python-clean-architecture — Claude Code Plugin

A 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' 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, github.com/arjancodes, and youtube.com/arjancodes.

Installation

1. Add the marketplace:

/plugin marketplace add MKToronto/python-clean-architecture

1. Install the plugin:

/plugin install python-clean-architecture@python-clean-architecture

1. Restart Claude Code (/exit then claude) to load the plugin.

Or download the repo and test locally without installing:

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

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 — 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 (agent skill for Codex)