pydantic-skill

Name: pydantic-skill
Availability: InStock
Author: Peabody

Define structured data contracts for LLM outputs using Pydantic BaseModel.

Updated Feb 11, 2026

Search Tags:#pydantic #validation #structured-output #data-model #llamaindex #nested-models

Ready to run

npx skills add https://github.com/chicagopeabodydev-sudo/library_bot_poc --skill pydantic-skill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic-skill
Source: https://github.com/chicagopeabodydev-sudo/library_bot_poc/tree/main/.cursor/skills/pydantic-skill
Command: npx skills add https://github.com/chicagopeabodydev-sudo/library_bot_poc --skill pydantic-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pydantic models provide a clean way to define and enforce structured data for LLM inputs and outputs, ensuring consistent validation and easy integration with RAG and indexing pipelines.

Core Features & Use Cases

Define strongly typed schemas using BaseModel and Field to express data contracts.
Support nested models and optional metadata for complex, real-world data.
Use with RAG/LlamaIndex for structured outputs, extraction pipelines, and robust data validation.

Quick Start

Create a Pydantic model named NodeMetadata with fields title: str, summary: str, and tags: List[str].

Frequently Asked Questions about pydantic-skill

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

FAQPage Schema

How do I define structured output schemas for LLM responses using Pydantic?▼

Define structured output schemas for LLM responses by creating a Pydantic BaseModel with explicit field types and optional Field metadata. This enforces consistent validation and handles complex data shapes using nested models for accurate extraction.

Can I use Pydantic models for data validation in LlamaIndex RAG pipelines?▼

Yes, you can use Pydantic models for data validation in LlamaIndex RAG pipelines. They provide strongly typed schemas that define data contracts, ensuring consistent validation and easy integration for extraction pipelines and robust data interpretation.

What is the best way to handle complex nested data shapes in LLM extraction tasks?▼

The best way to handle complex nested data shapes in LLM extraction tasks is using Pydantic BaseModel with support for nested models. This allows you to define precise data contracts with explicit field types and optional Field metadata for real-world data.

Do I need explicit field types when defining data contracts for AI outputs?▼

Yes, explicit field types are required when defining data contracts for AI outputs. Pydantic enforces strongly typed schemas using BaseModel and Field metadata to ensure consistent validation and accurate data interpretation in RAG and indexing pipelines.

Why does my LLM output fail validation when integrated into extraction pipelines?▼

LLM output fails validation when it does not match the structured data contract defined by your Pydantic BaseModel. Defining precise schemas with explicit field types, optional Field metadata, and nested models ensures consistent validation and accurate extraction.

When do I need structured data contracts for LLM inputs and outputs?▼

You need structured data contracts for LLM inputs and outputs when building RAG pipelines or validation tasks that require consistent schemas. Pydantic models provide strongly typed definitions to ensure accurate extraction and interpretation of complex real-world data.