Can this skill help if my agent performs well on benchmarks but fails in production?

Yes, it specifically addresses the 'benchmark gap' by providing patterns for production-centric evaluation and multi-dimensional metrics that prevent agents from being 'gamed' for specific scores.

How does this skill handle 'flaky' test results in AI agents?

It utilizes Statistical Test Evaluation, running tests multiple times to analyze the probability of success and the distribution of outcomes rather than relying on a single pass/fail result.

What is behavioral contract testing for agents?

It involves defining specific invariants—rules or behaviors that the agent must always follow—and testing against those rules to ensure the agent remains within its operational boundaries.

Why is LLM agent evaluation different from traditional software testing?

Traditional software is deterministic, while LLM agents can produce different outputs for the same input. Evaluation requires statistical analysis of result distributions and behavioral contracts rather than simple string matching.

Agent Evaluation & Benchmarking

Name: Agent Evaluation & Benchmarking
Author: claudiodearaujo

byclaudiodearaujo

0•

Security & Testing

Evaluates and benchmarks LLM agents using behavioral testing, reliability metrics, and production monitoring to ensure consistent performance in real-world scenarios.

The Agent Evaluation skill provides a comprehensive framework for testing and validating LLM agents, addressing the unique challenges of non-deterministic AI behavior. It enables developers to move beyond simple unit tests by implementing behavioral regression tests, capability assessments, and statistical evaluations that analyze result distributions. By focusing on behavioral contracts and adversarial testing, this skill helps identify why agents that ace standard benchmarks often fail in production, providing the tools necessary to bridge the gap between lab performance and real-world reliability.

Key Features

01Statistical test evaluation for non-deterministic LLM outputs

02Behavioral contract testing to define agent invariants

030 GitHub stars

04Regression testing patterns to prevent capability drift

05Multi-dimensional reliability metrics for production monitoring

06Adversarial testing to proactively identify edge-case failures

Use Cases

01Benchmarking new agent architectures against real-world production tasks

02Analyzing agent reliability and performance distributions across multiple runs

03Setting up CI/CD pipelines specifically for AI agent behavior validation

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