Enforce structured output formats and validation constraints on language model generation.
npx skills add https://github.com/pmcdowall/hermes-skills --skill guidance-pmcdowall
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/pmcdowall/hermes-skills/tree/main/.archive/guidance Command: npx skills add https://github.com/pmcdowall/hermes-skills --skill guidance-pmcdowall
This Skill helps prevent unreliable language model outputs by enforcing formats, validating generated structures, and controlling token generation through constraints.
Use the guidance skill to create a constrained generation workflow that produces valid JSON output from a language model.
High-intent search queries and answers about installing and using this skill.
Constrained generation enforces regex constraints and grammar-based rules during token generation, forcing the LLM to output valid JSON. This guarantees schema-compliant structured data without requiring repeated correction loops.
Grammar-based generation restricts language model token selection by applying formal grammar rules and selection constraints during decoding. This mechanism guarantees the output strictly conforms to specified structured formats like JSON or XML.
Yes, regex constraints can validate structured data extraction by enforcing specific pattern rules during LLM generation. This prevents unreliable outputs by ensuring tokens strictly match required formats before generation completes.
Build reliable multi-step agent workflows by applying Pythonic generation patterns and validation constraints across each step. This approach leverages backend integration and token healing to maintain structured output integrity throughout the pipeline.
Prevent invalid API responses by enforcing structured output formats and validation constraints during generation. Applying grammar rules and regex constraints guarantees the LLM produces validated, schema-compliant responses without repeated correction loops.
Language models output invalid JSON because token generation is probabilistic and lacks inherent structural validation. Applying grammar-based generation and regex constraints forces the model to only produce tokens that form valid structured data.
ovachieverConstrain LLM outputs to valid JSON, XML, and code using regex and grammar constraints.
peteromalletConstrain LLM outputs with regex and grammars using Guidance.
ChimeraFoundationaConstrain LLM outputs with regex and grammars for valid JSON, XML, and code.
WarrenZhu050413Create and update Claude Code agent skills with SKILL.md structure.
