One-click install
npx skills add https://github.com/liqiongyu/lenny_skills_plus --skill ai-evals-liqiongyu
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ai-evals
Source: https://github.com/liqiongyu/lenny_skills_plus/tree/main/skills/ai-evals
Command: npx skills add https://github.com/liqiongyu/lenny_skills_plus --skill ai-evals-liqiongyu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill streamlines the creation of comprehensive evaluation plans for AI and LLM features, ensuring quality and enabling confident deployment.

Core Features & Use Cases

  • Eval PRD Generation: Define clear requirements, scope, and acceptance thresholds for AI feature evaluations.
  • Test Set & Taxonomy Creation: Develop structured golden test sets and detailed error taxonomies from observed failures.
  • Rubric & Judge Planning: Design scoring rubrics and plan for human or LLM-as-judge execution.
  • Use Case: You've developed a new AI chatbot. Use this Skill to design a complete evaluation plan, including a test set of user queries, a rubric for assessing response quality, and a plan for how to judge the responses, ensuring it meets safety and performance standards before launch.

Quick Start

Use the ai-evals skill to design evals for a customer-support reply drafting assistant, including a safety rubric and a golden set.

Frequently Asked Questions about ai-evals

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

FAQPage Schema
How do I design an LLM evaluation rubric for a new AI feature?

Design an LLM evaluation rubric by defining scoring criteria, acceptance thresholds, and a judging plan to assess AI response quality. This process structures human or LLM-as-judge execution for consistent testing.

What is an error taxonomy in AI evaluation and how does it work?

An error taxonomy in AI evaluation categorizes observed LLM failures into structured groups for analysis. It works by classifying errors to establish quality gates and guide iteration loops for feature development.

How do I create a golden test set for AI testing?

Create a golden test set by developing structured user queries and expected outputs from observed failures. This structured dataset serves as a benchmark to measure AI system accuracy and safety.

What is an error taxonomy in AI evaluation and how does it work?

An error taxonomy in AI evaluation categorizes observed LLM failures into structured groups for analysis. It works by classifying errors to establish quality gates and guide iteration loops for feature development.

Do I need a structured system description to generate an evaluation PRD?

Yes, generating an evaluation PRD requires structured input on the system under test and decision criteria. This input defines clear requirements, scope, and acceptance thresholds for AI feature evaluations.

Can I plan for LLM-as-judge execution using an evaluation pack?

Yes, you can plan LLM-as-judge execution by designing scoring rubrics and harness plans within an evaluation pack. This facilitates automated assessment of AI responses against defined quality standards.