guidance
Category: llm-applications · License: MIT · Version: 1.0.0 · Includes scripts
Tags: Prompt Engineering, Guidance, Constrained Generation, Structured Output, JSON Validation, Grammar, Microsoft Research, Format Enforcement, Multi-Step Workflows
When to use it
Constrains LLM output during generation with Guidance (Microsoft Research), using regex, select() choices, context-free grammars, token healing, and @guidance functions, with Anthropic, OpenAI, Transformers, and llama.cpp backends. Use when you need generated text to match a regex or fixed format such as dates, emails, or IDs. Use when you need guaranteed valid JSON, XML, or code from a model. Use when building multi-step generation workflows or ReAct-style agents with Python control flow. Use when classifying text into fixed categories with select(). Use when running local models and wanting grammar constraints. Not for Pydantic validation with automatic retries; use Instructor for that.Example requests
Ask your agent in plain language, for example:- “How do I force a local Llama model to only output a date like 2024-03-15? Regex-constrained decoding please.”
- “I’m labeling clinical trial abstracts in abstracts.csv into exactly four categories (RCT, cohort, case report, review) using a llama.cpp GGUF model on my workstation. Sometimes the model returns ‘Randomized study’ or adds explanations and my pandas parsing breaks. How can I make it only ever emit one of the four labels?”
- “I want a model to produce SQL-like queries for my lab’s sample database, and the output must always parse under a specific grammar, no syntax errors ever. I also want it to first think of the table to use, then fill in the query, in several steps with Python if-statements in between. How should I build this?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole llm-applications category with /plugin install llm-applications@research-agent-skills.