Why do agents need special evaluation compared to standard LLMs?

Agents are non-deterministic and can take multiple valid paths to a goal; traditional unit tests fail to capture their dynamic decision-making and tool usage patterns.

How does LLM-as-judge work within this skill?

It utilizes a high-capability model to grade the output of an agent against a specific rubric and ground truth, providing scalable and consistent qualitative feedback.

What dimensions should be included in an agent rubric?

Effective rubrics include factual accuracy, completeness, citation precision, source quality, and tool efficiency to ensure a holistic view of agent performance.

What is the '95% Finding' in agent evaluation?

Research shows that token usage, number of tool calls, and model choice explain 95% of performance variance, making these the critical metrics to track in your framework.

Can I use this for production monitoring?

Yes, the framework supports continuous evaluation by sampling production interactions and running them through automated scoring pipelines and dashboards.

Agent System Evaluation

Name: Agent System Evaluation
Author: shipshitdev

byshipshitdev

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安全与测试

Builds robust evaluation frameworks and multi-dimensional rubrics to measure the performance, accuracy, and efficiency of AI agent systems.

This skill provides a comprehensive framework for assessing non-deterministic agent behaviors through outcome-focused evaluation methodologies. It enables developers to implement LLM-as-judge patterns, design complex test sets stratified by difficulty, and validate context engineering choices using empirical data. By focusing on multi-dimensional rubrics—including factual accuracy, tool efficiency, and token usage—this skill ensures that agentic workflows remain reliable, catch regressions before deployment, and optimize performance across varying model configurations.

主要功能

01LLM-as-judge implementation for scalable, automated performance grading

02Complexity-stratified test set generation for simple to research-level tasks

03Token budget and tool-call optimization analysis based on performance research

04Multi-dimensional rubric design for accuracy, completeness, and tool efficiency

05Context engineering validation to measure the impact of prompts and history

0610 GitHub stars

使用场景

01Comparative analysis of different agent architectures using standardized metrics

02Validating agent performance improvements after upgrading models or modifying prompt templates

03Building automated quality gates in CI/CD pipelines to prevent agent logic regressions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add shipshitdev/library evaluation

For use in Claude.ai and ChatGPT

Download Skill