Can I use this for image processing?

Yes, it includes full support for multimodal vision tasks using the unified DSPy::Image interface for local files and URLs.

How does this skill improve AI reliability?

By implementing type-safe signatures and Chain of Thought reasoning, it ensures predictable outputs and provides structured error handling for LLM tasks.

Is it compatible with Ruby on Rails?

Absolutely. The skill provides patterns for Rails initializers, directory structures, and service-layer integration for LLM-powered features.

DSPy.rb is a Ruby framework that allows you to program LLMs through type-safe modules and signatures rather than writing manual prompts.

Which LLM providers does this skill support?

It supports a wide range of providers including OpenAI, Anthropic Claude, Google Gemini, Ollama for local models, and OpenRouter.

DSPy.rb Expert

Name: DSPy.rb Expert
Author: Folly-Partners

byFolly-Partners

Data Science & ML

Builds type-safe, composable LLM applications in Ruby by programmatically defining models, modules, and optimization workflows.

About

This skill empowers developers to leverage DSPy.rb, a Ruby framework that shifts the focus from manual prompt engineering to structured LLM programming. It provides expert guidance on creating type-safe signatures, building reusable modules, and implementing complex agentic workflows using ReAct or CodeAct. Whether you are integrating OpenAI, Anthropic, or local models via Ollama, this skill helps you implement predictable AI features, automate prompt optimization, and maintain production-grade observability in your Ruby applications.

Key Features

Automated prompt optimization and A/B testing frameworks
Composable module architecture for complex multi-step AI workflows
Agentic capabilities including ReAct and CodeAct predictor patterns
0 GitHub stars
Multi-provider support for OpenAI, Anthropic, Gemini, and Ollama
Type-safe signature creation for robust LLM input/output contracts

Use Cases

Optimizing LLM accuracy through programmatic MIPROv2 compilation
Developing structured data extraction and classification pipelines in Ruby
Building intelligent agents that utilize external tools for complex tasks

About

Key Features

Automated prompt optimization and A/B testing frameworks
Composable module architecture for complex multi-step AI workflows
Agentic capabilities including ReAct and CodeAct predictor patterns
0 GitHub stars
Multi-provider support for OpenAI, Anthropic, Gemini, and Ollama
Type-safe signature creation for robust LLM input/output contracts

Use Cases

Optimizing LLM accuracy through programmatic MIPROv2 compilation
Developing structured data extraction and classification pipelines in Ruby
Building intelligent agents that utilize external tools for complex tasks