guidance

Controls LLM output with regexes and grammars for validated JSON and XML generation.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/wwwillott/jobnimbus --skill guidance-wwwillott
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: guidance
Source: https://github.com/wwwillott/jobnimbus/tree/main/optional-skills/mlops/guidance
Command: npx skills add https://github.com/wwwillott/jobnimbus --skill guidance-wwwillott

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill enables precise control over LLM outputs by applying regular expressions and context-free grammars to enforce structure, validation, and format in generated content.

Core Features & Use Cases

  • Regex-based constraints for numeric, textual, and pattern-specific fields.
  • Grammar-based generation for nested JSON, XML, and domain-specific formats.
  • Token healing, multi-step workflows, and reusable guidance functions to build robust prompts.
  • Use cases include generating validated JSON, extracting structured data, and enforcing strict output formats in production prompts.

Quick Start

Generate a constrained JSON payload containing a name and email field where the email matches a basic regex.

Frequently Asked Questions about guidance

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

FAQPage Schema
How do I constrain LLM output to a specific regex pattern?

This skill applies regex constraints to enforce structure, validation, and format in generated content, guaranteeing valid results for specific fields like emails or custom textual patterns.

How do I guarantee valid JSON generation from an LLM?

Grammar-based generation enforces nested JSON, XML, and domain-specific formats by constraining the LLM during decoding. This guarantees structured, valid payloads without relying on post-generation parsing or error correction.

What is context-free grammar generation for structured output?

Context-free grammar generation controls LLM decoding by applying grammatical rules to enforce nested structures. It guarantees valid JSON, XML, and domain-specific formats during generation rather than validating after the fact.

Does constrained generation work with local and cloud LLM backends?

Yes, regex constraints and grammar-based generation are applied across local and cloud backends. This ensures robust, reusable prompts and strict output formats regardless of your deployment environment.

What is token healing in prompt engineering?

Token healing is a technique used during constrained generation to fix boundary token issues, ensuring regex and grammar constraints apply smoothly. It supports multi-step workflows and robust, reusable guidance functions.

When should I use grammar-based constraints instead of post-processing?

Use grammar-based constraints for multi-step AI workflows and complex nested formats like JSON or XML to guarantee valid results at generation time, avoiding the fragility and extra overhead of post-generation validation.