Build type-safe LLM applications in Ruby with DSPy.rb.
npx skills add https://github.com/hackefeller/ghostwire --skill dspy-ruby-hackefeller
Please help me install this Agent Skill. Skill: dspy-ruby Source: https://github.com/hackefeller/ghostwire/tree/main/.github/skills/dspy-ruby Command: npx skills add https://github.com/hackefeller/ghostwire --skill dspy-ruby-hackefeller
This Skill empowers Ruby developers to build sophisticated LLM applications by providing a structured, type-safe framework that replaces manual prompt engineering with programmatic control.
Use the dspy-ruby skill to create a new type-safe signature for classifying customer support emails.
High-intent search queries and answers about installing and using this skill.
You can build type-safe LLM agents in Ruby using the DSPy.rb framework, which replaces manual prompt engineering with programmatic control. It enables defining clear input/output contracts and composable modules for predictable AI features.
The best way to structure LLM workflows in Ruby is using programmatic frameworks like DSPy.rb. It provides composable modules and type-safe signatures, replacing manual prompt engineering with code-based control for reliable LLM application development.
Yes, you can build agent systems in Ruby that work with OpenAI, Anthropic, and Gemini. The framework supports multiple LLM providers and includes ReAct and CodeAct predictors for implementing tool-using agents.
You classify and extract information from customer support emails in Ruby by defining type-safe signatures for LLM operations. This approach enables automated email classification, key information extraction, and personalized response generation.
Ruby frameworks like DSPy.rb support testing and optimizing LLM application performance. They provide automated testing and optimization techniques to improve LLM reliability and ensure predictable outputs for your AI features.
davekilleenDevelop LLM applications in Ruby using DSPy.rb with type-safe signatures.
planetaryescapeRoute user messages to optimal AI models via task classification and cost-aware tier selection.
gvkhoslaBuild type-safe LLM applications in Ruby with programmatic prompts and Sorbet signatures.
plurigridProvide structured prompts, RAG patterns, and LLM integration for AI applications.
