Co-Pilot / 辅助式
更新于 3 months ago

scorable-skills

Rroot-signals
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root-signals/scorable-skills
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💡 摘要

Scorable技能将LLM作为评审的评估者集成到应用程序中,以增强对LLM输出的评估。

🎯 适合人群

人工智能开发者聊天机器人创建者软件工程师数据科学家产品经理

🤖 AI 吐槽:看起来很能打,但别让配置把人劝退。

安全分析中风险

风险:Medium。建议检查:是否执行 shell/命令行指令;是否发起外网请求(SSRF/数据外发)。以最小权限运行,并在生产环境启用前审计代码与依赖。

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.

五维分析
清晰度8/10
创新性7/10
实用性9/10
完整性7/10
可维护性8/10
优缺点分析

优点

  • 与现有框架无缝集成。
  • 提高LLM输出的评估质量。
  • 支持多种代理框架。

缺点

  • 高级功能的文档有限。
  • 依赖于特定框架。
  • 生产环境可能需要额外设置。

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免责声明:本内容来源于 GitHub 开源项目,仅供展示和评分分析使用。

版权归原作者所有 root-signals.