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structured-output

Convert prompts into schema-constrained JSON objects using Zod or Pydantic.

4|Updated Mar 10, 2026
Search Tags:#data extraction#pydantic#API integration#zod#json schema#structured output#typed responses
One-click install
npx skills add https://github.com/JigsawStack/interfaze-skills --skill structured-output-jigsawstack
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: structured-output
Source: https://github.com/JigsawStack/interfaze-skills/tree/main/skills/structured-output
Command: npx skills add https://github.com/JigsawStack/interfaze-skills --skill structured-output-jigsawstack

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Producing reliable, machine-readable outputs from a language model is difficult because responses are often inconsistent, untyped, and hard to consume downstream.

Core Features & Use Cases

  • Strict JSON / schema-constrained objects: Generate outputs that match a specific structure, using Zod (TypeScript) or Pydantic (Python) schemas.
  • Validated field extraction: Extract named fields and normalized records from unstructured text into a predictable object shape.
  • Schema-first integration: Ensure the returned result conforms to an expected schema so application code can safely parse and use it without brittle regex or manual cleanup.

Quick Start

Ask for your results in JSON (or specify the fields you want), then provide a Zod or Pydantic schema so the skill returns a validated structured object.

Frequently Asked Questions about structured-output

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

FAQPage Schema
How do I extract structured JSON from unstructured text prompts?▼

You can extract structured JSON from unstructured text by providing a user-defined Zod or Pydantic schema alongside your prompt, enabling the provider to generate validated structured objects that conform to your expected output shape.

Can I use Pydantic and Zod schemas for validated field extraction in Python and TypeScript?▼

Yes, validated field extraction works with both Python and TypeScript SDKs, allowing you to define expected record shapes using Pydantic or Zod schemas for strict, typed responses across both environments.

What is the best way to normalize unstructured data into typed records for API integration?▼

The best way to normalize unstructured data into typed records is schema-first integration, where you define a Zod or Pydantic schema to constrain the output, ensuring reliable downstream consumption without manual cleanup.

Why do I need a schema to generate structured output from language model responses?▼

You need a schema because language model responses are naturally inconsistent and untyped; providing a schema satisfies the expected output shape with validation, ensuring application code can safely parse and use the data.

Do I need to write manual regex cleanup to parse JSON from LLM prompts?▼

No, you do not need manual regex or cleanup because the structured output mechanism uses your defined schema to return strict JSON or schema-constrained objects, eliminating the need for brittle parsing logic.

Does structured output support both Python and TypeScript SDKs for typed responses?▼

Yes, structured output supports both TypeScript and Python SDKs, allowing you to implement schema-constrained structured objects and typed responses across both development environments seamlessly.

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