evals

Evaluate AI agent performance with customizable scorers and multi-model comparisons.

17.4k|2.3k|Updated Sep 8, 2025
One-click install
npx skills add https://github.com/danielmiessler/LifeOS --skill evals-danielmiessler
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evals
Source: https://github.com/danielmiessler/LifeOS/tree/main/LifeOS/install/skills/Evals
Command: npx skills add https://github.com/danielmiessler/LifeOS --skill evals-danielmiessler

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @ai-sdk/anthropic, @langwatch/scenario, ai, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill evaluates AI agents based on transcripts, tool-call sequences, and multi-turn conversations. It utilizes customizable scorers and supports multi-model comparisons for capability and regression testing.

Core Features & Use Cases

  • Customizable Scoring: Apply code-based, model-based, and human graders for nuanced evaluation.
  • Multi-Model Comparison: Compare performance across multiple AI models for optimal selection.
  • Use Case: Test an AI agent's capability and consistency in different scenarios, comparing Claude, GPT-4, Gemini, and more.

Quick Start

Use the evals skill to run a multi-model comparison for the "newsletter summary" use case.

Frequently Asked Questions about evals

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

FAQPage Schema
How do I evaluate AI agent performance across multiple models like Claude and GPT-4?▼

AI agent evaluation utilizes customizable scorers to compare performance across multiple models like Claude and GPT-4. You apply code-based, model-based, and human graders to assess transcripts, tool-call sequences, and multi-turn conversations.

What is the best way to score multi-turn conversation transcripts for AI agents?▼

Scoring multi-turn conversation transcripts is best handled by applying customizable graders. You can utilize code-based, model-based, and human graders to perform nuanced assessment of tool-call sequences and conversational consistency.

Does this AI evaluation framework support model-based and human graders?▼

Yes, this AI evaluation framework supports model-based and human graders alongside code-based scoring. This combination allows for nuanced assessment of agent transcripts, tool-call sequences, and multi-turn conversations.

Can I run regression testing on AI agents using custom scoring criteria?▼

Yes, you can run regression testing on AI agents using custom scoring criteria. The framework evaluates agent consistency across different scenarios, applying code-based, model-based, and human graders for comprehensive capability testing.

Do I need a specific framework to run multi-model comparison evaluations?▼

Yes, you need Anthropic's "Demystifying Evals for AI Agents" framework for consistent execution. This provides the foundational structure required to run multi-model comparisons and evaluate AI agent performance accurately.