Validate and serialize Python data models with Pydantic v2.
npx skills add https://github.com/kid-sid/claude-spellbook --skill pydantic-kid-sid
Please help me install this Agent Skill. Skill: pydantic Source: https://github.com/kid-sid/claude-spellbook/tree/main/skills/pydantic Command: npx skills add https://github.com/kid-sid/claude-spellbook --skill pydantic-kid-sid
Pydantic provides robust data validation, serialization, and settings management for Python applications, streamlining the definition and enforcement of structured data.
Instantiate a simple model and validate a data payload to see automatic parsing and error reporting.
High-intent search queries and answers about installing and using this skill.
Python data models are validated using Pydantic v2 BaseModel and Field to define schemas, enforce data contracts, and automatically parse or reject incoming payloads with detailed error reporting. This ensures structured data consistency across web application API layers.
Environment-based settings in Python microservices are managed using pydantic-settings. It centralizes configuration loading by defining BaseSettings models that parse environment variables into typed data, ensuring safe and consistent application configuration across different deployment environments.
TypeAdapter validates non-model data structures in Python by applying Pydantic v2 parsing and validation logic to standard types like dictionaries or lists. It safely coerces and validates data payloads without requiring a custom BaseModel definition.
Python data models are serialized to JSON using Pydantic v2 BaseModel serialization methods. This converts validated model instances into dictionaries or JSON strings, ensuring reliable and consistent API response formatting across web apps and microservices.
Pydantic v2 supports discriminated unions to validate complex request schemas. By combining Annotated types with Field definitions, it accurately routes and parses incoming data payloads to the correct model variant based on specific discriminator fields.
Custom validators are needed in a Python data validation pipeline to enforce business logic that standard type coercion cannot handle. Pydantic v2 allows defining custom validation rules on BaseModel fields to parse and reject invalid data safely before processing.
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