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python-typing-patterns skill

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This skill helps you write safe Python code by teaching modern typing patterns, including TypeVar, Protocols, TypedDicts, and union types.

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---
name: python-typing-patterns
description: "Python type hints and type safety patterns. Triggers on: type hints, typing, TypeVar, Generic, Protocol, mypy, pyright, type annotation, overload, TypedDict."
compatibility: "Python 3.10+ (uses union syntax X | Y). Some patterns require 3.11+ (Self, TypeVarTuple)."
allowed-tools: "Read Write"
depends-on: []
related-skills: [python-pytest-patterns]
---

# Python Typing Patterns

Modern type hints for safe, documented Python code.

## Basic Annotations

```python
# Variables
name: str = "Alice"
count: int = 42
items: list[str] = ["a", "b"]
mapping: dict[str, int] = {"key": 1}

# Function signatures
def greet(name: str, times: int = 1) -> str:
    return f"Hello, {name}!" * times

# None handling
def find(id: int) -> str | None:
    return db.get(id)  # May return None
```

## Collections

```python
from collections.abc import Sequence, Mapping, Iterable

# Use collection ABCs for flexibility
def process(items: Sequence[str]) -> list[str]:
    """Accepts list, tuple, or any sequence."""
    return [item.upper() for item in items]

def lookup(data: Mapping[str, int], key: str) -> int:
    """Accepts dict or any mapping."""
    return data.get(key, 0)

# Nested types
Matrix = list[list[float]]
Config = dict[str, str | int | bool]
```

## Optional and Union

```python
# Modern syntax (3.10+)
def find(id: int) -> User | None:
    pass

def parse(value: str | int | float) -> str:
    pass

# With default None
def fetch(url: str, timeout: float | None = None) -> bytes:
    pass
```

## TypedDict

```python
from typing import TypedDict, Required, NotRequired

class UserDict(TypedDict):
    id: int
    name: str
    email: str | None

class ConfigDict(TypedDict, total=False):  # All optional
    debug: bool
    log_level: str

class APIResponse(TypedDict):
    data: Required[list[dict]]
    error: NotRequired[str]

def process_user(user: UserDict) -> str:
    return user["name"]  # Type-safe key access
```

## Callable

```python
from collections.abc import Callable

# Function type
Handler = Callable[[str, int], bool]

def register(callback: Callable[[str], None]) -> None:
    pass

# With keyword args (use Protocol instead)
from typing import Protocol

class Processor(Protocol):
    def __call__(self, data: str, *, verbose: bool = False) -> int:
        ...
```

## Generics

```python
from typing import TypeVar

T = TypeVar("T")

def first(items: list[T]) -> T | None:
    return items[0] if items else None

# Bounded TypeVar
from typing import SupportsFloat

N = TypeVar("N", bound=SupportsFloat)

def average(values: list[N]) -> float:
    return sum(float(v) for v in values) / len(values)
```

## Protocol (Structural Typing)

```python
from typing import Protocol

class Readable(Protocol):
    def read(self, n: int = -1) -> bytes:
        ...

def load(source: Readable) -> dict:
    """Accepts any object with read() method."""
    data = source.read()
    return json.loads(data)

# Works with file, BytesIO, custom classes
load(open("data.json", "rb"))
load(io.BytesIO(b"{}"))
```

## Type Guards

```python
from typing import TypeGuard

def is_string_list(val: list[object]) -> TypeGuard[list[str]]:
    return all(isinstance(x, str) for x in val)

def process(items: list[object]) -> None:
    if is_string_list(items):
        # items is now list[str]
        print(", ".join(items))
```

## Literal and Final

```python
from typing import Literal, Final

Mode = Literal["read", "write", "append"]

def open_file(path: str, mode: Mode) -> None:
    pass

# Constants
MAX_SIZE: Final = 1024
API_VERSION: Final[str] = "v2"
```

## Quick Reference

| Type | Use Case |
|------|----------|
| `X \| None` | Optional value |
| `list[T]` | Homogeneous list |
| `dict[K, V]` | Dictionary |
| `Callable[[Args], Ret]` | Function type |
| `TypeVar("T")` | Generic parameter |
| `Protocol` | Structural typing |
| `TypedDict` | Dict with fixed keys |
| `Literal["a", "b"]` | Specific values only |
| `Final` | Cannot be reassigned |

## Type Checker Commands

```bash
# mypy
mypy src/ --strict

# pyright
pyright src/

# In pyproject.toml
[tool.mypy]
strict = true
python_version = "3.11"
```

## Additional Resources

- `./references/generics-advanced.md` - TypeVar, ParamSpec, TypeVarTuple
- `./references/protocols-patterns.md` - Structural typing, runtime protocols
- `./references/type-narrowing.md` - Guards, isinstance, assert
- `./references/mypy-config.md` - mypy/pyright configuration
- `./references/runtime-validation.md` - Pydantic v2, typeguard, beartype
- `./references/overloads.md` - @overload decorator patterns

## Scripts

- `./scripts/check-types.sh` - Run type checkers with common options

## Assets

- `./assets/pyproject-typing.toml` - Recommended mypy/pyright config

---

## See Also

This is a **foundation skill** with no prerequisites.

**Related Skills:**
- `python-pytest-patterns` - Type-safe fixtures and mocking

**Build on this skill:**
- `python-async-patterns` - Async type annotations
- `python-fastapi-patterns` - Pydantic models and validation
- `python-database-patterns` - SQLAlchemy type annotations

Overview

This skill teaches modern Python type hints and practical patterns for type safety and clearer code. It focuses on annotations, collections, generics, Protocols, TypedDict, type guards, and running mypy/pyright checks. The goal is predictable, self-documented APIs and safer refactors.

How this skill works

The skill inspects code patterns related to type hints (TypeVar, Generic, Protocol, TypedDict, overload, Literal, Final) and suggests idiomatic replacements and refinements. It explains how to use collection ABCs, structural typing, TypeGuards, and bounded TypeVars, and shows commands to run static checkers like mypy and pyright. It also points to common configuration and runtime-validation approaches.

When to use it

  • When adding or improving type annotations in a codebase
  • Before releasing a library to ensure stable public APIs
  • While writing generic utilities or reusable components
  • When onboarding teams to a consistent typing style
  • To catch type regressions with CI type checks

Best practices

  • Prefer collection ABCs (Sequence, Mapping) over concrete types for flexibility
  • Use modern union syntax (X | None) on Python 3.10+ and avoid Optional alias confusion
  • Use TypedDict for structured dict shapes and Required/NotRequired for precise keys
  • Prefer Protocols for structural typing instead of inheritance when possible
  • Leverage TypeGuard functions to narrow runtime types for safer code paths
  • Run static checkers (mypy/pyright) in CI with strict settings

Example use cases

  • Annotate public functions and internal helpers to document expectations and catch bugs
  • Define a Protocol for plugins that only require a small callable surface
  • Create a generic first() helper using TypeVar to preserve element types
  • Use TypedDict for JSON-like API responses with required and optional fields
  • Write a TypeGuard to validate dynamic input like web payloads before processing

FAQ

Should I always enable strict mypy?

Strict mode is recommended for libraries and critical code to catch more issues early; for large legacy projects, enable incrementally with per-module settings.

When to choose Protocol over ABC?

Use Protocol when you want structural compatibility without requiring subclasses; use ABC if you need shared implementation or registration hooks.