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zhuangbiaowei
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outlines

Generate structured JSON, XML, or text using Pyndantic models and regex constraints with local model support.

2|Updated Feb 4, 2026
Search Tags:#type safety#pydantic#json schema#structured generation#local models#grammar-based generation
One-click install
npx skills add https://github.com/zhuangbiaowei/smart_bot --skill outlines-zhuangbiaowei
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/zhuangbiaowei/smart_bot/tree/main/skills/outlines
Command: npx skills add https://github.com/zhuangbiaowei/smart_bot --skill outlines-zhuangbiaowei

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 guarantees that generated text adheres to specific structures like JSON, XML, or code, preventing errors and saving significant debugging time.

Core Features & Use Cases

  • Type-Safe Generation: Use Pydantic models to ensure outputs are correctly typed and structured.
  • Local Model Support: Works with Hugging Face Transformers, vLLM, and llama.cpp for flexible deployment.
  • Use Case: Automatically extract user data from free-form text into a Pydantic model, ensuring the output is always valid JSON.

Quick Start

Use the outlines skill to generate a JSON object representing a user with name, age, and email.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee LLM outputs are valid JSON and match my Pydantic model?▼

To guarantee valid JSON generation, you can constrain LLM outputs using Pydantic models and regex patterns. This ensures the generated text strictly adheres to your defined structure, preventing formatting errors and saving debugging time.

Can I use structured generation with local models like vLLM and Transformers?▼

Yes, structured generation supports local model backends including Hugging Face Transformers, vLLM, and llama.cpp. This allows for flexible local deployment while maintaining high-speed, zero-overhead inference for constrained text generation.

What is the best way to extract structured data from free-form text using LLMs?▼

The best way to extract structured data is using type-safe generation with Pydantic models. This automatically maps free-form text into structured fields, ensuring the resulting output is always correctly typed and valid JSON.

Does grammar-based generation work for formats other than JSON?▼

Yes, grammar-based generation works for formats other than JSON. It can constrain LLM outputs to valid XML, code, or specific grammars using regex, preventing structural errors across multiple text formats.

Why do I need type-safe generation for my LLM application?▼

You need type-safe generation to prevent output formatting errors and save significant debugging time. By constraining outputs to specific structures like JSON or XML, it guarantees that generated text adheres to required application schemas.

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