dspy-ruby

Provides typed-codegen-free scaffolding for LLM-powered Ruby applications.

25|15|Updated Aug 2, 2021

Search Tags:#llm #observability #modules #ruby #signatures #dspy

Ready to run

npx skills add https://github.com/JesusFilm/core --skill dspy-ruby-jesusfilm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy-ruby
Source: https://github.com/JesusFilm/core/tree/main/.claude/skills/dspy-ruby
Command: npx skills add https://github.com/JesusFilm/core --skill dspy-ruby-jesusfilm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

DSPy.rb provides a structured, typed framework for building LLM-powered Ruby applications, enabling safer prompts, predictable interfaces, and robust observability.

Core Features & Use Cases

Type-safe signatures and modules to compose LLM-driven workflows
Toolsets, agents (ReAct, ChainOfThought, CodeAct), and pluggable predictors
Observability, per-task model configuration, and multi-provider support for production-grade apps

Quick Start

Install and scaffold a DSPy.rb module, then instantiate a predictor with a signed interface to validate typed inputs and outputs.

Frequently Asked Questions about dspy-ruby

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build type-safe LLM applications in Ruby?▼

You can build type-safe LLM applications in Ruby using DSPy.rb to define structured signatures and modular predictors, ensuring predictable interfaces and typed inputs. It enforces strict typing and JSON schema generation for robust workflows.

What is the best way to structure LLM prompts in a Ruby project?▼

The best way to structure LLM prompts in Ruby is using DSPy.rb signatures, which validate typed inputs and outputs. This modular approach replaces fragile string templates with predictable, type-safe interfaces.

Can I use ReAct and ChainOfThought agents in a Ruby LLM workflow?▼

Yes, you can use ReAct, ChainOfThought, and CodeAct agents in a Ruby LLM workflow. DSPy.rb provides pluggable predictors and toolsets to compose agent-driven tasks and end-to-end pipelines.

Do I need strict typing and JSON schema generation for Ruby LLM modules?▼

Strict typing and JSON schema generation are required for Ruby LLM modules using DSPy.rb. These practices enforce predictable interfaces, validate inputs, and ensure robust testing across modular predictors.

Why does dspy-ruby require frontmatter metadata in SKILL.md?▼

dspy-ruby requires frontmatter metadata in SKILL.md to properly scaffold optional components like scripts and references. This enforces strict typing and model configuration rules for the Ruby LLM pipeline.