python-design-patterns

Q: How do I install python-design-patterns?

Run npx skills add https://github.com/julianobarbosa/claude-code-skills --skill python-design-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-design-patterns/ (personal) or .claude/skills/python-design-patterns/ (project).

by julianobarbosaCode Quality & Workflow

Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use when making architecture decisions, refactoring code structure, or evaluating when abstractions are appropriate.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/julianobarbosa/claude-code-skills --skill python-design-patterns

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads python-design-patterns automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Python Design Patterns

Write maintainable Python code using fundamental design principles. These patterns help you build systems that are easy to understand, test, and modify.

When to Use This Skill

Designing new components or services
Refactoring complex or tangled code
Deciding whether to create an abstraction
Choosing between inheritance and composition
Evaluating code complexity and coupling
Planning modular architectures

Core Concepts

1. KISS (Keep It Simple)

Choose the simplest solution that works. Complexity must be justified by concrete requirements.

2. Single Responsibility (SRP)

Each unit should have one reason to change. Separate concerns into focused components.

3. Composition Over Inheritance

Build behavior by combining objects, not extending classes.

4. Rule of Three

Wait until you have three instances before abstracting. Duplication is often better than premature abstraction.

Quick Start

# Simple beats clever
# Instead of a factory/registry pattern:
FORMATTERS = {"json": JsonFormatter, "csv": CsvFormatter}

def get_formatter(name: str) -> Formatter:
    return FORMATTERS[name]()

Fundamental Patterns

Pattern 1: KISS - Keep It Simple

Before adding complexity, ask: does a simpler solution work?

# Over-engineered: Factory with registration
class OutputFormatterFactory:
    _formatters: dict[str, type[Formatter]] = {}

    @classmethod
    def register(cls, name: str):
        def decorator(formatter_cls):
            cls._formatters[name] = formatter_cls
            return formatter_cls
        return decorator

    @classmethod
    def create(cls, name: str) -> Formatter:
        return cls._formatters[name]()

@OutputFormatterFactory.register("json")
class JsonFormatter(Formatter):
    ...

# Simple: Just use a dictionary
FORMATTERS = {
    "json": JsonFormatter,
    "csv": CsvFormatter,
    "xml": XmlFormatter,
}

def get_formatter(name: str) -> Formatter:
    """Get formatter by name."""
    if name not in FORMATTERS:
        raise ValueError(f"Unknown format: {name}")
    return FORMATTERS[name]()

The factory pattern adds code without adding value here. Save patterns for when they solve real problems.

Pattern 2: Single Responsibility Principle

Each class or function should have one reason to change.

# BAD: Handler does everything
class UserHandler:
    async def create_user(self, request: Request) -> Response:
        # HTTP parsing
        data = await request.json()

        # Validation
        if not data.get("email"):
            return Response({"error": "email required"}, status=400)

        # Database access
        user = await db.execute(
            "INSERT INTO users (email, name) VALUES ($1, $2) RETURNING *",
            data["email"], data["name"]
        )

        # Response formatting
        return Response({"id": user.id, "email": user.email}, status=201)

# GOOD: Separated concerns
class UserService:
    """Business logic only."""

    def __init__(self, repo: UserRepository) -> None:
        self._repo = repo

    async def create_user(self, data: CreateUserInput) -> User:
        # Only business rules here
        user = User(email=data.email, name=data.name)
        return await self._repo.save(user)

class UserHandler:
    """HTTP concerns only."""

    def __init__(self, service: UserService) -> None:
        self._service = service

    async def create_user(self, request: Request) -> Response:
        data = CreateUserInput(**(await request.json()))
        user = await self._service.create_user(data)
        return Response(user.to_dict(), status=201)

Now HTTP changes don't affect business logic, and vice versa.

Pattern 3: Separation of Concerns

Organize code into distinct layers with clear responsibilities.

