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dspy-ruby

Create type-safe, modular LLM applications in Ruby with DSPy.rb.

Updated Aug 23, 2026
Search Tags:#type safety#prompt engineering#ai development#llm#ruby#dsp
One-click install
npx skills add https://github.com/greatxrider/nomanuAI --skill dspy-ruby-greatxrider
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/greatxrider/nomanuAI/tree/main/.claude/plugins/compound-engineering/2.31.1/skills/dspy-ruby
Command: npx skills add https://github.com/greatxrider/nomanuAI --skill dspy-ruby-greatxrider

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the development of large language model (LLM) applications in Ruby by introducing software engineering best practices like type safety, modularity, and testability, moving beyond manual prompt tweaking.

Core Features & Use Cases

  • Type-Safe Signatures: Define LLM inputs and outputs using Sorbet types for compile-time and runtime safety.
  • Modular Components: Compose LLM logic using Predict, ChainOfThought, ReAct agents, and custom modules.
  • Automatic Optimization: Improve prompt accuracy using data-driven techniques like MIPROv2 and GEPA.
  • Use Case: Integrate LLM capabilities into a Ruby on Rails application by defining type-safe signatures for tasks like email classification, data summarization, or agent-based tool usage.

Quick Start

Use the dspy-ruby skill to classify the sentiment of the provided text.

Frequently Asked Questions about dspy-ruby

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build type-safe LLM applications in Ruby?▼

You can build type-safe LLM applications in Ruby by defining inputs and outputs using Sorbet types, composing modular components like Predict and ChainOfThought, and applying automated prompt optimization techniques.

What is automated prompt optimization for Ruby LLM apps?▼

Automated prompt optimization uses data-driven techniques like MIPROv2 and GEPA to improve prompt accuracy programmatically, replacing manual prompt tweaking with systematic, testable optimization workflows.

Can I integrate LLM capabilities into a Ruby on Rails application?▼

Yes, you can integrate LLM capabilities into a Ruby on Rails application by defining type-safe signatures for tasks like email classification, data summarization, or agent-based tool usage.

How do I develop LLM agents with tools in Ruby?▼

You develop LLM agents with tools in Ruby by composing modular components such as ReAct agents, which facilitate programmatic prompt engineering and tool usage within a type-safe environment.

Does dspy-ruby work with various LLM providers?▼

Yes, dspy-ruby integrates with various LLM providers via RubyLLM or direct adapters, allowing you to connect your type-safe Ruby LLM applications to different model backends.

What are the limitations of manual prompt tweaking for LLM apps?▼

Manual prompt tweaking lacks type safety, modularity, and testability, making LLM apps harder to maintain; using signatures, modules, and teleprompters introduces software engineering best practices to overcome these limitations.

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