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AmidVoshakul
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llm-structured-output

Extract validated typed data from LLM responses using schema-constrained decoding.

Updated Dec 29, 2025
Search Tags:#pydantic#schema#llm#zod#json#structured-output#data-extraction
One-click install
npx skills add https://github.com/AmidVoshakul/chatorai --skill llm-structured-output-amidvoshakul
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-structured-output
Source: https://github.com/AmidVoshakul/chatorai/tree/main/assets/skills/llm-structured-output
Command: npx skills add https://github.com/AmidVoshakul/chatorai --skill llm-structured-output-amidvoshakul

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the issue of unreliable, free-text LLM responses by enforcing strict schema conformance, ensuring that AI output is always machine-readable and ready for direct integration into your application code.

Core Features & Use Cases

  • Multi-Provider Support: Implements native structured output methods for OpenAI, Anthropic, and Google Gemini.
  • Schema Validation: Provides workflows for using Pydantic and Zod to guarantee data integrity.
  • Use Case: Use this skill to convert unstructured customer feedback into a validated JSON object containing sentiment, key topics, and purchase intent for automated database entry.

Quick Start

Ask the AI to define a Pydantic schema for your data and configure the model to return a strictly validated JSON response.

Frequently Asked Questions about llm-structured-output

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

FAQPage Schema
How do I get validated JSON structured output from an LLM?▼

You can get validated JSON structured output from an LLM by applying schema-constrained decoding with Pydantic or Zod. This enforces strict schema conformance, ensuring AI responses are reliably machine-readable and ready for direct application integration.

Does this approach for extracting typed data work with Anthropic and OpenAI?▼

Yes, extracting typed data with schema-constrained decoding works with Anthropic and OpenAI. The approach implements native structured output methods across major AI providers, including Google Gemini, to enforce strict schema validation.

What is the best way to convert unstructured text into a typed JSON object?▼

The best way to convert unstructured text into a typed JSON object is by using Pydantic or Zod schemas to constrain LLM responses. This enforces strict schema conformance, directly yielding validated data for automated database entry or pipelines.

Why do I need Pydantic or Zod for LLM data extraction?▼

You need Pydantic or Zod for LLM data extraction to guarantee data integrity and resolve unreliable free-text responses. They enforce strict schema conformance, ensuring the AI output is always validated, typed, and machine-readable for your application.

How to define a schema for LLM responses to ensure strict JSON formatting?▼

To define a schema for LLM responses and ensure strict JSON formatting, ask the AI to configure a Pydantic or Zod schema. This applies schema-constrained decoding and native API response formatting to enforce strict schema conformance across major AI providers.

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