What is the 'LLM-as-judge' method?

It involves using a high-capability language model to score agent outputs based on predefined dimensions like factual accuracy, completeness, and tool efficiency.

How do token budgets affect agent performance?

Research indicates that token usage accounts for approximately 80% of performance variance in browsing agents, making token budget management a critical metric for evaluation.

What should be included in an agent test set?

A robust test set should include samples stratified by complexity, ranging from simple single-tool lookups to complex, multi-step reasoning and extended research tasks.

Why is agent evaluation different from traditional software testing?

Agent systems are non-deterministic and can take multiple valid paths to a goal, requiring outcome-focused rubrics rather than strict step-by-step assertions.

AI Agent Evaluation Framework

Name: AI Agent Evaluation Framework
Author: shipshitdev

byshipshitdev

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Seguridad y Pruebas

Builds robust evaluation frameworks to test agent performance, validate context engineering, and measure system improvements over time.

This skill provides comprehensive methodologies for assessing non-deterministic agent systems, moving beyond traditional software testing to outcome-focused evaluation. It enables developers to implement multi-dimensional rubrics, utilize LLM-as-judge patterns, and stratify test sets by complexity to catch regressions and optimize token usage. By focusing on both process quality and final outcomes, it ensures agentic workflows remain reliable and efficient through continuous monitoring and systematic benchmarking across various model configurations.

Características Principales

01Multi-dimensional rubric design for accuracy, completeness, and tool efficiency

02Context engineering validation to identify performance cliffs and optimal token budgets

03LLM-as-judge implementation for scalable automated assessments

04Complexity-stratified test set generation for diverse interaction scenarios

05Continuous evaluation pipelines for production monitoring and regression testing

0610 GitHub stars

Casos de Uso

01Validating agent performance after upgrading models or modifying system prompts

02Benchmarking different context management strategies to optimize cost and quality

03Measuring the impact of new tool integrations on overall task success rates

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