skills.rest
LynxLabVN
LynxLabVNOFFICIAL
Explore all skills by this author →
GitHub

outlines

Generate valid JSON, XML, or code outputs using Pydantic models and local model backends.

Updated Jul 3, 2026
Search Tags:#pydantic#json#code#xml#structured generation#valid output
One-click install
npx skills add https://github.com/LynxLabVN/office-agent --skill outlines-lynxlabvn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: outlines
Source: https://github.com/LynxLabVN/office-agent/tree/main/agent-core/optional-skills/mlops/inference/outlines
Command: npx skills add https://github.com/LynxLabVN/office-agent --skill outlines-lynxlabvn

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of ensuring the validity of JSON/XML/code structures during text generation, and provides fast inference for structured generation tasks.

Core Features & Use Cases

  • Valid JSON/XML/Code Structure: Guarantees valid outputs by constraining token generation.
  • Support for Local Models: Integrates with Transformers, llama.cpp, vLLM for fast inference.
  • Pydantic Models: Allows for type-safe outputs and structured generation.
  • Use Cases: Ideal for use cases where structured, predictable outputs are needed, such as generating code snippets, filling out PDF forms, or creating machine learning models.

Quick Start

Generate a structured response to the query "What is the capital of France?" with type constraints using the outlines skill.

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 output when generating text with local language models?▼

Guaranteeing valid JSON output involves constraining token generation during inference using Pydantic models. This ensures predictable, type-safe structured text generation for tasks like data extraction or form filling without manual validation.

Can I use Pydantic models to enforce structured generation in vLLM?▼

Yes, you can use Pydantic models to enforce structured generation in vLLM. This integration supports fast inference while providing type-safe outputs and guaranteeing valid JSON, XML, or code structures.

What is the best way to generate structured XML from local models like Transformers or llama.cpp?▼

The best way to generate structured XML from local models is by constraining token generation directly. This guarantees valid XML structures and predictable outputs without relying on post-generation parsing or correction.

Does constrained text generation work with local inference engines for fast performance?▼

Yes, constrained text generation works with local inference engines. It integrates directly with Transformers, llama.cpp, and vLLM to provide fast inference while guaranteeing valid JSON, XML, or code outputs.

Why do I need structured generation for code generation and data extraction tasks?▼

You need structured generation for code generation and data extraction to ensure predictable and reliable outputs. By constraining token generation, it guarantees valid JSON, XML, or code structures, preventing formatting errors.

Related Skills

Explore More Software Engineering Skills→
matteocervellimatteocervelli
25

data-modeler

Generate Pydantic schemas with validation and nested models.

Community
Advanced
cryptopaficryptopafi

outlines

Validate generated JSON, XML, and code structures with Pydantic models.

Community
Intermediate
lenadlmlenadlm

outlines

Generate structured JSON, XML, or code from unstructured text using Pydantic models.

Community
Advanced
SethyPagnaSethyPagna

guidance

Generate structured text using Python-based Guidance syntax with regex and grammar constraints.

Community
Advanced