Develop type-safe Ruby AI applications with DSPy.rb and Sorbet annotations.
npx skills add https://github.com/GuitarAlchemist/ix --skill dspy-ruby-guitaralchemist
Please help me install this Agent Skill. Skill: dspy-ruby Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/dspy-ruby Command: npx skills add https://github.com/GuitarAlchemist/ix --skill dspy-ruby-guitaralchemist
This Skill provides a framework for building type-safe language model applications in Ruby, enabling developers to implement AI features, create LLM signatures and modules, and optimize prompts with programmatic approaches.
Use the dspy-ruby skill to analyze the sentiment of a given text with the following command:
sentiment = DSPy::Predict.new(SentimentAnalysis).call(text: "This is an amazing product!")
puts sentiment.sentiment # Output: positive
High-intent search queries and answers about installing and using this skill.
To build type-safe LLM applications in Ruby, use the DSPy.rb gem alongside Sorbet type annotations to define structured inputs and outputs for robust LLM interfaces.
The best way to define structured inputs and outputs for LLM integration in Ruby is by creating type-safe signatures using Sorbet, which allows the DSPy.rb framework to enforce structured data handling.
Yes, you need Sorbet to use DSPy.rb for AI development, as this Skill requires Sorbet type annotations to establish type-safe interfaces and ensure structured inputs and outputs for LLM modules.
Yes, you can optimize prompts programmatically in a Ruby AI application by leveraging data within the DSPy.rb framework to automatically improve prompts and optimize your AI features.
Modular design for LLM integration in Ruby works by composing reusable LLM logic components, allowing you to build complex AI workflows while maintaining type safety and structured data flows.
When using Sorbet type annotations with LLM features in Ruby, you must maintain strict type definitions for all inputs and outputs, which may require additional boilerplate when dynamically adjusting LLM signatures.
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