🐍 Python Queen - Queen Claude Python Template

Complete Python project generator with multiple pathways - CLI tools, REST APIs, AI applications, automation scripts, and libraries.

What This Is

Python Queen is a dynamic project generator that creates complete, production-ready Python applications from scratch. It's NOT a template repository - it's a generator that builds exactly what you need based on your choices.

Available Pathways

$3

- Framework: Typer + Rich
- Use Case: Command-line applications, developer tools, scripts
- Features: Beautiful terminal UI, subcommands, progress bars, tables

$3

- Framework: FastAPI
- Use Case: Web services, microservices, backend APIs
- Features: Auto-documentation, async support, type safety, validation

$3

- Frameworks: OpenAI/Anthropic/Ollama + Pydantic AI
- Use Case: Chatbots, agents, AI-powered tools
- Features: Type-safe agents, function calling, streaming, embeddings

$3

- Framework: APScheduler
- Use Case: Cron jobs, background tasks, data pipelines
- Features: Scheduled execution, job monitoring, async tasks

$3

- Framework: Standard packaging
- Use Case: Reusable packages, PyPI distribution
- Features: Proper structure, build config, version management

Quick Start

$3

1. Setup Python Project:
``
User: /1-setup-python
Claude: What type of Python project?
User: 2 (for REST API)
Claude: Creates complete FastAPI project
`

2. Add Database (Optional):
`
User: /2-setup-database
Claude: Database type? (1=SQLite, 2=PostgreSQL)
User: 2
Claude: Adds SQLAlchemy + migrations
`

3. Add AI (Optional):
`
User: /3-setup-ai
Claude: AI Provider? (1=OpenAI, 2=Anthropic, 3=Local)
User: 1
Claude: Adds OpenAI integration
`

$3

`javascript
// Create a FastAPI project
await mcp_queen_mcp_setup_python_foundation({
project_path: "/path/to/my-api",
project_type: "api",
database_type: "postgresql"
});

// Add database
await mcp_queen_mcp_setup_python_database({
project_path: "/path/to/my-api",
database_type: "postgresql",
auth_type: "simple"
});

// Add AI capabilities
await mcp_queen_mcp_setup_python_ai({
project_path: "/path/to/my-api",
ai_provider: "openai",
vector_store: "chroma"
});
`

What Gets Created

$3


`
my-project/
├── src/
│ └── my_project/
│ ├── __init__.py
│ ├── main.py # Entry point
│ ├── core/
│ │ ├── config.py # Pydantic settings
│ │ └── logging.py # Structured logging
│ └── utils/ # Utility functions
├── tests/
│ ├── unit/
│ └── integration/
├── .env.example # Environment template
├── pyproject.toml # Dependencies & config
├── README.md # Complete documentation
└── .gitignore
`

$3

CLI Tool:
- commands/ - Typer subcommands
- models/ - Data models
- Rich tables and progress bars
- YAML config support

REST API:
- routers/ - API endpoints
- models/ - SQLAlchemy models
- middleware/ - CORS, auth
- Swagger UI at /docs

AI Application:
- agents/ - AI agent definitions
- prompts/ - Prompt templates
- tools/ - Function tools
- Provider integrations

Automation:
- jobs/ - Scheduled tasks
- services/ - Business logic
- Scheduler configuration
- Job monitoring

Library:
- Proper package structure
- __version__.py
- Build configuration
- PyPI metadata

Running Generated Projects

$3


`bash
cd my-cli-tool
uv run python -m src.my_cli_tool.main --help
`

$3

`bash
cd my-api
uv run uvicorn src.my_api.main:app --reload

API at http://localhost:8000

Docs at http://localhost:8000/docs

`

$3

`bash
cd my-ai-app
export OPENAI_API_KEY="sk-..."
uv run python -m src.my_ai_app.main
`

$3

`bash
cd my-automation
uv run python -m src.my_automation.main

Runs scheduler with configured jobs

`

$3

`bash
cd my-library
uv run pytest # Run tests
uv run python -m build # Build package
uv run twine upload dist/* # Upload to PyPI
`

Development Commands

All projects include:

`bash


Dependency management

uv add package-name # Add dependency
uv add --dev package-name # Add dev dependency
uv sync # Install all deps

Code quality

uv run ruff check . # Lint code
uv run ruff format . # Format code
uv run mypy src/ # Type checking

Testing

uv run pytest # Run tests
uv run pytest --cov # With coverage
uv run pytest -v # Verbose output

Database (if applicable)

uv run alembic revision --autogenerate -m "message" # Create migration
uv run alembic upgrade head # Apply migrations
`

Environment Configuration

Each project includes .env.example:

`bash


API Configuration

API_HOST=0.0.0.0
API_PORT=8000
DEBUG=false

Database (if added)

DATABASE_URL=postgresql://user:pass@localhost/dbname

AI Provider (if added)

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
OLLAMA_HOST=http://localhost:11434

Security (if auth enabled)

SECRET_KEY=your-secret-key
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30
``

Key Features

$3

- Projects run immediately after generation
- All dependencies installed
- Configuration ready
- Example code included

$3

- Pydantic models everywhere
- Full type hints
- Mypy configuration
- Runtime validation

$3

- Pytest configured
- Test structure created
- Fixtures included
- Coverage setup

$3

- Python 3.11+ features
- Async/await support
- UV package manager
- Ruff for linting

$3

- Structured logging
- Error handling
- Environment management
- Docker support

System Requirements

- Python 3.11 or higher
- uv package manager
- Git (for version control)

How It Works

1. You Choose: Select your project type (CLI, API, AI, etc.)
2. Generator Creates: Complete project structure with all files
3. Dependencies Install: Via uv automatically
4. Ready to Run: Immediately functional application

This is NOT a template you clone. It's a generator that creates fresh projects tailored to your specific needs.

Integration with Queen Claude

Python Queen is part of the larger Queen Claude ecosystem:

- Queen Claude: Main autonomous development system
- Python Queen: Python project generator (this)
- Next.js Queen: Next.js/React project generator
- Queen MCP: Model Context Protocol server

All work together to provide complete full-stack development capabilities.

License

MIT