Imported from majiayu000/claude-skill-registry (skills/development/class-design/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry --skill class-design. Copyright stays with the author.

Class Design Conventions

Favor composition over inheritance. Use Protocol classes for interfaces and dependency injection for shared behavior.

Quick Reference

Protocol Classes for Interfaces

Use Protocol to define interfaces. This enables duck typing with static type checking.

# CORRECT - Protocol defines the interface
from typing import Protocol

class Tokenizer(Protocol):
    """Interface for text tokenization."""

    def tokenize(self, text: str) -> list[str]:
        """Split text into tokens."""
        ...

    def detokenize(self, tokens: list[str]) -> str:
        """Join tokens back into text."""
        ...


class SimpleTokenizer:
    """Whitespace tokenizer implementing Tokenizer protocol."""

    def tokenize(self, text: str) -> list[str]:
        return text.split()

    def detokenize(self, tokens: list[str]) -> str:
        return " ".join(tokens)


def process_text(tokenizer: Tokenizer, text: str) -> list[str]:
    """Works with any Tokenizer implementation."""
    return tokenizer.tokenize(text.lower())


# INCORRECT - abstract base class forces explicit inheritance
from abc import ABC, abstractmethod

class Tokenizer(ABC):
    @abstractmethod
    def tokenize(self, text: str) -> list[str]:
        pass

class SimpleTokenizer(Tokenizer):  # Must explicitly inherit
    ...

When to Use Protocol vs ABC

Composition via Dependency Injection

Inject dependencies rather than inheriting behavior.

# CORRECT - composition via dependency injection
from dataclasses import dataclass

@dataclass
class DocumentProcessor:
    """Processes documents using injected components."""

    tokenizer: Tokenizer
    embedder: Embedder
    storage: Storage

    def process(self, doc: Document) -> ProcessedDocument:
        tokens = self.tokenizer.tokenize(doc.content)
        embedding = self.embedder.embed(tokens)
        self.storage.save(doc.id, embedding)
        return ProcessedDocument(doc.id, tokens, embedding)


# Usage - compose with specific implementations
processor = DocumentProcessor(
    tokenizer=SimpleTokenizer(),
    embedder=OpenAIEmbedder(api_key=settings.api_key),
    storage=RedisStorage(url=settings.redis_url),
)

# INCORRECT - inheriting behavior from multiple classes
class DocumentProcessor(TokenizerMixin, EmbedderMixin, StorageMixin):
    def process(self, doc: Document) -> ProcessedDocument:
        tokens = self.tokenize(doc.content)  # Where does this come from?
        ...

Inheritance Rules

Maximum Depth: 2 Levels

# CORRECT - shallow hierarchy
class BaseHandler:
    """Base handler with common functionality."""
    ...

class FileHandler(BaseHandler):
    """Handles file operations."""
    ...


# INCORRECT - too deep (3+ levels)
class BaseHandler:
    ...

class IOHandler(BaseHandler):
    ...

class FileHandler(IOHandler):  # Third level - forbidden
    ...

Framework Base Classes: Allowed

Inherit from framework base classes that provide essential functionality.

# CORRECT - inheriting from framework base classes
from pydantic import BaseModel
from torch import nn

class UserConfig(BaseModel):
    """User configuration with Pydantic validation."""
    name: str
    max_retries: int = 3


class Encoder(nn.Module):
    """Neural network encoder layer."""

    def __init__(self, input_dim: int, output_dim: int) -> None:
        super().__init__()
        self.linear = nn.Linear(input_dim, output_dim)

    def forward(self, x: Tensor) -> Tensor:
        return self.linear(x)

Concrete Class Inheritance: Forbidden

Never inherit from concrete (non-abstract, non-framework) classes. Use mixins or composition.

# INCORRECT - inheriting from concrete class
class FileProcessor:
    def process(self, path: Path) -> Data:
        content = path.read_text()
        return self.parse(content)

    def parse(self, content: str) -> Data:
        ...

class JsonProcessor(FileProcessor):  # Forbidden - FileProcessor is concrete
    def parse(self, content: str) -> Data:
        return json.loads(content)


# CORRECT - use composition
class JsonParser:
    """Parses JSON content."""

    def parse(self, content: str) -> Data:
        return json.loads(content)


@dataclass
class FileProcessor:
    """Processes files using an injected parser."""

    parser: Parser

    def process(self, path: Path) -> Data:
        content = path.read_text()
        return self.parser.parse(content)


# Usage
processor = FileProcessor(parser=JsonParser())

Mixins for Shared Behavior

Use mixins only when composition isn't practical. Mixins should:

• Be small and focused on one capability
• Not maintain state
• Use clear naming (*Mixin suffix)

# CORRECT - focused mixin for logging
class LoggingMixin:
    """Adds structured logging to a class."""

    @property
    def logger(self) -> Logger:
        return logging.getLogger(self.__class__.__name__)

    def log_operation(self, operation: str, **context: Any) -> None:
        self.logger.info(operation, extra=context)


class DataLoader(LoggingMixin):
    """Loads data with logging support."""

    def load(self, path: Path) -> Data:
        self.log_operation("load_start", path=str(path))
        data = self._load_impl(path)
        self.log_operation("load_complete", path=str(path), size=len(data))
        return data

Avoid Static Methods

Use module-level functions instead of @staticmethod.

# INCORRECT - static method
class MathUtils:
    @staticmethod
    def clamp(value: float, min_val: float, max_val: float) -> float:
        return max(min_val, min(value, max_val))


# CORRECT - module-level function
def clamp(value: float, min_val: float, max_val: float) -> float:
    """Clamp value to the range [min_val, max_val]."""
    return max(min_val, min(value, max_val))

Why avoid static methods?

• They don't use class or instance state
• Module functions are simpler and more Pythonic
• Easier to import and test

Plain Attributes Over Getters/Setters

Use plain attributes. Add @property only when you need computed values or validation.

# CORRECT - plain attributes
@dataclass
class User:
    """User with plain attributes."""

    name: str
    email: str
    age: int


# CORRECT - property for computed value
class Rectangle:
    """Rectangle with computed area property."""

    def __init__(self, width: float, height: float) -> None:
        self.width = width
        self.height = height

    @property
    def area(self) -> float:
        """Computed from width and height."""
        return self.width * self.height


# INCORRECT - unnecessary getters/setters
class User:
    def __init__(self, name: str) -> None:
        self._name = name

    def get_name(self) -> str:
        return self._name

    def set_name(self, name: str) -> None:
        self._name = name

Decision Flow

Need to define an interface?
├── Yes → Use Protocol
└── No → Need shared behavior?
    ├── Yes → Can inject as dependency?
    │   ├── Yes → Use composition (dependency injection)
    │   └── No → Use a focused Mixin
    └── No → Inheriting from framework base class?
        ├── Yes → OK (BaseModel, nn.Module, etc.)
        └── No → Is it concrete?
            ├── Yes → Forbidden - refactor to composition
            └── No → OK if depth ≤ 2 levels