Pydantic
AI skill sets that provides intelligence for building solid Python backends across multiple platforms and frameworks.
npx -y skills add jiatastic/open-python-skills --skill pydanticAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 7 stars7 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Pydantic models and validation. Use when: (1) Defining schemas, (2) Validating input/output, (3) Generating JSON schema.
SKILL.md
1.6 KB, as published. Nobody here has run it
pydantic
Type-driven validation and serialization using Pydantic models.
Overview
Pydantic validates data using Python type hints and provides rich serialization via model_dump() and JSON schema output.
When to Use
- Validating request/response payloads
- Normalizing untrusted input
- Generating JSON schema for docs
Quick Start
uv pip install pydantic
from pydantic import BaseModel
class User(BaseModel):
id: int
email: str
user = User(id=1, email="[email protected]")
Core Patterns
- Typed fields: strict schema definitions.
- Field validators: custom validation logic.
- Model validators: cross-field checks.
- Serialization:
model_dump()andmodel_dump_json(). - Settings: environment-driven config via
BaseSettings.
Example: field_validator
from pydantic import BaseModel, field_validator
class Model(BaseModel):
name: str
@field_validator("name")
@classmethod
def ensure_not_empty(cls, v: str):
if not v:
raise ValueError("name required")
return v
Example: model_validate + model_dump
from pydantic import BaseModel
class Model(BaseModel):
foo: int
model = Model.model_validate({"foo": 1})
print(model.model_dump())
Troubleshooting
- Coercion surprises: use strict types if needed
- Slow validators: keep them minimal
- Mutable defaults: use
default_factory