dspy-ruby
This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers (OpenAI, Anthropic, Gemini, Ollama), building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Security Assessment
About dspy-ruby
The 'dspy-ruby' skill is designed for developers working with DSPy.rb, a Ruby framework focused on enabling type-safe and composable LLM (Large Language Model) applications. Unlike traditional approaches where developers manually craft prompts, DSPy.rb allows them to define LLM requirements through reusable, tested, and optimized code, enhancing the predictability and control over AI behavior. This skill provides the tools and guidance needed for building type-safe signatures, configuring multiple LLM providers, and creating sophisticated agent systems, ultimately streamlining the process of developing LLM-powered applications in Ruby.
Key capabilities of this skill include the creation of type-safe input/output contracts for LLM operations, which ensures consistent and predictable behavior. It also facilitates the construction of modular, reusable LLM components that can be combined into complex workflows. With the ability to configure multiple LLM providers like OpenAI, Anthropic, and Gemini, users can easily switch between different models as needed. Additionally, this skill supports advanced use cases such as building agent systems with tools, optimizing LLM-powered applications, and enabling testing for Ruby-based LLM solutions.
Target users for this skill include Ruby developers working on AI-powered applications who seek a structured, maintainable way to integrate LLMs into their systems. It is especially useful for those building applications with complex AI workflows, requiring predictable results and easy integration with various AI model providers.
FAQ
How do I define LLM tasks using dspy-ruby?
You define LLM tasks by creating type-safe signatures, where you specify the input and output contracts for your task. Each signature can be customized to describe the specific requirements of your LLM operation, from simple tasks like classification to more complex analysis.
Can I integrate different LLM providers in my application?
Yes, dspy-ruby allows you to configure multiple LLM providers such as OpenAI, Anthropic, Gemini, and Ollama. This flexibility makes it easier to switch between different models depending on your needs.
What is the difference between the 'Predict', 'ChainOfThought', and 'ReAct' predictors?
The 'Predict' predictor performs basic LLM inference with type-safe inputs and outputs. 'ChainOfThought' enhances accuracy by adding reasoning steps to the predictions. 'ReAct' is designed for building agent systems that can use tools and reason iteratively.
Can I build multi-step workflows with dspy-ruby?
Yes, dspy-ruby allows you to create composable modules that can be chained together to form complex workflows. Each module can handle a specific task, and the results can be passed along the pipeline to other modules.
Are there any best practices for building LLM signatures?
Yes, it's recommended to provide clear and specific descriptions, use enums for constrained outputs, include field descriptions with the 'desc:' parameter, and prefer more specific types over generic ones like String when possible.
Install dspy-ruby
Quick Setup:
- Copy the skill folder to
.claude/skills/ - Claude will automatically detect and use the skill
Repository
EveryInc/compound-engineering-plugin