Can I use this for complex agent workflows?

Absolutely. It supports @guidance functions and context managers to build stateful, multi-step reasoning chains and interactive tool-use agents.

What is the primary benefit of using the Guidance skill?

It ensures LLM outputs match specific formats (like JSON or regex) by filtering tokens during generation, which eliminates parsing errors and 'hallucinated' formatting.

How do I restrict the model to a list of specific choices?

You can use the select() function to force the model to choose exactly one option from a predefined list, which is ideal for classification and decision-making tasks.

Does this skill support local models?

Yes, it includes built-in support for Transformers and llama.cpp backends in addition to cloud APIs like Anthropic and OpenAI.

What is 'token healing' and why does it matter?

Token healing automatically fixes unnatural token boundaries at the prompt-generation interface, ensuring correct spacing and improving model performance by presenting natural token sequences.

LLM Guidance & Constrained Generation

Name: LLM Guidance & Constrained Generation
Author: zechenzhangAGI

byzechenzhangAGI

•

384

Ciencia de Datos y ML

Enforces structured LLM outputs using regex and grammars to guarantee valid JSON, XML, and code generation.

Acerca de

This skill integrates Microsoft Research's Guidance framework into Claude Code, providing a powerful set of tools for controlling Large Language Model behavior. It allows developers to define strict output constraints via regular expressions or context-free grammars, eliminating the risk of malformed data structures. With advanced features like token healing and stateful Pythonic workflows, it bridges the gap between raw prompting and reliable software engineering, making it indispensable for building robust AI agents and high-fidelity data extraction pipelines.

Características Principales

Compatibility with Anthropic, OpenAI, and local backends like llama.cpp
Stateful multi-step workflows with Pythonic context managers
Token healing to prevent unnatural token boundaries and spacing issues
Guaranteed JSON, XML, and structured code generation
Strict output validation using regex and context-free grammars
384 GitHub stars

Casos de Uso

Developing high-performance classification systems with fixed output categories
Extracting structured data from unstructured text into strict schema formats
Building reliable ReAct agents with deterministic tool-use and reasoning patterns

Acerca de

Características Principales

Compatibility with Anthropic, OpenAI, and local backends like llama.cpp
Stateful multi-step workflows with Pythonic context managers
Token healing to prevent unnatural token boundaries and spacing issues
Guaranteed JSON, XML, and structured code generation
Strict output validation using regex and context-free grammars
384 GitHub stars

Casos de Uso

Developing high-performance classification systems with fixed output categories
Extracting structured data from unstructured text into strict schema formats
Building reliable ReAct agents with deterministic tool-use and reasoning patterns