outlines

Generate JSON, XML, and code outputs validated against Pydantic schemas.

Updated Apr 21, 2026
One-click install
npx skills add https://github.com/samuelmukoti/myai-agent --skill outlines-samuelmukoti
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/samuelmukoti/myai-agent/tree/main/skills/mlops/inference/outlines
Command: npx skills add https://github.com/samuelmukoti/myai-agent --skill outlines-samuelmukoti

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill guarantees that generated JSON, XML, and code outputs conform to defined schemas, reducing downstream validation errors by combining strict structure with Pydantic-based type safety.

Core Features & Use Cases

  • Support for JSON, XML, and code generation with built-in validation against Pydantic models.
  • Local-model backends (Transformers, vLLM) for privacy, speed, and offline capability.
  • End-to-end guidance from schema to output, including error handling and reusable templates.

Quick Start

Generate a JSON object that conforms to a provided Pydantic schema.

Frequently Asked Questions about outlines

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

FAQPage Schema
How do I guarantee valid JSON generation from a local model using Pydantic schemas?

Structured generation enforces FSM-based constraints during local model inference, ensuring outputs conform strictly to defined Pydantic schemas with zero overhead. This validation happens during generation rather than post-generation.

Can I use vLLM for structured output generation with local backends?

Yes, you can use vLLM for structured output generation. The Skill supports local-model backends including vLLM and Transformers to provide privacy, speed, and offline capability for your type-safe outputs.

What is the best way to enforce XML schema validation during LLM generation?

The best way to enforce XML schema validation during LLM generation is applying grammar-based constraints. This Skill guarantees valid XML structure during generation, reducing downstream validation errors by combining strict rules with Pydantic type safety.

Does structured generation with FSM constraints add overhead to local model inference?

No, structured generation with FSM constraints does not add overhead to local model inference. The Skill applies finite state machine-based constraints to guarantee valid JSON, XML, and code outputs with zero overhead generation.

How do I generate code that conforms to specific structural constraints using a local model?

To generate code conforming to specific structural constraints using a local model, apply grammar-based generation rules. This Skill ensures valid code structure during generation while supporting local backends like Transformers for offline capability.