Enforce Pydantic v2 patterns with ConfigDict and modern validators.
npx skills add https://github.com/JoaquinCampo/Skills --skill pydantic-joaquincampo
Please help me install this Agent Skill. Skill: pydantic Source: https://github.com/JoaquinCampo/Skills/tree/main/pydantic Command: npx skills add https://github.com/JoaquinCampo/Skills --skill pydantic-joaquincampo
Pydantic v2 best practices and correct syntax for modern Python models, helping developers migrate from v1 patterns and write robust validation code.
Define a Pydantic v2 model using ConfigDict and validators to validate data.
High-intent search queries and answers about installing and using this skill.
To migrate Pydantic v1 validators, replace @validator with @field_validator and @root_validator with @model_validator. This ensures your data validation models use correct v2 syntax and maintain robust validation logic.
The best way to configure Pydantic v2 models is using model_config with ConfigDict. This modern pattern replaces the deprecated class Config to properly define model behavior and validation rules.
You serialize Pydantic models in v2 using model_dump for dictionary outputs and model_dump_json for JSON strings. These modern serialization methods replace older v1 dict and json approaches.
Yes, you can validate data without a model by using TypeAdapter. This allows direct validation and serialization of arbitrary Python types, providing flexible data validation across services.
You should update Python API schemas to Pydantic v2 to enforce modern best practices, leverage ConfigDict, and utilize updated serialization methods. It resolves v1 syntax confusion and improves backend data validation robustness.
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