Can this skill help with agents that score well on benchmarks but fail in reality?

Yes, it provides frameworks to bridge the gap by designing custom benchmarks that reflect real-world production data and adversarial scenarios.

What are behavioral contract tests in the context of AI?

Behavioral contracts define invariants that an agent must never violate, regardless of the specific phrasing of its output, ensuring it adheres to safety and logic constraints.

Why is standard unit testing insufficient for LLM agents?

Standard unit testing relies on deterministic string matching, which fails to account for the varied but valid outputs LLMs produce. Agent evaluation uses statistical analysis and behavioral contracts to handle non-determinism.

How does this skill handle 'flaky' test results?

It implements statistical test evaluation, running scenarios multiple times to analyze the distribution of results rather than relying on a single pass/fail outcome.

Agent Evaluation Framework

Name: Agent Evaluation Framework
Author: claudiodearaujo

byclaudiodearaujo

0•

Security & Testing

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

This skill equips Claude with the expertise of a quality engineer specializing in LLM agents, addressing the unique challenges of testing non-deterministic software. It provides structured patterns for behavioral contract testing, statistical distribution analysis, and adversarial testing to bridge the gap between high benchmark scores and actual production reliability. By implementing these rigorous evaluation frameworks, developers can identify regressions, assess multi-dimensional capabilities, and prevent common pitfalls like data leakage or metric gaming in AI-driven workflows.

Key Features

01Adversarial testing to identify edge-case failures

02Multi-dimensional reliability and capability metrics

03Production-ready monitoring and regression frameworks

04Behavioral contract testing for invariant verification

050 GitHub stars

06Statistical evaluation of non-deterministic agent outputs

Use Cases

01Benchmarking a new LLM agent against real-world production scenarios

02Identifying and fixing flaky agent behaviors in complex workflows

03Preventing performance regressions when updating underlying LLM models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

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

For use in Claude.ai and ChatGPT

Download Skill