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Generate valid JSON, XML, regex-constrained, and Pydantic outputs from text generation.

Updated May 4, 2026
Search Tags:#pydantic#json schema#transformers#structured generation#vllm#regex constraints
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill outlines-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/outlines
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill outlines-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It removes the fragility of free-form LLM output when you need machine-readable results, making it easier to generate text that always matches a schema or format.

Core Features & Use Cases

  • Structured generation: Produce valid JSON, XML, regex-matched text, and type-safe Pydantic objects.
  • Local and production backends: Work with Transformers, llama.cpp, vLLM, and limited OpenAI support.
  • Use case: Extract user profiles, classify documents, generate SQL, or build reliable API payloads without post-processing cleanup.

Quick Start

Use the outlines skill to generate a valid Pydantic JSON object for a user profile from a short text prompt.

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 an LLM without post-processing cleanup?▼

You can generate valid JSON without cleanup by applying grammar-level token constraints during text generation. This enforces schema validation directly, guaranteeing machine-readable outputs that match your required format.

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

Yes, you can use Pydantic models for structured generation with vLLM. The system applies grammar-level token constraints to guarantee type-safe Pydantic objects across local and production backends like vLLM and Transformers.

How do regex constraints work for LLM structured output?▼

Regex constraints work by applying grammar-level token masking during generation, forcing the LLM to only sample tokens that match the pattern. This guarantees valid regex-constrained text without runtime overhead.

What is the best way to ensure LLM outputs match a specific schema?▼

The best way to ensure schema-matched LLM outputs is using grammar-level token constraints. This approach validates nested types and formats during generation, removing the fragility of free-form text outputs.

Does structured generation work with llama.cpp backends?▼

Yes, structured generation works with llama.cpp backends. It supports grammar-level token constraints for JSON, XML, and Pydantic-driven workflows across llama.cpp, Transformers, and vLLM environments.

Why does my LLM produce invalid JSON when extracting structured data?▼

LLMs produce invalid JSON because free-form text generation lacks format awareness. Applying schema validation and grammar-level token constraints during generation guarantees valid structured outputs for data extraction.

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