pydantic-models-py – Coding skill for AI agents

Pydantic Models

Create Pydantic models following the multi-model pattern for clean API contracts.

Quick Start

Use the inline model patterns below and adapt class names and fields to the actual API contract. Inspect the installed Pydantic version before selecting configuration syntax; these fragments require the imports and application types shown by your project. Do not assume a standalone template file is bundled.

Multi-Model Pattern

| Model | Purpose |

|-------|---------|

| Base | Common fields shared across models |

| Create | Request body for creation (required fields) |

| Update | Request body for updates (all optional) |

| Response | API response with all fields |

| InDB | Database document with doc_type |

camelCase Aliases

class MyModel(BaseModel):
    workspace_id: str = Field(..., alias="workspaceId")
    created_at: datetime = Field(..., alias="createdAt")
    
    class Config:
        populate_by_name = True  # Accept both snake_case and camelCase

Optional Update Fields

class MyUpdate(BaseModel):
    """All fields optional for PATCH requests."""
    name: Optional[str] = Field(None, min_length=1)
    description: Optional[str] = None

Database Document

class MyInDB(MyResponse):
    """Adds doc_type for Cosmos DB queries."""
    doc_type: str = "my_resource"

Integration Steps

1. Create models in src/backend/app/models/

2. Export from src/backend/app/models/__init__.py

3. Add corresponding TypeScript types

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.