Generate constrained LLM outputs conforming to JSON, XML, or code formats.
npx skills add https://github.com/unclehowell/unclehowell --skill guidance-unclehowell
Please help me install this Agent Skill. Skill: guidance Source: https://github.com/unclehowell/unclehowell/tree/main/skills/mlops/inference/guidance Command: npx skills add https://github.com/unclehowell/unclehowell --skill guidance-unclehowell
Large language models often generate outputs that deviate from required formats, causing downstream parsing errors, security risks, and extra validation work.
Use Guidance to generate a JSON object containing a name and email that always conforms to proper formatting.
High-intent search queries and answers about installing and using this skill.
To constrain LLM output and generate valid JSON, you can enforce regex and grammar constraints during generation. This ensures the model adheres to exact syntactic formats, preventing parsing errors in API responses and data pipelines.
Yes, constrained generation works with local llama.cpp models, OpenAI, Anthropic, and Hugging Face Transformers. This backend flexibility allows you to apply grammar constraints and token healing across different model environments.
Token healing seamlessly joins prompts and generations to avoid spacing and token-boundary issues. It fixes LLM prompt formatting by correcting tokenization mismatches at the boundary between the prompt and the newly generated text.
You can build deterministic multi-step workflows for structured output using Pythonic context managers and reusable functions. This orchestrates pipelines to enforce regex and grammar constraints consistently across generation steps.
LLMs generate invalid XML and code syntax in automated scripts because they often deviate from required formats. Applying grammar constraints enforces exact patterns, ensuring outputs conform to specified syntactic formats for automated scripting.
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