What is behavioral contract testing for agents?

It defines and tests agent behavioral invariants—rules or constraints the agent must always follow regardless of the specific task output.

How can I prevent my agent from 'gaming' its evaluation metrics?

The skill provides patterns for multi-dimensional evaluation, ensuring the agent is optimized for the actual task outcome rather than a single, narrow metric.

Why is agent evaluation different from traditional software testing?

LLM agents are non-deterministic, meaning the same input can yield different outputs. Evaluation requires statistical distribution analysis and behavioral invariants rather than simple string matching.

How does this skill handle flaky agent tests?

It implements statistical test evaluation patterns, running tests multiple times to analyze result distributions and distinguish between true failures and expected variance.

Agent Evaluation & Benchmarking

Name: Agent Evaluation & Benchmarking
Author: claudiodearaujo

byclaudiodearaujo

0•

보안 및 테스팅

Evaluates and benchmarks LLM agents using behavioral testing, reliability metrics, and adversarial assessments to ensure production readiness.

This skill empowers developers to bridge the gap between high benchmark scores and real-world agent performance by implementing robust evaluation frameworks. It provides domain-specific guidance on statistical test evaluation, behavioral contract testing, and adversarial strategies to identify regressions and non-deterministic failures. By moving beyond simple string matching and single-run tests, it helps quality engineers ensure that AI agents remain reliable, objective-oriented, and free from data leakage, making it an essential tool for teams deploying complex autonomous systems.

주요 기능

01Statistical test evaluation and distribution analysis

02Multi-dimensional reliability and production metrics

03Data leakage prevention and benchmark validation

04Adversarial testing patterns to identify edge cases

05Behavioral regression testing frameworks

060 GitHub stars

사용 사례

01Validating agent reliability before production deployment

02Designing custom benchmarks for domain-specific agent tasks

03Diagnosing and fixing flaky behavior in non-deterministic LLM workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/izacenter agent-evaluation

For use in Claude.ai and ChatGPT

Download Skill