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dspy-ruby

Develop type-safe LLM features in Ruby with composable modules.

5|Updated Mar 27, 2026
Search Tags:#prompt optimization#llm#ruby#type-safe#rails#agent systems#dspy-ruby
One-click install
npx skills add https://github.com/barkleesanders/claude-code-starter --skill dspy-ruby-barkleesanders
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/barkleesanders/claude-code-starter/tree/main/skills/dspy-ruby
Command: npx skills add https://github.com/barkleesanders/claude-code-starter --skill dspy-ruby-barkleesanders

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill eliminates the fragility and maintenance burden of manual prompt engineering for Ruby LLM applications, replacing hard-to-test prompt strings with type-safe, version-controllable code modules that produce consistent, predictable AI outputs.

Core Features & Use Cases

  • Type-Safe LLM Interfaces: Define enforced input/output contracts for LLM operations using Ruby type signatures, eliminating invalid responses at runtime.
  • Composable Module Workflows: Chain simple LLM modules together to build complex pipelines, agent systems with tool access, and conditional routing logic without managing prompt state manually.
  • Multi-Provider Support: Configure and switch between OpenAI, Anthropic, Google Gemini, and local Ollama models with a single unified API, plus built-in cost optimization and fallback logic.
  • Production-Grade Tooling: Includes built-in patterns for automated prompt optimization, RSpec testing for LLM logic, OpenTelemetry/Langfuse observability, and retry/fallback error handling for production deployments.
  • Use Case Example: Build a customer support email classifier that routes tickets to the correct team, with automated tests to validate classification accuracy and optimization tools to improve performance over time.

Quick Start

Use the dspy-ruby skill to create a type-safe signature for classifying customer support emails and build a testable module that automatically routes incoming tickets to the correct support team based on their content.

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 features in Ruby without manual prompt engineering?▼

You can build type-safe LLM features in Ruby by replacing fragile prompt strings with composable, testable code modules that enforce input and output contracts, ensuring predictable AI outputs without manual prompt maintenance.

Does dspy-ruby work with Rails for production AI feature deployment?▼

dspy-ruby works with Ruby and Rails projects for production AI feature deployment, offering multi-provider LLM configuration, retry and fallback error handling, and integrated OpenTelemetry and Langfuse observability for monitoring.

Can I configure multiple LLM providers like OpenAI, Anthropic, and Google Gemini in one Ruby application?▼

You can configure multiple LLM providers including OpenAI, Anthropic, Google Gemini, and local Ollama models through a single unified API, enabling built-in cost optimization and automatic fallback logic across providers.

What's the best way to automate prompt optimization for LLM applications in Ruby?▼

The best way to automate prompt optimization for Ruby LLM applications is using the built-in MIPROv2 optimization pattern, which programmatically improves prompt performance over time alongside RSpec testing for accuracy validation.

How do I build a tool-using agent system with composable LLM modules in Ruby?▼

You build agent systems with tool access by chaining simple LLM modules together into complex pipelines with conditional routing logic, eliminating the need to manage prompt state manually while maintaining type-safe input and output contracts.

How do I test LLM classification accuracy in Ruby before deploying to production?▼

You test LLM classification accuracy in Ruby using built-in RSpec testing patterns designed specifically for LLM logic, allowing you to validate that modules like a customer support email classifier route tickets to the correct team consistently.

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