python-clean-architecture
Scaffold, review, and refactor Python projects with clean code and architecture. Design principles and patterns implemented the Pythonic way
Open source Repository Open in the app JSON README (API)
About
Scaffold, review, and refactor Python projects with clean code and architecture. Design principles and patterns implemented the Pythonic way — using Protocol, Callable, functools.partial, and closures instead of heavy class hierarchies. Includes a /review-architecture command, layered FastAPI project scaffolding, and reference guides for 7 design principles, 17 code quality rules, and 10 Pythonic pattern recipes. Inspired by Arjan Codes.
Details
- Kind
- Plugins
- Topic
- Developer tools
- Publisher
- mktoronto
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 8
- Forks
- 1
- Last push
- 2026-06-19T09:11:59Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
mktoronto/python-clean-architecture/python-clean-architecture
README
# python-clean-architecture — Claude Code Plugin
A [Claude Code](https://docs.anthropic.com/en/docs/claude-code) plugin that provides Clean Architecture guidance for Python/FastAPI projects. Scaffold, review, and refactor Python projects with design principles and Pythonic patterns — directly inside Claude Code.
## Attribution
The principles, patterns, and architectural approach in this plugin are inspired by and synthesized from [Arjan Codes](https://www.arjancodes.com/)' courses:
- **The Software Designer Mindset** — Seven core design principles (cohesion, coupling, abstractions, composition, creation/use separation, data-first design, simplicity)
- **Pythonic Patterns** — Classic GoF patterns reimagined for Python using Protocol, Callable, functools.partial, and closures
- **Complete Extension** — Three-layer FastAPI architecture (Routers → Operations → Database) with Protocol-based dependency injection
The specific Pythonic framing (Protocol-based DI, functional pattern progression, three-layer FastAPI architecture) originates from his teaching. This plugin distills those principles into actionable guidance for Claude Code — it is not a reproduction of course content. If you find this useful, consider supporting Arjan's work at [arjancodes.com](https://www.arjancodes.com/), [github.com/arjancodes](https://github.com/arjancodes), and [youtube.com/arjancodes](https://www.youtube.com/arjancodes).
## Installation
1. Add the marketplace:
```
/plugin marketplace add MKToronto/python-clean-architecture
```
2. Install the plugin:
```
/plugin install python-clean-architecture@python-clean-architecture
```
3. Restart Claude Code (`/exit` then `claude`) to load the plugin.
Or download the repo and test locally without installing:
```bash
git clone https://github.com/MKToronto/python-clean-architecture.git
claude --plugin-dir /path/to/python-clean-architecture
```
## Updating
```
/plugin marketplace update python-clean-architecture
```
Then restart Claude Code (`/exit` then `claude`).
## Usage
### Automatic (Skill)
The skill triggers automatically when you ask Claude Code to:
- "Scaffold a new FastAPI project"
- "Set up clean architecture"
- "Refactor to clean architecture"
- "Add a new endpoint / router / use case / repository"
- "Review my code structure"
- "Apply design patterns in Python"
- "Decouple my code"
- "Make my code testable"
Or mention: layered architecture, dependency injection, Protocol-based design, Pythonic design patterns.
### Slash Commands
```
/review-architecture Review current directory
/review-architecture src/ Review a specific directory
/review-architecture app/main.py Review a specific file
```
The review checks:
- **Architecture layers** — Three-layer separation, no layer skipping, router as composition root
- **7 design principles** — Cohesion, coupling, abstractions, composition, creation/use, data-first, simplicity
- **22 code quality rules** — Naming, nesting, types, error handling, imports, structure
- **Pythonic patterns** — All 25 patterns checked (Strategy as Callable, Factory as dict mapping, Protocol over ABC, etc.)
Findings are reported by severity (Critical / Important / Suggestions) with file/line references and fix snippets.
### All Commands
**Review & Analysis:**
```
/review-architecture [path] Full architecture review (standard or in-depth)
/review-api-design [path] Review REST API endpoints for HTTP conventions
/check-quality [path] Quick check against 22 code quality rules
/suggest-patterns [path] Recommend Pythonic design patterns for your code
/decouple [path] Find tight coupling and suggest DI improvements
```
**Refactoring:**
```
/make-pythonic [path] Refactor to Pythonic patterns (ABC→Protocol, if/elif→dict, etc.)
/extract-god-class [path] Find and split god classes into focused collaborators
```
**Learning:**
```
/learn-architecture [topic] Learn a design or architecture topic — theory + the Pythonic way + your code
```
`/learn-architecture` explains rather than edits. Pick any topic from the skill (the 7 principles, three-layer architecture, the 25 Pythonic patterns, GRASP, DDD, code smells, REST, Python craft) and get either a **Lesson** (structured walkthrough) or a **Drill** (interactive Socratic Q&A). Every topic is grounded twice: a canonical illustration from the bundled hotel-API example, plus an analysis of *your* code — whether the concept already applies, could apply, or should apply (a violation to fix). Each lesson ends with trade-offs, the "commonly confused with" comparison (Strategy vs State, Adapter vs Facade…), and self-check questions.
