Program LLMs with type-safe, composable Ruby modules.
npx skills add https://github.com/jjeremycai/claudekit --skill dspy-ruby-jjeremycai
Please help me install this Agent Skill. Skill: dspy-ruby Source: https://github.com/jjeremycai/claudekit/tree/main/plugins/compound-engineering/skills/dspy-ruby Command: npx skills add https://github.com/jjeremycai/claudekit --skill dspy-ruby-jjeremycai
This Skill enables Ruby developers to program LLMs with type-safe, composable modules, reducing risk and boilerplate.
Create a simple module and signature using the assets templates, then instantiate and forward a sample input to verify type safety.
High-intent search queries and answers about installing and using this skill.
You can program LLMs in Ruby with type-safe workflows by using DSPy modules that enforce Sorbet-style types, allowing you to define precise inputs and outputs for predictable AI operations.
You can configure multiple LLM providers in Ruby by using provided templates and references to set up OpenAI, Anthropic, Gemini, and Ollama for your testing and production AI workflows.
Type-safe signatures in Ruby define precise inputs and outputs for LLM operations, reducing boilerplate and risk by ensuring predictable data structures for composable AI pipelines.
Yes, you can build composable AI pipelines in Ruby by using DSPy modules that include predictor templates like Predict, ChainOfThought, and ReAct to structure complex workflows.
Yes, this approach supports both testing and production LLM workflows by providing asset templates and references that guide implementation and enforce Sorbet-style type safety.
The best way to reduce boilerplate when coding LLM pipelines in Ruby is to use composable modules with type-safe signatures that precisely define inputs and outputs for maintainable AI workflows.
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