guidance

Constrain LLM outputs with regex and grammars for structured JSON or XML.

Updated Apr 9, 2026
One-click install
npx skills add https://github.com/nadicodeai/argo-agent --skill guidance-nadicodeai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: guidance
Source: https://github.com/nadicodeai/argo-agent/tree/main/optional-skills/mlops/guidance
Command: npx skills add https://github.com/nadicodeai/argo-agent --skill guidance-nadicodeai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires guidance, transformers.

What problem does it solve?

LLMs often produce unstructured or invalid outputs; Guidance addresses this by enforcing syntax and structure through regex constraints and context-free grammars, guaranteeing valid JSON/XML/code formats and reliable multi-step workflows.

Core Features & Use Cases

  • Constrained generation using regex and grammars to produce structured, verifiable outputs.
  • Token healing, grammar-based generation, and grammar caching for robust results.
  • Local and API-backed backends for flexible deployments, including multi-step Pythonic workflows.

Quick Start

Use Guidance to generate a JSON payload that strictly matches a schema by applying a grammar constraint to the output.

Frequently Asked Questions about guidance

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

FAQPage Schema
How do I constrain LLM outputs to ensure valid JSON generation?▼

Constrain LLM outputs to produce valid JSON by applying regex constraints and context-free grammars during generation. This enforces deterministic syntax, guaranteeing structured data formats without parsing errors through grammar-based generation.

What is constrained generation and how does token healing work?▼

Constrained generation restricts LLM outputs to match specific grammars or regex patterns. Token healing fixes boundary artifacts during generation, ensuring outputs strictly adhere to defined context-free grammars and structured syntax constraints.

Can I use regex to validate structured output in multi-step LLM workflows?▼

Yes, you can use regex to validate structured output in multi-step LLM workflows. Guidance orchestrates Pythonic workflows applying regex constraints and grammar caching across local and cloud environments, ensuring reliable structured format validation.

Do I need the transformers library to apply grammar-based generation?▼

Yes, the transformers library is required to apply grammar-based generation. Guidance depends on it alongside its own library to enforce context-free grammars and execute constrained generation workflows in local and cloud environments.

Best way to force an LLM to generate XML instead of unstructured text?▼

The best way to force XML generation instead of unstructured text is enforcing context-free grammars. This applies constrained generation to guarantee valid XML formats, preventing invalid syntax through deterministic grammar-based output control.