/python-project-structure
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.
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SKILL.md
python-project-structure.SKILL.mdname: 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
# 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.
# 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 exportedPattern 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.
# 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:
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.pyFile 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:
# 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 su
Read more
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
# 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.
# 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 exportedPattern 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.
# 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:
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.pyFile 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:
# 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 su
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