┌─────────────────────────────────────────────────────┐
│  API Layer (handlers)                                │
│  - Parse requests                                    │
│  - Call services                                     │
│  - Format responses                                  │
└─────────────────────────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────┐
│  Service Layer (business logic)                      │
│  - Domain rules and validation                       │
│  - Orchestrate operations                            │
│  - Pure functions where possible                     │
└─────────────────────────────────────────────────────┘
                        │
                        ▼
┌─────────────────────────────────────────────────────┐
│  Repository Layer (data access)                      │
│  - SQL queries                                       │
│  - External API calls                                │
│  - Cache operations                                  │
└─────────────────────────────────────────────────────┘

Each layer depends only on layers below it:

# Repository: Data access
class UserRepository:
    async def get_by_id(self, user_id: str) -> User | None:
        row = await self._db.fetchrow(
            "SELECT * FROM users WHERE id = $1", user_id
        )
        return User(**row) if row else None

# Service: Business logic
class UserService:
    def __init__(self, repo: UserRepository) -> None:
        self._repo = repo

    async def get_user(self, user_id: str) -> User:
        user = await self._repo.get_by_id(user_id)
        if user is None:
            raise UserNotFoundError(user_id)
        return user

# Handler: HTTP concerns
@app.get("/users/{user_id}")
async def get_user(user_id: str) -> UserResponse:
    user = await user_service.get_user(user_id)
    return UserResponse.from_user(user)

Pattern 4: Composition Over Inheritance

Build behavior by combining objects rather than inheriting.

# Inheritance: Rigid and hard to test
class EmailNotificationService(NotificationService):
    def __init__(self):
        super().__init__()
        self._smtp = SmtpClient()  # Hard to mock

    def notify(self, user: User, message: str) -> None:
        self._smtp.send(user.email, message)

# Composition: Flexible and testable
class NotificationService:
    """Send notifications via multiple channels."""

    def __init__(
        self,
        email_sender: EmailSender,
        sms_sender: SmsSender | None = None,
        push_sender: PushSender | None = None,
    ) -> None:
        self._email = email_sender
        self._sms = sms_sender
        self._push = push_sender

    async def notify(
        self,
        user: User,
        message: str,
        channels: set[str] | None = None,
    ) -> None:
        channels = channels or {"email"}

        if "email" in channels:
            await self._email.send(user.email, message)

        if "sms" in channels and self._sms and user.phone:
            await self._sms.send(user.phone, message)

        if "push" in channels and self._push and user.device_token:
            await self._push.send(user.device_token, message)

# Easy to test with fakes
service = NotificationService(
    email_sender=FakeEmailSender(),
    sms_sender=FakeSmsSender(),
)

Advanced Patterns

Pattern 5: Rule of Three

Wait until you have three instances before abstracting.

# Two similar functions? Don't abstract yet
def process_orders(orders: list[Order]) -> list[Result]:
    results = []
    for order in orders:
        validated = validate_order(order)
        result = process_validated_order(validated)
        results.append(result)
    return results

def process_returns(returns: list[Return]) -> list[Result]:
    results = []
    for ret in returns:
        validated = validate_return(ret)
        result = process_validated_return(validated)
        results.append(result)
    return results

# These look similar, but wait! Are they actually the same?
# Different validation, different processing, different errors...
# Duplication is often better than the wrong abstraction

# Only after a third case, consider if there's a real pattern
# But even then, sometimes explicit is better than abstract

Pattern 6: Function Size Guidelines

Keep functions focused. Extract when a function:

Exceeds 20-50 lines (varies by complexity)
Serves multiple distinct purposes
Has deeply nested logic (3+ levels)

# Too long, multiple concerns mixed
def process_order(order: Order) -> Result:
    # 50 lines of validation...
    # 30 lines of inventory check...
    # 40 lines of payment processing...
    # 20 lines of notification...
    pass

# Better: Composed from focused functions
def process_order(order: Order) -> Result:
    """Process a customer order through the complete workflow."""
    validate_order(order)
    reserve_inventory(order)
    payment_result = charge_payment(order)
    send_confirmation(order, payment_result)
    return Result(success=True, order_id=order.id)

