scorable-skills
💡 Summary
Scorable Skills integrates LLM-as-a-Judge evaluators into applications for enhanced evaluation of LLM outputs.
🎯 Target Audience
🤖 AI Roast: “Powerful, but the setup might scare off the impatient.”
Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress). Run with least privilege and audit before enabling in production.
Scorable Skills
Skills for integrating and using Scorable LLM-as-a-Judge evaluators into applications with LLM interactions.
What these skills do
- scorable-integration: Guides you through integrating Scorable LLM-as-a-Judge evaluators into your codebase.
Installation
npx skills add root-signals/scorable-skills
Usage
The skill automatically activates when you mention evaluation, judges, or Scorable. It works with frameworks like LangChain, PydanticAI, Mastra, and similar agent frameworks.
Examples
Basic integration:
Help me add Scorable evaluation to my chatbot
Framework-specific:
Integrate Scorable judges into my LangChain application
Analysis and setup:
Analyze my codebase for LLM interactions and help me set up Scorable evaluation
Production deployment:
Set up production sampling for Scorable evaluation with 10% coverage
About Scorable
Scorable is a tool for creating LLM-as-a-Judge based evaluators for safeguarding applications. It generates custom evaluators (judges) that assess LLM outputs for quality, safety, and policy adherence.
Pros
- Seamless integration with existing frameworks.
- Enhances evaluation quality of LLM outputs.
- Supports multiple agent frameworks.
Cons
- Limited documentation on advanced features.
- Dependency on specific frameworks.
- May require additional setup for production.
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