Summary
If you're tired of prompt engineering through trial and error, this brings a different approach to LLM apps: define what you want with Ruby types and let the framework handle optimization. It's the Ruby port of DSPy, offering type-safe signatures with Sorbet, modular components you can actually compose and test, and automatic prompt improvement based on data rather than guessing. The real value is treating LLM integration like regular software engineering instead of an art project. With 19.4K GitHub stars on the original project, it's a proven approach now available for Ruby developers who want production-ready observability and testing built in.
Install to Claude Code
npx -y skills add everyinc/compound-engineering-plugin --skill dspy-ruby --agent claude-codeInstalls into .claude/skills of the current project.
Plug Mailtrap into your AI workflow and let it handle the email.
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SKILL.md
Select a file.
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Plug Mailtrap into your AI workflow and let it handle the email.
Connect Mailtrap MCP →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →Make coding agent sessions - Searchable, Shareable, Vendor-neutral & Scored.
Try For Free →Your agent targets a perfect 10 Code Health score. Deterministic. Every commit.
Try For Free →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →belt cli automatically finds the best tools and skills for your agent. image, video, music, tts...
one prompt install →
