
npx skills add https://github.com/AI-Foundry-Core/ril-agents --skill python-type-safety-ai-foundry-core
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: python-type-safety
Source: https://github.com/AI-Foundry-Core/ril-agents/tree/main/plugins/python-development/skills/python-type-safety
Command: npx skills add https://github.com/AI-Foundry-Core/ril-agents --skill python-type-safety-ai-foundry-core

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Python type safety with type hints, generics, protocols, and strict type checking to catch errors early and improve code readability.

Core Features & Use Cases

  • Type annotations for public APIs and internal logic to make intent explicit and enable static analysis.
  • Generics and protocols to define reusable, structurally typed interfaces.
  • Guidance for configuring mypy or pyright to enforce strict checks in CI and development environments.
  • Practical patterns for building type-safe APIs, data models, and robust error handling.

Quick Start

Annotate public functions and classes with explicit types, then run mypy --strict to validate types across the codebase.

Frequently Asked Questions about python-type-safety

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

FAQPage Schema
How do I enforce strict type checking in Python to catch errors before runtime?

Python protocols enable structurally typed interfaces by defining method signatures without inheritance. This allows static analysis tools to verify duck typing compatibility, ensuring objects meet interface requirements without explicit subclassing.

How do I configure mypy or pyright for strict type safety in a Python project?

Configuring mypy or pyright for strict type safety involves setting strict flags in the project configuration file to enforce explicit typing discipline across all modules, enabling robust validation during CI and local development.

How do I use generics in Python to define reusable type-safe interfaces?

Generics in Python allow defining reusable, type-safe interfaces by parameterizing classes and functions with type variables. This enables static analysis to track specific data types through reusable components, catching type mismatches early.

Can I build type-safe Python APIs and data models using type hints?

Type-safe Python APIs and data models are built by applying explicit type annotations to public APIs and internal logic. This makes intent explicit and enables static analysis to validate data flow and catch type errors.

Why use strict static analysis for Python instead of relying on runtime checks?

Strict static analysis validates type correctness before deployment, shifting error detection left. It improves code readability by making intent explicit through type hints, reducing runtime failures compared to relying solely on runtime checks.