type-hints_skill

Overview

This skill teaches practical Python type hints, static analysis, and mypy configuration to improve code reliability and developer experience. It covers basic and advanced typing constructs, runtime typing tools, and workflows for gradual adoption. You will learn how to write annotations that enable better IDE support, clearer APIs, and fewer runtime errors.

How this skill works

The skill inspects code patterns and explains appropriate type annotations for variables, functions, and classes. It demonstrates configuring and running mypy, interpreting its output, and using type stubs and ignore directives when needed. It also shows how to combine static typing with runtime validators and dataclass/attrs patterns for robust validation.

When to use it

• When you want stronger guarantees and earlier detection of bugs in Python code.
• When onboarding teams that need consistent API contracts and clearer interfaces.
• When integrating third-party libraries and you need or provide type stubs (.pyi).
• When improving IDE completions and refactor safety across a codebase.
• When adopting gradual typing to incrementally improve legacy code.

Best practices

• Start with function signatures and public APIs before annotating every local variable.
• Use mypy strict settings gradually; add per-module overrides while improving coverage.
• Prefer typing.Protocol for structural interfaces over ad-hoc duck typing when possible.
• Adopt TypeVar and generics for reusable container and algorithm code.
• Keep runtime validation (Pydantic or custom checks) for security-critical inputs.

Example use cases

• Annotating a web service layer so request/response shapes are explicit and validated.
• Adding generics to a utilities library to make functions type-safe across types.
• Configuring CI to run mypy and enforce a minimum type coverage threshold.
• Creating .pyi stubs for a C-extension or untyped third-party package.
• Combining dataclasses with types and Pydantic models for validation plus static checks.