agent-evaluation

Evaluate agent capabilities, tool-use trajectories, and final response quality.

Updated Jan 20, 2026

Search Tags:#benchmarking #quality-assurance #trajectory #human-in-the-loop #tool-use #evaluation-framework #agent-evaluation

Ready to run

npx skills add https://github.com/abhishekmmgn/skills --skill agent-evaluation-abhishekmmgn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/abhishekmmgn/skills/tree/main/agents/agent-evaluation
Command: npx skills add https://github.com/abhishekmmgn/skills --skill agent-evaluation-abhishekmmgn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Agent evaluation frameworks address the need to systematically assess an agent's capabilities, reasoning trajectory, and final outputs in automated testing and human-in-the-loop validation.

Core Features & Use Cases

Multi-layered evaluation: trajectory, final response, and capabilities assessment
Benchmark-driven scoring with golden datasets and autoraters
HITL calibration and A/B testing for model improvements
Enables reproducible, safety-conscious agent validation in development and production contexts

Quick Start

Run a structured agent-evaluation pass on a test run to measure trajectory quality and final response accuracy.

Frequently Asked Questions about agent-evaluation

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

FAQPage Schema

How do I evaluate agent capabilities and tool-use trajectories for automated testing?▼

You can evaluate agent performance by running a structured evaluation pass that measures trajectory quality and final response accuracy. This requires well-defined reference trajectories, evaluation metrics, and guardrails to ensure safe, reproducible assessments.

What is multi-layered agent evaluation and how does it work?▼

Multi-layered agent evaluation assesses tool-use trajectories, final responses, and capabilities using benchmark-driven scoring with golden datasets and autoraters. It provides reproducible, safety-conscious validation for agent workflows in development and production.

How do I benchmark agent performance with golden datasets and autoraters?▼

Benchmark agent performance by applying benchmark-driven scoring with golden datasets and autoraters to evaluate tool-use trajectories and final responses. This enables reproducible, safety-conscious agent validation and A/B testing for model improvements.

Can I use human-in-the-loop validation for agent benchmarking?▼

Human-in-the-loop validation is supported through HITL calibration and A/B testing for model improvements. This combines automated evaluation with human oversight to ensure safe, reproducible agent validation in development and production contexts.

What do I need to run an agent evaluation pass on a test run?▼

Running an agent evaluation pass requires well-defined reference trajectories, evaluation metrics, and guardrails. These inputs ensure safe, reproducible assessments of trajectory quality and final response accuracy across testing and benchmarking scenarios.

When should I not use automated agent evaluation frameworks?▼

Avoid automated agent evaluation when you lack well-defined reference trajectories, evaluation metrics, or guardrails. Without these prerequisites, the framework cannot ensure safe, reproducible assessments of tool-use trajectories and final response quality.