Agent Evaluation Framework Builder

Design evaluation suites for LLM agents with datasets, metrics, and CI integration.

9|1|Updated Mar 15, 2026

Search Tags:#llm #metrics #ci #evaluate #agent-testing #evaluation-framework

Ready to run

npx skills add https://github.com/Notysoty/openagentskills --skill agent-evaluation-framework-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Agent Evaluation Framework Builder
Source: https://github.com/Notysoty/openagentskills/tree/main/skills/agent-eval-framework-builder
Command: npx skills add https://github.com/Notysoty/openagentskills --skill agent-evaluation-framework-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Teams building AI agent systems often lack repeatable, objective evaluation frameworks to measure performance, reliability, and safety before production.

Core Features & Use Cases

Standardized evaluation templates for datasets, metrics, and evaluation types
LLM-as-judge setup and trajectory-based scoring for multi-step tasks
CI-ready harness to run evals in PRs across multiple agent environments and pipelines
Regression testing and baseline comparison to track improvements over time

Quick Start

Copy this file to your project's .agents/skills/agent-eval-framework-builder/SKILL.md to start designing your evaluation suite.

Frequently Asked Questions about Agent Evaluation Framework Builder

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

FAQPage Schema

How do I build a repeatable evaluation framework for LLM agents?▼

Build a repeatable LLM agent evaluation framework by defining evaluation types, constructing reproducible datasets, selecting metrics, and setting up an automated LLM-as-judge process. This provides standardized templates to measure performance, reliability, and safety before production.

How do I set up LLM-as-judge scoring for multi-step agent tasks?▼

Set up LLM-as-judge scoring by configuring automated judging processes and trajectory-based scoring mechanisms for multi-step tasks. This automates the evaluation of complex agent pipelines, ensuring objective measurement of reliability and safety across multiple agent environments.

Can I run AI agent evaluations in CI pipelines for regression testing?▼

Yes, you can run AI agent evaluations in CI pipelines using a CI-ready harness. This enables automated regression testing and baseline comparison directly in pull requests, tracking improvements over time across multiple agent environments and pipelines.

What's the best way to structure datasets for AI pipeline evaluation?▼

Structure AI pipeline evaluation datasets using standardized templates to ensure reproducibility. Define evaluation types and select specific metrics tailored to your LLM workflows, enabling consistent baseline comparison and automated trajectory scoring.

Do I need a specific testing environment to validate LLM agent metrics?▼

No specific testing environment is required, but you need reproducible datasets and selected metrics to validate LLM agent performance. The framework provides templates adaptable across multiple agent environments, integrating directly into your existing CI pipelines.