python-project-structure

来源声明：本技能内容源自 wshobson/agents （MIT License, Copyright (c) 2024 Seth Hobson；完整许可文本见同目录 LICENSE.txt）。 使用、修改与再分发须遵守该 MIT 许可条款并保留版权声明。

中文描述

本技能提供全栈开发相关的最佳实践和模式指导。

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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

# 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 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.

# 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.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:

# 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

能力边界

✅ 擅长处理

• 全栈开发相关的最佳实践
• 代码架构设计指导
• 性能优化建议

⚠️ 需要素材

• 具体的项目上下文
• 技术栈信息

❌ 超出范围

• 非技术领域的业务决策
• 具体的代码实现（仅提供指导）

工作流程

Step 1: 需求分析

理解项目需求和技术约束

Step 2: 架构设计

设计系统架构和组件划分

Step 3: 实现指导

提供具体的实现建议和代码模式

Step 4: 代码审查

审查代码质量和最佳实践遵循情况

Step 5: 优化建议

提供性能优化和改进建议

Gotchas

1. 过度设计

避免在简单场景中使用复杂的设计模式

2. 性能忽视

不要忽视性能优化，特别是在生产环境中

3. 安全考虑

始终考虑安全性和数据保护

4. 测试覆盖

确保有足够的测试覆盖，特别是边界情况

5. 文档维护

保持文档与代码同步更新