Which LLM providers does this skill support?

It provides comprehensive configuration for Anthropic Claude, OpenAI, Google Gemini, local Ollama models, and OpenRouter.

Is this skill suitable for Ruby on Rails applications?

Absolutely. It includes specific templates for Rails initializers and organizational patterns for placing LLM modules within the app/llm directory.

How does prompt optimization work in this skill?

It utilizes the MIPROv2 optimizer to automatically refine your LLM modules based on specific evaluation metrics and training datasets you provide.

DSPy.rb is a Ruby framework inspired by Stanford's DSPy that allows you to program LLMs through signatures and modules rather than manual prompting, ensuring type safety and predictability.

Can I use this for vision-based tasks?

Yes, it includes a unified DSPy::Image interface for processing images via file, URL, or base64 across different multimodal model providers.

DSPy.rb Expert

Name: DSPy.rb Expert
Author: jjeremycai

byjjeremycai

Ciencia de Datos y ML

Builds type-safe, composable LLM applications in Ruby using the DSPy framework to program AI behavior instead of manual prompting.

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The DSPy.rb skill empowers developers to build predictable and maintainable AI features by treating LLM operations as structured code. Instead of fragile prompt strings, this skill enables the creation of type-safe signatures and modular workflows that can be version-controlled, tested, and automatically optimized. It provides comprehensive patterns for implementing Chain-of-Thought reasoning, building ReAct agents with tools, and configuring multi-provider setups including Anthropic, OpenAI, and local Ollama. It is ideal for Ruby developers moving from experimental scripts to production-grade AI engineering.

Características Principales

Define type-safe signatures with runtime validation for LLM inputs and outputs
Implement advanced predictors including Chain of Thought, ReAct agents, and CodeAct
0 GitHub stars
Unified interface for multimodal vision support and image processing
Automatic prompt optimization and metric-based improvement using MIPROv2
Build composable modules and pipelines for complex multi-step AI workflows

Casos de Uso

Building structured data extraction pipelines from unstructured text or images in Ruby
Converting legacy prompt-based logic into optimized, repeatable Ruby modules
Creating autonomous agents that use Ruby-based tools to solve complex multi-step tasks

Acerca de

Características Principales

Define type-safe signatures with runtime validation for LLM inputs and outputs
Implement advanced predictors including Chain of Thought, ReAct agents, and CodeAct
0 GitHub stars
Unified interface for multimodal vision support and image processing
Automatic prompt optimization and metric-based improvement using MIPROv2
Build composable modules and pipelines for complex multi-step AI workflows

Casos de Uso

Building structured data extraction pipelines from unstructured text or images in Ruby
Converting legacy prompt-based logic into optimized, repeatable Ruby modules
Creating autonomous agents that use Ruby-based tools to solve complex multi-step tasks