Enforce regex and grammar constraints for structured LLM outputs in Python.
npx skills add https://github.com/Z43L/zeus-agent --skill guidance-z43l
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/Z43L/zeus-agent/tree/main/optional-skills/mlops/guidance Command: npx skills add https://github.com/Z43L/zeus-agent --skill guidance-z43l
Guidance prevents large-language models from producing malformed or ill-formed outputs by enforcing regex and grammar constraints during generation, so you can reliably get structured results instead of relying on fragile “prompt + guess” formatting.
Ask an AI to generate a valid JSON object by specifying a grammar or regex constraints for each required field, ensuring the response is syntactically correct and immediately parseable.
High-intent search queries and answers about installing and using this skill.
You can enforce grammar-based constraints during LLM generation to guarantee valid JSON outputs. This applies structural syntax rules directly to the generation process, ensuring the model produces syntactically correct and immediately parseable JSON objects instead of malformed text.
Regex-constrained generation restricts LLM outputs to match specific regular expression patterns. It ensures generated fields match required formats like emails, IDs, dates, or numeric values, preventing malformed data and guaranteeing the response fits your exact validation rules.
Extract structured fields by applying regex or grammar constraints to LLM generation. This forces the model to output data matching your exact field patterns, ensuring reliable extraction of specific formats like emails or IDs from unstructured text.
Yes, token healing corrects tokenization boundary errors during constrained LLM generation. It maintains correct syntax boundaries and reduces failures from tokenization artifacts, ensuring generated structured outputs remain valid across token splits.
Yes, you can build multi-step agent workflows in Python using constrained generation primitives. By applying regex and CFG grammars to generation steps, you ensure each workflow phase produces syntactically valid structured outputs for reliable downstream processing.
LLMs output invalid JSON despite prompt instructions because standard generation lacks structural enforcement. Applying grammar-based constraints during generation forces the model to follow strict syntax rules, guaranteeing valid JSON instead of relying on fragile prompt formatting.
AlvaroBianoConstrain LLM outputs with regex and grammars for valid JSON, XML, or code.
devMoezConstrain LLM generation to produce valid JSON, XML, or code.
automatedigitalEnforce regex and grammar constraints during LLM inference for valid structured outputs.
ovachieverConstrain LLM outputs to valid JSON, XML, and code using regex and grammar constraints.