Pattern 7: Dependency Injection

Pass dependencies through constructors for testability.

from typing import Protocol

class Logger(Protocol):
    def info(self, msg: str, **kwargs) -> None: ...
    def error(self, msg: str, **kwargs) -> None: ...

class Cache(Protocol):
    async def get(self, key: str) -> str | None: ...
    async def set(self, key: str, value: str, ttl: int) -> None: ...

class UserService:
    """Service with injected dependencies."""

    def __init__(
        self,
        repository: UserRepository,
        cache: Cache,
        logger: Logger,
    ) -> None:
        self._repo = repository
        self._cache = cache
        self._logger = logger

    async def get_user(self, user_id: str) -> User:
        # Check cache first
        cached = await self._cache.get(f"user:{user_id}")
        if cached:
            self._logger.info("Cache hit", user_id=user_id)
            return User.from_json(cached)

        # Fetch from database
        user = await self._repo.get_by_id(user_id)
        if user:
            await self._cache.set(f"user:{user_id}", user.to_json(), ttl=300)

        return user

# Production
service = UserService(
    repository=PostgresUserRepository(db),
    cache=RedisCache(redis),
    logger=StructlogLogger(),
)

# Testing
service = UserService(
    repository=InMemoryUserRepository(),
    cache=FakeCache(),
    logger=NullLogger(),
)

Pattern 8: Avoiding Common Anti-Patterns

Don't expose internal types:

# BAD: Leaking ORM model to API
@app.get("/users/{id}")
def get_user(id: str) -> UserModel:  # SQLAlchemy model
    return db.query(UserModel).get(id)

# GOOD: Use response schemas
@app.get("/users/{id}")
def get_user(id: str) -> UserResponse:
    user = db.query(UserModel).get(id)
    return UserResponse.from_orm(user)

Don't mix I/O with business logic:

# BAD: SQL embedded in business logic
def calculate_discount(user_id: str) -> float:
    user = db.query("SELECT * FROM users WHERE id = ?", user_id)
    orders = db.query("SELECT * FROM orders WHERE user_id = ?", user_id)
    # Business logic mixed with data access

# GOOD: Repository pattern
def calculate_discount(user: User, order_history: list[Order]) -> float:
    # Pure business logic, easily testable
    if len(order_history) > 10:
        return 0.15
    return 0.0

Best Practices Summary

Keep it simple - Choose the simplest solution that works
Single responsibility - Each unit has one reason to change
Separate concerns - Distinct layers with clear purposes
Compose, don't inherit - Combine objects for flexibility
Rule of three - Wait before abstracting
Keep functions small - 20-50 lines (varies by complexity), one purpose
Inject dependencies - Constructor injection for testability
Delete before abstracting - Remove dead code, then consider patterns
Test each layer - Isolated tests for each concern
Explicit over clever - Readable code beats elegant code

Gotchas

Singleton via __new__ doesn't work with subclasses — each subclass gets its own instance. Use __init_subclass__ or a metaclass if subclass-aware singletons matter.
Dataclass frozen=True doesn't freeze nested mutable fields — a frozen dataclass with a list field still allows .append on that list.
Observer pattern with strong references leaks memory when subscribers go out of scope but the publisher holds them. Use weakref.WeakSet for subscribers.
functools.lru_cache on instance methods retains the instance forever (via the cache holding self) — apply to module-level functions or use methodtools.lru_cache.
Abstract base classes (ABC): @abstractmethod is enforced at instantiation, not at subclass definition — a subclass missing the abstract method silently passes type-checking until you try to construct it.

FAQ

What does the python-design-patterns skill do?

How do I install python-design-patterns?

Run npx skills add https://github.com/julianobarbosa/claude-code-skills --skill python-design-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-design-patterns/ (personal) or .claude/skills/python-design-patterns/ (project).

Which AI agents can use python-design-patterns?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is python-design-patterns safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains python-design-patterns?

It is published by julianobarbosa in julianobarbosa/claude-code-skills. It has 102 recorded installs.