evals

Plan, execute, and evaluate AI evals with EZVals.

1|Updated Aug 21, 2025

Search Tags:#ai-agent #datasets #targets #evaluation-framework #evals #graders

Ready to run

npx skills add https://github.com/camronh/EZVals --skill evals-camronh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: evals
Source: https://github.com/camronh/EZVals/tree/main/ezvals/skills/evals
Command: npx skills add https://github.com/camronh/EZVals --skill evals-camronh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables teams to plan, execute, and analyze evaluations of AI agents and LLMs using EZVals, turning evaluation planning into repeatable, auditable processes.

Core Features & Use Cases

Structured blueprint to scaffold evals, datasets, graders, and targets.
Guidance on running experiments, comparing runs, and reviewing results.
Library of patterns and references to accelerate evaluation workflows.

Quick Start

Initiate an evaluation planning session by outlining a target, dataset, and grader and then wire them into EZVals for execution.

Frequently Asked Questions about evals

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

FAQPage Schema

How do I plan and run evaluations for AI agents and LLMs?▼

To plan and run AI agent evaluations, define your target, dataset, and grader, then wire them into an evaluation framework like EZVals for execution and result comparison across runs.

What is an evaluation framework and when do I need graders and datasets?▼

An evaluation framework structures AI agent testing by pairing datasets of test cases with graders that score outputs. You need this process to turn LLM evaluation into repeatable, auditable workflows.

How do I compare results across multiple AI evaluation runs?▼

Comparing results across multiple AI evaluation runs involves executing experiments with defined targets and datasets, then analyzing the scored outputs side-by-side to review performance differences and patterns.

What's the best way to structure an LLM evaluation blueprint?▼

The best way to structure an LLM evaluation blueprint is to scaffold your evals, datasets, graders, and targets using structured metadata like YAML frontmatter, ensuring the process is repeatable and auditable.

Do I need any specific dependencies to execute AI evals?▼

No specific dependencies are required to execute AI evals. The evaluation framework operates independently, allowing you to plan, wrap targets, select datasets, and apply graders without external package constraints.

Can I use custom scripts and references within my AI evaluation workflow?▼

Yes, you can use custom scripts and references within your AI evaluation workflow. The framework supports optional scripts, references, and assets alongside the required structured metadata in the skill definition.