Can this skill prevent data leakage?

Yes, it includes specific patterns to ensure test data is not accidentally included in prompt contexts or training sets, which preserves the integrity of your evaluations.

What is Statistical Test Evaluation?

It involves running the same test multiple times and analyzing the distribution of outcomes to ensure an agent's success is consistent and not a statistical fluke.

Why can't I use traditional unit testing for LLM agents?

Agents are non-deterministic, meaning the same input can yield different results. This skill uses statistical analysis and behavioral invariants rather than simple string matching to account for this variability.

How does this skill help with production failures?

It implements behavioral contract testing and adversarial patterns to catch edge cases that standard benchmarks often miss, ensuring the agent performs in the wild as well as it does in the lab.

Agent Evaluation

Name: Agent Evaluation
Author: claudiodearaujo

byclaudiodearaujo

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Security & Testing

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

The Agent Evaluation skill transforms Claude into a specialized quality engineer focused on the unique challenges of non-deterministic AI systems. It moves beyond simple string matching to implement robust evaluation frameworks including behavioral contract testing, adversarial testing, and statistical analysis of result distributions. By bridging the gap between high-level benchmark scores and real-world performance, this skill helps developers identify regressions, prevent data leakage, and ensure agents remain reliable even when handling complex, edge-case scenarios in production environments.

Key Features

01Regression testing frameworks designed specifically for LLM workflows

02Statistical distribution analysis for non-deterministic agent outputs

03Production-grade reliability metrics and capability assessment

04Behavioral contract testing to ensure invariant agent logic

05Adversarial testing patterns to discover hidden edge-case failures

061 GitHub stars

Use Cases

01Benchmarking a new autonomous agent against real-world production datasets

02Setting up CI/CD pipelines for AI agents to detect behavioral regressions

03Validating agent reliability through multi-run statistical evaluation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/sistema-de-narra-o-de-livro agent-evaluation

For use in Claude.ai and ChatGPT

Download Skill