Validate and coerce Python data into typed Pydantic v2 models.
npx skills add https://github.com/GeorgeKuzora/dotfiles --skill pydantic-georgekuzora
Please help me install this Agent Skill. Skill: pydantic Source: https://github.com/GeorgeKuzora/dotfiles/tree/main/dot_config/opencode/skills/pydantic Command: npx skills add https://github.com/GeorgeKuzora/dotfiles --skill pydantic-georgekuzora
Pydantic provides a robust framework for validating and parsing complex Python data into typed models at runtime, reducing boilerplate and preventing invalid data from propagating through applications.
Create a simple Pydantic model and validate an input payload to observe automatic type coercion and error messages.
High-intent search queries and answers about installing and using this skill.
Runtime data validation in Python uses BaseModel schemas to coerce and parse input payloads into typed models, preventing invalid data from propagating through your application. This Skill supports Pydantic v2 features like model_validate and model_dump for this exact task.
Yes, Pydantic v2 integrates seamlessly with FastAPI and Django for API payload validation. It validates request and response data using BaseModel schemas, handling nested models and model_validator rules to enforce complex business logic across web apps and CLIs.
Generating JSON Schema from Python data models is handled automatically by defining BaseModel schemas with field validators. This process serializes your typed models into standard JSON Schema, providing structured configuration loading and API documentation outputs.
Serializing Python data models to JSON and dicts is performed using the model_dump method in Pydantic v2. This function transforms validated BaseModel instances into standard dictionary or JSON formats, enabling straightforward data export and API response generation.
Yes, Pydantic v2 supports nested models and custom validators for complex business rules. You can apply field_validator and model_validator decorators to BaseModel schemas to enforce specific data coercion and validation logic across nested structures.
Runtime validation for Python configuration loading is needed when parsing external data into typed models to prevent invalid configurations. Using BaseModel schemas with model_config ensures that configuration data is strictly coerced and validated before application startup.
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