dspy-ruby

Name: dspy-ruby
Availability: InStock
Author: whmathews15

Build type-safe Ruby LLM applications with DSPy signatures and modules.

1|Updated Jun 28, 2026

Search Tags:#optimization #llm #observability #modules #ruby #signatures #dspy

Ready to run

npx skills add https://github.com/whmathews15/DEX-Personal-Operating-System --skill dspy-ruby-whmathews15

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/whmathews15/DEX-Personal-Operating-System/tree/main/.claude/plugins/compound-engineering/skills/dspy-ruby
Command: npx skills add https://github.com/whmathews15/DEX-Personal-Operating-System --skill dspy-ruby-whmathews15

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It replaces brittle prompt-only workflows with a type-safe, composable way to build Ruby AI features that are easier to test, optimize, and maintain.

Core Features & Use Cases

Type-safe signatures for defining clear input and output contracts for LLM tasks.
Composable modules and agents for chaining predictors, routing logic, tool use, retries, and stateful workflows.
Provider configuration and multimodal support for OpenAI, Anthropic, Gemini, Ollama, OpenRouter, and image-based tasks.
Testing, optimization, and observability for RSpec validation, VCR-based determinism, MIPROv2 tuning, OpenTelemetry, and Langfuse tracing.
Use case: A Ruby team can turn a manual email triage flow into a monitored classification pipeline that labels messages, assigns priority, and records reasoning.

Quick Start

Use the dspy-ruby skill to design a Ruby DSPy signature and module for your task, select the right model provider, and generate a testable implementation with configuration and optimization guidance.

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 Ruby LLM applications without brittle prompt engineering?▼

To build type-safe Ruby LLM applications without brittle prompt engineering, use composable signatures and modules to define clear input and output contracts. This approach replaces manual prompting with testable, maintainable, and type-safe logic.

Can I use OpenAI, Anthropic, and Gemini providers with Ruby LLM modules?▼

Yes, you can configure multiple providers including OpenAI, Anthropic, Gemini, Ollama, and OpenRouter with Ruby LLM modules. The framework supports provider switching and multimodal inputs for image-based tasks.

How do I test and optimize LLM pipelines in a Ruby application?▼

You can test and optimize LLM pipelines in a Ruby application using RSpec validation for testing, VCR-based determinism for recording interactions, and MIPROv2 tuning for automatic prompt and module optimization.

What is the best way to add observability and tracing to Ruby LLM agents?▼

The best way to add observability and tracing to Ruby LLM agents is by integrating OpenTelemetry and Langfuse tracing. This records reasoning steps and monitors classification pipelines for stateful workflows.

How do I chain predictors and routing logic for complex AI workflows in Ruby?▼

You can chain predictors and routing logic for complex AI workflows in Ruby by using composable modules and agents. This approach supports tool use, retries, and stateful workflows for tasks like email triage.

Do I need Sorbet-style type contracts for Ruby LLM signatures?▼

Yes, Sorbet-style type contracts are required for Ruby LLM signatures to ensure type safety. Reusable templates for signatures, modules, and configuration depend on these contracts to maintain reliable application structure.