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zeta-pydantic

Generate Pydantic v2 models with financial validation for backend request and response schemas.

Updated Aug 27, 2026
Search Tags:#pydantic#python#validation#data modeling#financial#api schema
One-click install
npx skills add https://github.com/russiankendricklamar/zetaterminal --skill zeta-pydantic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: zeta-pydantic
Source: https://github.com/russiankendricklamar/zetaterminal/tree/main/.claude/skills/zeta-pydantic
Command: npx skills add https://github.com/russiankendricklamar/zetaterminal --skill zeta-pydantic

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines the creation and validation of Pydantic models for financial applications, ensuring data integrity and adherence to business rules.

Core Features & Use Cases

  • Financial Data Modeling: Define robust Pydantic models for request and response schemas in financial backends.
  • Custom Validation: Implement complex validation rules, including range checks, list size limits, and custom logic.
  • Use Case: When developing a new API endpoint for bond pricing, use this Skill to define the input parameters with strict validation for spot price, strike, and maturity, and to structure the output response with calculated Greeks.

Quick Start

Use the zeta-pydantic skill to create a Pydantic model for a bond pricing request with specified validation rules.

Frequently Asked Questions about zeta-pydantic

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

FAQPage Schema
How do I use Pydantic for financial data validation in Python?▼

You use Pydantic for financial data validation by defining models with Field validators to enforce constraints on monetary values, time horizons, and list sizes for backend service schemas.

Can I define custom validation rules for monetary values in Pydantic v2?▼

Yes, you can define custom validation rules for monetary values in Pydantic v2 by implementing custom logic and using enum-like string patterns within a FinancialBaseModel to ensure strict data integrity.

What is the best way to structure API request and response schemas for bond pricing?▼

The best way to structure API schemas for bond pricing is using Pydantic models to define input parameters with strict validation for spot price, strike, and maturity, and structure output responses with calculated Greeks.

How do I enforce range checks and list size limits in a Python data model?▼

You enforce range checks and list size limits in a Python data model by applying Pydantic Field validators to constrain monetary values and limit list sizes, ensuring adherence to business rules.

Does this approach support enum-like string patterns for precise data definition?▼

Yes, this Pydantic modeling approach supports enum-like string patterns to achieve precise data definition and enforce strict validation logic for financial backend services.

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