Pydantic Model Scaffolder Workflow

This skill creates type-safe Pydantic models with validation, documentation, and examples.

Workflow Steps

1. Ask for model details:

   • Model name (PascalCase, e.g., NotificationPreferences, EmailTemplate)
   • Purpose (e.g., "User notification settings", "Email template data")
   • Model type: Request, Response, Database, or All

2. Ask for fields:

   • Field names and types (e.g., email_enabled: bool, push_enabled: bool, frequency: str)
   • Optional fields (mark with Optional[type])
   • Default values if any
   • Field descriptions for documentation
   • Validation rules (e.g., email format, string length, numeric ranges)

3. Generate model file:

   • Read template based on model type:
     • request_model.py.tpl for request models
     • response_model.py.tpl for response models
     • database_model.py.tpl for database models
     • complete_model.py.tpl for all model types
   • Replace placeholders:
     • {{MODEL_NAME}} - PascalCase model name
     • {{MODEL_DESCRIPTION}} - Purpose description
     • {{FIELDS}} - Field definitions with types
     • {{VALIDATORS}} - Custom validators
     • {{EXAMPLE}} - Example data for documentation
   • Write to: backend/app/models/{{model_name_snake}}_schemas.py

4. Update models init.py:

   • Read: backend/app/models/__init__.py
   • Add import statement for new model
   • Add to __all__ list
   • Write updated file

5. Generate validation patterns:

   • Add field validators for:
     • Email format validation
     • String length constraints
     • Numeric ranges
     • Enum validation
     • Custom business rules

6. Report success:

   • Show file path
   • Display generated model structure
   • Show example usage code
   • List validation rules applied

Model Types

Request Model

• Used for incoming API requests
• Strict validation
• Required fields enforced
• Examples for OpenAPI docs

Response Model

• Used for API responses
• May include computed fields
• Documentation for frontend
• Consistent structure

Database Model

• Used for Firestore/database operations
• May include timestamps
• Conversion methods (to_dict, from_dict)
• Field mapping

Example Usage

User: Create a Pydantic model for notification preferences
Assistant: I'll create a notification preferences model. Let me ask for the details...

- Model name: NotificationPreferences
- Fields:
  - email_enabled: bool (default: True)
  - push_enabled: bool (default: False)
  - sms_enabled: bool (default: False)
  - frequency: str (options: "instant", "daily", "weekly")
  - quiet_hours_start: Optional[int] (0-23)
  - quiet_hours_end: Optional[int] (0-23)

Template Variables

• {{MODEL_NAME}}: PascalCase model name
• {{MODEL_DESCRIPTION}}: Description of the model's purpose
• {{FIELDS}}: Field definitions with types and defaults
• {{VALIDATORS}}: Custom validation functions
• {{EXAMPLE}}: Example JSON for documentation
• {{IMPORTS}}: Required imports based on field types