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pydantic

Validate and serialize data with Pydantic. Use when a user asks to validate API inputs, parse JSON/env config, define data models in Python, serialize objects, or implement data validation with type hints.

#pydantic#validation#python#data#serialization
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed pydantic v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Review the open pull requests and summarize what needs attention"
  • "Generate a changelog from the last 20 commits on the main branch"

Documentation

Overview

Pydantic is a data validation library that uses Python type hints. Define a model class, and Pydantic validates inputs, coerces types, and serializes outputs automatically. Used by FastAPI, LangChain, and most modern Python frameworks.

Instructions

Step 1: Basic Models

python
# schemas.py — Data models with validation
from pydantic import BaseModel, Field, EmailStr, field_validator
from datetime import datetime

class UserCreate(BaseModel):
    name: str = Field(min_length=2, max_length=100)
    email: EmailStr
    age: int = Field(ge=13, le=120)
    role: str = Field(default="member", pattern="^(admin|member|viewer)$")

class UserResponse(BaseModel):
    id: str
    name: str
    email: str
    role: str
    created_at: datetime

    model_config = {"from_attributes": True}    # works with ORM objects

# Usage
user = UserCreate(name="Alice", email="alice@example.com", age=28)
print(user.model_dump())            # {"name": "Alice", "email": "alice@example.com", ...}
print(user.model_dump_json())       # JSON string

# Validation error
try:
    UserCreate(name="A", email="not-an-email", age=5)
except ValidationError as e:
    print(e.errors())
    # [{"type": "string_too_short", "loc": ["name"], ...}, ...]

Step 2: Custom Validators

python
from pydantic import BaseModel, field_validator, model_validator

class ProjectCreate(BaseModel):
    name: str
    slug: str
    start_date: datetime
    end_date: datetime | None = None

    @field_validator("slug")
    @classmethod
    def validate_slug(cls, v: str) -> str:
        if not v.replace("-", "").isalnum():
            raise ValueError("Slug must contain only letters, numbers, and hyphens")
        return v.lower()

    @model_validator(mode="after")
    def validate_dates(self):
        if self.end_date and self.end_date <= self.start_date:
            raise ValueError("End date must be after start date")
        return self

Step 3: Settings from Environment

python
# config.py — App configuration from env vars
from pydantic_settings import BaseSettings

class Settings(BaseSettings):
    database_url: str
    redis_url: str = "redis://localhost:6379"
    secret_key: str
    debug: bool = False
    allowed_origins: list[str] = ["http://localhost:3000"]
    max_upload_mb: int = 10

    model_config = {
        "env_file": ".env",
        "env_file_encoding": "utf-8",
    }

settings = Settings()   # auto-reads from .env and environment variables

Step 4: Discriminated Unions

python
# events.py — Polymorphic event types
from pydantic import BaseModel
from typing import Literal

class TaskCreated(BaseModel):
    type: Literal["task.created"] = "task.created"
    task_id: str
    project_id: str
    title: str

class TaskCompleted(BaseModel):
    type: Literal["task.completed"] = "task.completed"
    task_id: str
    completed_by: str
    duration_hours: float

class CommentAdded(BaseModel):
    type: Literal["comment.added"] = "comment.added"
    comment_id: str
    task_id: str
    body: str

# Discriminated union — Pydantic picks the right type based on "type" field
WebhookEvent = TaskCreated | TaskCompleted | CommentAdded

# Parse any event
event = WebhookEvent.model_validate({"type": "task.completed", "task_id": "123", ...})
# Returns TaskCompleted instance

Guidelines

  • Pydantic v2 is 5-50x faster than v1 — rewritten in Rust (pydantic-core).
  • Use Field(...) for constraints: min_length, max_length, ge, le, pattern.
  • from_attributes = True enables direct serialization of ORM objects (SQLAlchemy, Django).
  • Use pydantic-settings for type-safe configuration from environment variables.
  • Discriminated unions handle polymorphic data — Pydantic picks the right model based on a field value.

Information

Version
1.0.0
Author
terminal-skills
Category
Development
License
Apache-2.0