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structured-output

Extract structured data from LLM responses with JSON Schema validation.

3|Updated Aug 17, 2025
Search Tags:#structured data#data extraction#API#validation#parsing#JSON Schema#LLM output
One-click install
npx skills add https://github.com/juanre/llmring --skill structured-output
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: structured-output
Source: https://github.com/juanre/llmring/tree/main/skills/structured-output
Command: npx skills add https://github.com/juanre/llmring --skill structured-output

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LLMs often produce free-form text, making it challenging to reliably extract structured data for downstream processing or database integration. This Skill ensures LLM outputs conform to a defined schema, eliminating manual parsing and validation.

Core Features & Use Cases

  • Unified JSON Schema: Define desired output structures using standard JSON Schema, automatically adapted for all major LLM providers (OpenAI, Anthropic, Google, Ollama).
  • Automatic Validation & Parsing: Receive pre-parsed Python dictionaries, guaranteed to match your schema, reducing parsing errors and development time.
  • Strict Mode Enforcement: Ensure LLM outputs strictly adhere to the schema, preventing unexpected data formats and improving data quality.
  • Use Case: Extract customer contact information (name, age, email) from unstructured text, classify sentiment with a confidence score, or generate complex nested JSON objects for API responses, all with guaranteed structural integrity.

Quick Start

Use the structured-output skill to generate a 'person' object with 'name', 'age', and 'email' fields, ensuring the output is strictly validated JSON.

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 data from LLM outputs?▼

Structured data extraction from LLM responses uses JSON Schema validation to define output format. This Skill enforces schema compliance across providers (OpenAI, Anthropic, Google, Ollama), returning pre-parsed Python dictionaries that match your schema exactly, eliminating manual parsing errors.

Can I use JSON Schema validation with multiple LLM providers?▼

Yes. This Skill adapts a single JSON Schema definition for all major LLM providers—OpenAI, Anthropic, Google Gemini, and Ollama—via the llmring API, so you write the schema once and deploy cross-provider without reformatting.

What's the best way to ensure LLM outputs conform to a specific data structure?▼

Strict mode enforcement guarantees LLM outputs adhere to your defined schema, preventing unexpected formats. The Skill validates and parses responses into structured data before returning them, ensuring data quality for downstream processing or database integration.

Do I need to manually parse and validate JSON from LLM APIs?▼

No. This Skill handles automatic validation and parsing, delivering pre-validated Python dictionaries that conform to your schema. It eliminates manual JSON parsing work and reduces development time across LLM provider integrations.

How do I generate complex nested JSON objects with guaranteed structure?▼

Define the nested structure using JSON Schema, including field types and hierarchy. The Skill applies strict validation to ensure the LLM generates output matching your schema exactly, suitable for API responses or complex data pipelines.

What happens if an LLM output doesn't match my JSON Schema?▼

Strict mode enforcement rejects non-conforming outputs, preventing malformed data from entering your pipeline. The Skill ensures all returned data matches your schema, maintaining data integrity for downstream processing.

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