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Use this skill when
Defining a BaseModel for an HTTP request/response body, config, or domain object
Calling .model_validate(), .model_validate_json(), .model_dump(), .model_dump_json() and handling ValidationError
Writing @field_validator or @model_validator with explicit mode='before' | 'after' | 'wrap'
Using Annotated[T, Field(...), AfterValidator(...)] to compose constraints, validators, and serializers
Modeling tagged unions with Field(discriminator='kind') or callable Discriminator + Tag
Validating non-BaseModel types (list[Item], TypedDict, dataclasses) with TypeAdapter
Generating JSON Schema for OpenAPI / Claude tool definitions via model_json_schema() or TypeAdapter.json_schema()
Loading settings/config from env, .env, or secrets via pydantic-settingsBaseSettings
Migrating Pydantic v1 code (.parse_obj, .dict(), class Config, @validator) to v2
Choosing strict vs lax validation (strict=True, Strict[...] annotation, Field(strict=True))
Do not use this skill when
Task is Zod (TypeScript runtime validation) — →zod
Task is pure Python dataclasses with no validation needed — stay in stdlib
Task is Marshmallow, attrs, or other legacy validators — suggest migrating to Pydantic, don't maintain
Task is Pydantic v1 maintenance with no v2 migration in sight — note the EOL stance; v1 references in migration-from-v1.md
Task is FastAPI request routing wiring without schema design — →fastapi
Task is general Python typing (mapped types, Protocols) with no runtime validation — →python
Purpose
Pydantic is the dominant Python runtime validation library — the bridge between Python type hints and untrusted data (HTTP bodies, env vars, LLM responses, JSON files). It powers FastAPI request/response validation, LangChain tool argument parsing, settings management, and structured LLM outputs. The Rust core (pydantic-core) makes v2 5–50× faster than v1.
Pydantic v2 is a fundamental redesign from v1: methods are prefixed model_* (.model_validate() not .parse_obj(), .model_dump() not .dict()); the inner class Config becomes a model_config = ConfigDict(...) mapping; @validator becomes @field_validator with an explicit mode=; constrained types like constr / conint are replaced by Annotated[T, Field(...)]; and BaseSettings moves to the separate pydantic-settings package. This skill owns the validation layer — the framework skill (fastapi) owns request lifecycle wiring around it.
Capabilities
BaseModel core
Subclass BaseModel, annotate fields with type hints, and Pydantic handles validation and serialization. Construct via Model(**data), Model.model_validate(data) (dict / object), or Model.model_validate_json(bytes_or_str). Serialize via .model_dump() (dict), .model_dump_json() (str), or .model_copy(update={...}). Inspect with Model.model_fields, instance.model_fields_set. Use Model.model_construct(...) only for trusted data — it skips validation. RootModel[list[Item]] for non-object root types.
Field-level: @field_validator('name', mode='before' | 'after' | 'wrap' | 'plain') — after is default and runs on coerced values; before runs on raw input; wrap wraps the inner validator with a handler. Model-level: @model_validator(mode='before' | 'after' | 'wrap') — after is an instance method returning self. The Annotated form (AfterValidator(fn), BeforeValidator(fn), WrapValidator(fn)) makes validators reusable across models. @computed_field exposes a @property as a serialized output. Access other validated fields via info.data, runtime context via info.context.
For tagged unions, Field(discriminator='kind') with Literal['kind'] discriminator fields gives O(1) dispatch and clear errors (pet.dog.barks) instead of trying each variant. For variants whose discriminator field has different names, use Discriminator(callable) paired with Annotated[Variant, Tag('name')] on each member.
class Response[T](BaseModel) (PEP 695) or Generic[T] (3.9+). Parametrize at use site: Response[User]. Generic models cache by type-argument tuple. TypeVar defaults and bounds are supported.
Model.model_json_schema() returns a draft 2020-12 schema dict; TypeAdapter(T).json_schema() does the same for non-models. mode='validation' (default) reflects accepted inputs; mode='serialization' reflects output shape. Customize via Field(json_schema_extra=...) per field or by subclassing GenerateJsonSchema. Used to feed OpenAPI specs (FastAPI auto-wires this) and LLM tool-definition payloads.
