outlines

Name: outlines
Availability: InStock
Author: heximao

Generate structured JSON or XML using schemas, regex patterns, or Pydantic models.

Updated May 11, 2026

Search Tags:#pydantic #regex #json schema #structured generation #text generation

Ready to run

npx skills add https://github.com/heximao/agents --skill outlines-heximao

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/heximao/agents/tree/main/skill-archive/docker-hermes/mlops/inference/outlines
Command: npx skills add https://github.com/heximao/agents --skill outlines-heximao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires outlines, transformers, vllm, pydantic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill allows you to generate structured text, such as JSON, based on defined schemas, ensuring accuracy and efficiency in your outputs.

Core Features & Use Cases

JSON Schema Generation: Automatically generate JSON from Python models or predefined schemas.
Regex Text Matching: Generate text that matches specified regular expressions.
Pydantic Model Support: Use Pydantic models for type-safe and validated structured output.
Use Case: If you need to generate structured information from unstructured text, such as parsing resumes into a structured format or converting natural language descriptions into structured data, this Skill can help.

Quick Start

Generate a JSON representation of a user profile from the following text: "John Doe, 30 years old, [email protected]".

Frequently Asked Questions about outlines

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

FAQPage Schema

How do I generate valid JSON from unstructured text using a predefined schema?▼

To generate valid JSON from unstructured text, you use structured text generation with predefined JSON schemas or Pydantic models. This ensures outputs are type-safe, validated, and match the exact structure automatically.

Can I use Pydantic models for structured text generation with vLLM?▼

Yes, you can use Pydantic models for structured text generation with vLLM. The framework supports multiple backends, including vLLM, Transformers, and llama.cpp, to enforce type-safe and validated structured outputs directly.

What is the best way to constrain LLM outputs to match a specific regex pattern?▼

The best way to constrain LLM outputs to match a specific regex pattern is through structured text generation. By applying regex text matching during generation, the model produces text strictly adhering to your specified regular expressions.

Does structured text generation work with Transformers and llama.cpp backends?▼

Yes, structured text generation works with Transformers and llama.cpp backends. It supports various models and backends, allowing you to generate JSON or XML using predefined schemas, regex patterns, or Pydantic models across these environments.

When do I need structured text generation instead of standard prompt engineering?▼

You need structured text generation when parsing unstructured text into structured formats like JSON, ensuring accuracy and type safety. Standard prompt engineering cannot guarantee valid schemas or regex matching, whereas this method enforces strict structural validation.