Enforce structured generation constraints on large language model outputs.
npx skills add https://github.com/Gitnapp/Skills --skill guidance-gitnapp
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/Gitnapp/Skills/tree/main/mlops/inference/guidance Command: npx skills add https://github.com/Gitnapp/Skills --skill guidance-gitnapp
This Skill solves unreliable LLM output formatting by enforcing constraints that make generated text, JSON, XML, and code conform to required structures.
Use the guidance skill to create a constrained JSON generator that extracts names, emails, and other fields from user text.
High-intent search queries and answers about installing and using this skill.
Guaranteed structured LLM output is achieved by enforcing constrained generation constraints like regex validation and grammar rules, ensuring generated JSON, XML, and code conform to required structures without retry logic.
Constrained generation in prompt engineering controls invalid LLM outputs by applying selection rules, regex, and grammar-based constraints to force valid text, JSON, or code generation during AI engineering tasks.
Build multi-step LLM workflows using Pythonic control flow and reusable generation functions to validate tool interactions, classify inputs, and create structured responses for agent workflows.
Regex validation applies to LLM outputs during data extraction by enforcing structured generation constraints, guaranteeing valid JSON records and extracted fields conform to required formats.
The best way to stop LLMs from generating invalid JSON is enforcing grammar-based constraints and token healing, which control invalid outputs by satisfying regex validation requirements and backend integration rules.
Structured generation works with supported LLM providers for agent workflows by applying backend integration constraints, enabling validated tool interactions and constrained JSON generation within custom AI data extraction services.
MarbleSodasGenerate schema-constrained JSON or typed objects from LLM prompts.
hschneQuery, filter, validate, and transform JSON data using jq.
ejolietDesign prompts, evaluate LLM outputs, and build agentic workflows.
Clay-HHKEnforce token-level regex and grammar constraints for structured LLM outputs.
