Enforce regex and grammar constraints on LLM text generation.
npx skills add https://github.com/icyzh/hermes-web --skill guidance-icyzh
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/icyzh/hermes-web/tree/main/optional-skills/mlops/guidance Command: npx skills add https://github.com/icyzh/hermes-web --skill guidance-icyzh
This Skill solves the challenge of unpredictable LLM output by enforcing strict syntactic and structural constraints, ensuring that generated content is always valid, formatted, and reliable.
Use the guidance skill to generate a valid JSON object containing a name and age field by applying a regex constraint to the output.
High-intent search queries and answers about installing and using this skill.
You can enforce valid JSON generation by applying regex constraints and grammars to the LLM output. This ensures the model produces syntactically correct, machine-readable JSON structures rather than unpredictable free text.
Constrained generation is the process of enforcing syntactic and structural constraints on LLM text generation. It uses selection logic and grammars to guarantee reliable outputs in specific formats like JSON, XML, or domain-specific code.
Yes, you can build complex multi-step workflows using Pythonic control flow and tool integration. This allows for stateful agentic reasoning while maintaining strict token-level control over the generated output.
Yes, constrained generation integrates with both local and remote model backends. This provides token-level control and structured multi-step reasoning workflows across different model environments.
Token boundary issues often cause unnatural text flow and bad spacing during generation. Token healing automatically fixes these issues to ensure perfect spacing and natural text continuity in the output.
The best way to validate domain-specific code formats is by using grammars and selection lists to force the model into specific output structures. This prevents formatting errors and guarantees reliable, machine-readable outputs.
pelchersConnect HuggingFace models, datasets, and spaces for ML workflows.
zhnnky329Classify parsed subquestions into standard mathematical modeling problem types.
nadicodeaiConstrain LLM outputs with regex and grammars for structured JSON or XML.
ovachieverConstrain LLM outputs to valid JSON, XML, and code using regex and grammar constraints.
