Define and validate Pydantic v2 models with field and model validators.
npx skills add https://github.com/anicdh/agstack --skill python-schemas
Please help me install this Agent Skill. Skill: python-schemas Source: https://github.com/anicdh/agstack/tree/main/.claude/skills/python/python-schemas Command: npx skills add https://github.com/anicdh/agstack --skill python-schemas
This Skill helps developers define and validate Pydantic v2 BaseModel schemas, manage settings, and implement modern validation patterns to reduce boilerplate and ensure consistent data handling across Python applications.
Define a simple Pydantic v2 model with a few fields and validators, then instantiate it with sample data to observe automatic validation and normalization.
High-intent search queries and answers about installing and using this skill.
Define Pydantic v2 models for API request and response schemas by creating BaseModel classes with typed fields, then apply field_validator and model_validator decorators to enforce data validation and normalization automatically.
Serializing ORM objects using Pydantic v2 requires setting ConfigDict(from_attributes=True) on your BaseModel, which allows the schema to read data directly from object attributes for safe exposure and conversion.
Manage application settings with environment variables and .env files in Python by extending the Settings base class with env_file support, centralizing configuration for predictable deployment across different environments.
You can use field_validator and model_validator together in Pydantic v2 to handle complex validation, combining field-specific checks with whole-model validation logic to reduce boilerplate.
Pydantic v2 schema serialization from ORM objects fails when the model lacks ConfigDict(from_attributes=True), which is required to map object attributes to model fields during conversion.
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