**Scaffolding & Testing:**
```
/scaffold-api <project-name> Generate a new FastAPI project with clean architecture
/add-endpoint <entity-name> Scaffold a new endpoint across all three layers
/scaffold-tests [path] Generate stub-based tests for existing operations
```
`/scaffold-api` generates a full project structure with Pydantic models, operations, routers, database layer, and tests. `/add-endpoint` adds a new entity to an existing project — it creates files across all three layers (router + operations + DB model + Pydantic models + tests) and wires them into `main.py`. `/scaffold-tests` generates DataInterfaceStub-based tests for existing operations without requiring a database.
## What's Inside
```text
python-clean-architecture/
├── .claude-plugin/
│ ├── plugin.json
│ └── marketplace.json
├── README.md
├── LICENSE
├── commands/
│ ├── review-architecture.md
│ ├── review-api-design.md
│ ├── check-quality.md
│ ├── suggest-patterns.md
│ ├── decouple.md
│ ├── make-pythonic.md
│ ├── extract-god-class.md
│ ├── learn-architecture.md
│ ├── scaffold-api.md
│ ├── scaffold-tests.md
│ └── add-endpoint.md
└── skills/clean-architecture/
├── SKILL.md
├── references/
│ ├── design-principles.md
│ ├── layered-architecture.md
│ ├── testable-api.md
│ ├── testing-advanced.md
│ ├── rest-api-design.md
│ ├── code-quality.md
│ ├── classes-and-dataclasses.md
│ ├── function-design.md
│ ├── data-structures.md
│ ├── error-handling.md
│ ├── monadic-error-handling.md
│ ├── types-and-type-hints.md
│ ├── project-organization.md
│ ├── context-managers.md
│ ├── decorators.md
│ ├── async-patterns.md
│ ├── pydantic-validation.md
│ ├── pattern-matching.md
│ ├── grasp-principles.md
│ ├── domain-driven-design.md
│ ├── pythonic-patterns.md
│ └── patterns/ (25 pattern files)
│ ├── strategy.md
│ ├── registry.md
│ ├── command.md
│ ├── builder.md
│ ├── repository.md
│ ├── cqrs.md
│ └── ...
└── examples/
└── fastapi-hotel-api/
├── main.py
├── models/
├── operations/
├── routers/
└── db/
```
## Key Concepts
### Three-Layer Architecture
```
Routers (API) → Operations (business logic) → Database (persistence)
```
Each layer depends only on the layer below. The router is the composition root where concrete DB implementations are injected into operations via the `DataInterface` Protocol.
### Seven Design Principles
1. High Cohesion — Single responsibility per unit
2. Low Coupling — Minimize dependencies, Law of Demeter
3. Depend on Abstractions — Protocol + Callable for DI
4. Composition over Inheritance — Never mixins, shallow hierarchies only
5. Separate Creation from Use — Dict mapping, creator functions, one composition root
6. Start with the Data — Information Expert, fix data structures first
7. Keep Things Simple — DRY, KISS, YAGNI (but avoid hasty abstractions)
### Pythonic Pattern Defaults
- Protocol over ABC (unless shared superclass state needed)
- `functools.partial` over wrapper classes
- Closures over factory class hierarchies
- `Callable` type aliases over single-method abstract classes
- Dict mapping over if/elif chains
- Readability over dogmatic functional purity
25 design patterns implemented the Pythonic way:
**Core Patterns:**
- **Strategy** — `Callable` type alias, pass functions as args
- **Abstract Factory** — Tuples of functions + `functools.partial`
- **Bridge** — `Callable` type alias replaces abstract reference
- **Command** — Functions returning undo closures
- **Pub/Sub** — Dict-based `subscribe(event, handler)` / `post_event(event, data)`
- **Registry** — `dict[str, Callable]` mapping + `**kwargs` unpacking
- **Template Method** — Free function + Protocol parameter
- **Pipeline** — `functools.reduce` for composition
- **Callback / Wrapper / Builder** — Functional patterns for event handling, interface translation, and configuration
**Extended Patterns:**
- **Value Objects** — Subclass built-in types with `__new__` validation, or frozen dataclass
- **Event Sourcing** — Immutable events, append-only EventStore, projection functions
- **CQRS** — Separate write model + read projection, projector function after writes
- **Builder** — Fluent API with `Self` return type, `.build()` returns frozen product
- **Unit of Work** — Context manager wrapping transaction: commit on success, rollback on error
- **Singleton** — Module-level instance (preferred), or metaclass with `_instances` dict
- **State** — Protocol-based state objects, context delegates to current state
- **Adapter** — Protocol interface + `functools.partial` for single-method adaptation
- **Facade** — Simplified interface class, `functools.partial` to bind dependencies
- **Retry** — `@retry` decorator with exponential backoff, fallback strategies
- **Lazy Loading** — `functools.cache`, `cached_property`, generators, `__getattr__`
- **Repository** — Protocol interface for CRUD, concrete implementations per storage backend
- **Fluent Interface** — Methods return `self` for chaining, domain-specific verbs for readability
- **Plugin Architecture** — Config-driven creation, `importlib` auto-discovery, self-registering modules
## Example Output
See the [full example review](https://mktoronto.github.io/python-clean-architecture/#example) — a color-coded `/review-architecture` run against a test bookstore-api project (15 findings with before/after code).
## Also available for
- **Codex CLI** — [python-clean-architecture-codex](https://github.com/MKToronto/python-clean-architecture-codex) (agent skill for Codex)