Can I validate nested JSON structures?

Absolutely. It provides clear patterns for defining nested models, lists of objects, and complex hierarchical data structures.

How does this improve API security?

It enforces strict input schemas to prevent injection or malformed data and uses response models to ensure sensitive database fields are never leaked to the client.

Does it handle query and path parameter validation?

Yes, it includes patterns for using FastAPI's Query and Path classes to enforce regex, numerical ranges, and length constraints on URL parameters.

Can I implement custom business logic in my models?

Yes, the skill covers both field-level and model-level validators for cross-field dependency checks and complex data normalization.

Does this skill support Pydantic v2?

Yes, the skill is specifically updated for Pydantic v2 patterns, including the use of ConfigDict, field_validator, and model_validator.

FastAPI Validation & Pydantic Master

Name: FastAPI Validation & Pydantic Master
Author: TheBushidoCollective

byTheBushidoCollective

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API Development

Streamlines FastAPI development by implementing robust Pydantic-based data validation for type-safe and secure APIs.

About

This skill provides specialized guidance for implementing FastAPI validation using Pydantic v2. It covers foundational patterns like BaseModel configuration, advanced request body validation with nested models, and precise query and path parameter constraints. It also includes patterns for custom field and model validators, specialized field types, and response model shaping to ensure sensitive data is filtered appropriately. It is essential for developers building production-grade Python APIs who prioritize data integrity, strict type-hinting, and professional software engineering standards.

Key Features

72 GitHub stars
Response model shaping for secure and consistent API outputs
Comprehensive request body and nested model validation
Precise Query and Path parameter constraints using Field
Pydantic v2 BaseModel and ConfigDict implementation
Custom field and model-level validation logic

Use Cases

Implementing domain-specific business rules via custom Pydantic validators
Building complex API request schemas with nested objects and lists
Ensuring data security by filtering sensitive fields from API responses

About

Key Features

72 GitHub stars
Response model shaping for secure and consistent API outputs
Comprehensive request body and nested model validation
Precise Query and Path parameter constraints using Field
Pydantic v2 BaseModel and ConfigDict implementation
Custom field and model-level validation logic

Use Cases

Implementing domain-specific business rules via custom Pydantic validators
Building complex API request schemas with nested objects and lists
Ensuring data security by filtering sensitive fields from API responses