home / skills / wshobson / agents / python-project-structure
This skill helps you structure Python projects with clear module boundaries, explicit public APIs, and maintainable layouts to improve discovery and changes.
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---
name: python-project-structure
description: Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with __all__, or planning directory layouts.
---
# Python Project Structure & Module Architecture
Design well-organized Python projects with clear module boundaries, explicit public interfaces, and maintainable directory structures. Good organization makes code discoverable and changes predictable.
## When to Use This Skill
- Starting a new Python project from scratch
- Reorganizing an existing codebase for clarity
- Defining module public APIs with `__all__`
- Deciding between flat and nested directory structures
- Determining test file placement strategies
- Creating reusable library packages
## Core Concepts
### 1. Module Cohesion
Group related code that changes together. A module should have a single, clear purpose.
### 2. Explicit Interfaces
Define what's public with `__all__`. Everything not listed is an internal implementation detail.
### 3. Flat Hierarchies
Prefer shallow directory structures. Add depth only for genuine sub-domains.
### 4. Consistent Conventions
Apply naming and organization patterns uniformly across the project.
## Quick Start
```
myproject/
├── src/
│ └── myproject/
│ ├── __init__.py
│ ├── services/
│ ├── models/
│ └── api/
├── tests/
├── pyproject.toml
└── README.md
```
## Fundamental Patterns
### Pattern 1: One Concept Per File
Each file should focus on a single concept or closely related set of functions. Consider splitting when a file:
- Handles multiple unrelated responsibilities
- Grows beyond 300-500 lines (varies by complexity)
- Contains classes that change for different reasons
```python
# Good: Focused files
# user_service.py - User business logic
# user_repository.py - User data access
# user_models.py - User data structures
# Avoid: Kitchen sink files
# user.py - Contains service, repository, models, utilities...
```
### Pattern 2: Explicit Public APIs with `__all__`
Define the public interface for every module. Unlisted members are internal implementation details.
```python
# mypackage/services/__init__.py
from .user_service import UserService
from .order_service import OrderService
from .exceptions import ServiceError, ValidationError
__all__ = [
"UserService",
"OrderService",
"ServiceError",
"ValidationError",
]
# Internal helpers remain private by omission
# from .internal_helpers import _validate_input # Not exported
```
### Pattern 3: Flat Directory Structure
Prefer minimal nesting. Deep hierarchies make imports verbose and navigation difficult.
```
# Preferred: Flat structure
project/
├── api/
│ ├── routes.py
│ └── middleware.py
├── services/
│ ├── user_service.py
│ └── order_service.py
├── models/
│ ├── user.py
│ └── order.py
└── utils/
└── validation.py
# Avoid: Deep nesting
project/core/internal/services/impl/user/
```
Add sub-packages only when there's a genuine sub-domain requiring isolation.
### Pattern 4: Test File Organization
Choose one approach and apply it consistently throughout the project.
**Option A: Colocated Tests**
```
src/
├── user_service.py
├── test_user_service.py
├── order_service.py
└── test_order_service.py
```
Benefits: Tests live next to the code they verify. Easy to see coverage gaps.
**Option B: Parallel Test Directory**
```
src/
├── services/
│ ├── user_service.py
│ └── order_service.py
tests/
├── services/
│ ├── test_user_service.py
│ └── test_order_service.py
```
Benefits: Clean separation between production and test code. Standard for larger projects.
## Advanced Patterns
### Pattern 5: Package Initialization
Use `__init__.py` to provide a clean public interface for package consumers.
```python
# mypackage/__init__.py
"""MyPackage - A library for doing useful things."""
from .core import MainClass, HelperClass
from .exceptions import PackageError, ConfigError
from .config import Settings
__all__ = [
"MainClass",
"HelperClass",
"PackageError",
"ConfigError",
"Settings",
]
__version__ = "1.0.0"
```
Consumers can then import directly from the package:
```python
from mypackage import MainClass, Settings
```
### Pattern 6: Layered Architecture
Organize code by architectural layer for clear separation of concerns.
```
myapp/
├── api/ # HTTP handlers, request/response
│ ├── routes/
│ └── middleware/
├── services/ # Business logic
├── repositories/ # Data access
├── models/ # Domain entities
├── schemas/ # API schemas (Pydantic)
└── config/ # Configuration
```
Each layer should only depend on layers below it, never above.
### Pattern 7: Domain-Driven Structure
For complex applications, organize by business domain rather than technical layer.
```
ecommerce/
├── users/
│ ├── models.py
│ ├── services.py
│ ├── repository.py
│ └── api.py
├── orders/
│ ├── models.py
│ ├── services.py
│ ├── repository.py
│ └── api.py
└── shared/
├── database.py
└── exceptions.py
```
## File and Module Naming
### Conventions
- Use `snake_case` for all file and module names: `user_repository.py`
- Avoid abbreviations that obscure meaning: `user_repository.py` not `usr_repo.py`
- Match class names to file names: `UserService` in `user_service.py`
### Import Style
Use absolute imports for clarity and reliability:
```python
# Preferred: Absolute imports
from myproject.services import UserService
from myproject.models import User
# Avoid: Relative imports
from ..services import UserService
from . import models
```
Relative imports can break when modules are moved or reorganized.
## Best Practices Summary
1. **Keep files focused** - One concept per file, consider splitting at 300-500 lines (varies by complexity)
2. **Define `__all__` explicitly** - Make public interfaces clear
3. **Prefer flat structures** - Add depth only for genuine sub-domains
4. **Use absolute imports** - More reliable and clearer
5. **Be consistent** - Apply patterns uniformly across the project
6. **Match names to content** - File names should describe their purpose
7. **Separate concerns** - Keep layers distinct and dependencies flowing one direction
8. **Document your structure** - Include a README explaining the organization
This skill helps you design clear, maintainable Python project layouts, module boundaries, and public APIs. It focuses on module cohesion, explicit exports via __all__, sensible directory depth, and consistent naming. Use it to set up new projects or reorganize existing code for discoverability and predictable change impact.
The skill evaluates code organization patterns and recommends concrete structure changes: one concept per file, explicit package exports, flat hierarchies, and layered or domain-driven layouts when appropriate. It proposes test placement strategies (co-located or parallel test directories) and gives import and naming conventions to reduce friction when refactoring. Outputs are practical patterns and small examples you can apply immediately.
When should I prefer domain-driven structure over layered architecture?
Choose domain-driven layout when business domains are large and change independently; prefer layered architecture when separation by technical responsibilities simplifies onboarding and reuse.
Should every package define __all__?
For public packages and top-level modules, yes—explicit __all__ clarifies the consumer API. For small internal modules it’s optional but still useful for intent.