Python Design Patterns

When to use this skill

Use Python Design Patterns when an AI agent needs a reusable SKILL.md workflow for this job: Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new service or component from scratch and choosing how to layer responsibilities, when refactoring a God class or monolithic function that has grown too large, when deciding whether to add a new abstraction or live with duplication, when evaluating a pull request for structural issues like tight coupling or leaking internal types, when choosing between inheritance and composition for a new class hierarchy, or when a codebase is becoming hard to test because of entangled I/O and business logic.

When not to use it

Skip Python Design Patterns when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/python-design-patterns/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/python-design-patterns/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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

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

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

## Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

## Best Practices Summary

1. **Keep it simple** - Choose the simplest solution that works 2. **Single responsibility** - Each unit has one reason to change 3. **Separate concerns** - Distinct layers with clear purposes 4. **Compose, don't inherit** - Combine objects for flexibility 5. **Rule of three** - Wait before abstracting 6. **Keep functions small** - 20-50 lines (varies by complexity), one purpose 7. **Inject dependencies** - Constructor injection for testability 8. **Delete before abstracting** - Remove dead code, then consider patterns 9. **Test each layer** - Isolated tests for each concern 10. **Explicit over clever** - Readable code beats elegant code

## Troubleshooting

**A class is growing and seems to have multiple responsibilities, but splitting it feels wrong.** Apply the "reason to change" test: list every change that could require editing this class. If the list has items from different domains (e.g., HTTP parsing AND business rules AND formatting), split it. If all changes stem from the same domain concern, the class may be appropriately sized.

**Injecting all dependencies through the constructor is producing constructors with 7+ parameters.** This is a sign of too many responsibilities in one class, not a problem with dependency injection. Split the class into smaller units first, then each constructor naturally becomes smaller.

**Composition is producing deeply nested wrapper objects that are hard to trace.** Keep the composition shallow (2-3 levels). If wrapping is the only mechanism, consider whether a Protocol-based approach or simple function composition would be cleaner than a chain of decorator objects.

**The rule of three says not to abstract yet, but the duplication is causing bugs when one copy is updated but not the other.** Duplication that diverges in dangerous ways should be abstracted sooner. The rule of three is a heuristic, not a law. If the copies are already diverging incorrectly, extract immediately and add a test that exercises the shared behavior.

**A service layer is importing from the API layer, breaking the dependency direction.** This is a layering violation. The service layer must not import from handlers. Introduce a shared types/models layer that both can import from, keeping the dependency arrow pointing downward (API → Service → Repository).

## Related Skills

- [python-testing-patterns](../python-testing-patterns/SKILL.md) — Test each layer in isolation using the dependency injection structure established here - [python-project-structure](../python-project-structure/SKILL.md) — Organize modules and directory layout so layer boundaries are explicit from the start

Intended uses

• 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