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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.
Passed 21 of 22 test cases with the skill, and 11 of 22 without it.
and 12 more in the full report
11 fixed10 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write a DSPy.rb configuration snippet in Ruby that sets up Anthropic Claude 3.5 Sonnet as the main language model using environment variables.
| Checked | Without skill | With skill |
|---|---|---|
| Configures LM with string model name anthropic/claude-3-5-sonnet-20241022. | Missed | Passed |
The instructions your model reads when the skill loads.
DSPy.rb is a Ruby framework that enables developers to program LLMs, not prompt them. Instead of manually crafting prompts, define application requirements through type-safe, composable modules that can be tested, optimized, and version-controlled like regular code.
This skill provides comprehensive guidance on:
Create input/output contracts for LLM operations with runtime type checking.
When to use: Defining any LLM task, from simple classification to complex analysis.
Quick reference:
class EmailClassificationSignature < DSPy::Signature
description "Classify customer support emails"
input do
const :email_subject, String
const :email_body, String
end
output do
const :category, T.enum(["Technical", "Billing", "General"])
const :priority, T.enum(["Low", "Medium", "High"])
end
end
Templates: See assets/signature-template.rb for comprehensive examples including:
test cases passed, vs 11 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.