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kimrejstrom
kimrejstromCOMMUNITY
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pydantic-model

Define and validate Pydantic v2 models, configs, and event types in Python projects.

2|2|Updated Feb 12, 2025
Search Tags:#templates#pydantic#python#validation#migration#type-safety#data-models
One-click install
npx skills add https://github.com/kimrejstrom/alpacalyzer-algo-trader --skill pydantic-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic-model
Source: https://github.com/kimrejstrom/alpacalyzer-algo-trader/tree/main/.claude/skills/pydantic-model
Command: npx skills add https://github.com/kimrejstrom/alpacalyzer-algo-trader --skill pydantic-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a clear approach to creating and validating Pydantic data models, configs, and event types in Python projects, ensuring consistent, type-safe structures.

Core Features & Use Cases

  • Model placement guidance: Defines where to place models like data/models.py, strategies/config.py, events/models.py, and execution/models.py during migrations.
  • Validation best practices: Demonstrates use of BaseModel, Field, field_validator, model_validator, and ConfigDict for robust validation and serialization.
  • Migration guidance: Outlines Phase 1–3 migration patterns and templates for evolving a codebase to Pydantic v2.

Quick Start

Use this skill to design a new Pydantic v2 model by following the provided template and validators to ensure proper typing and serialization.

Frequently Asked Questions about pydantic-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure and validate Pydantic models in a multi-module Python project?▼

Migrating to Pydantic v2 follows a structured Phase 1-3 migration workflow, using provided templates to evolve your codebase. It guides the transition of existing data structures into BaseModel classes, ensuring proper typing and serialization through robust validation patterns.

How do I use field_validator and model_validator for Pydantic v2 validation?▼

Pydantic model placement separates concerns by routing data structures to specific paths like data/models.py, strategies/config.py, and events/models.py. This separation enforces type safety, maintains cross-module integrity, and streamlines testing and serialization workflows across the codebase.

What is the best way to migrate existing Python data structures to Pydantic v2?▼

Migrating to Pydantic v2 follows a structured Phase 1-3 migration workflow, using provided templates to evolve your codebase. It guides the transition of existing data structures into BaseModel classes, ensuring proper typing and serialization through robust validation patterns.

How do I use field_validator and model_validator for Pydantic v2 validation?▼

Pydantic v2 validation uses field_validator and model_validator decorators within BaseModel classes to enforce custom rules. Combined with ConfigDict and Field, these tools provide robust validation and serialization, ensuring data integrity across complex Python data structures.

Where should I place Pydantic models for execution and events in Python?▼

Pydantic model placement separates concerns by routing data structures to specific paths like data/models.py, strategies/config.py, and events/models.py. This separation enforces type safety, maintains cross-module integrity, and streamlines testing and serialization workflows across the codebase.

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