Why is statistical evaluation necessary for agents?

Because LLMs are stochastic, a single successful run doesn't guarantee reliability. Statistical evaluation runs tests multiple times to calculate pass rates and confidence intervals.

Which benchmarks are supported by this skill?

The skill provides guidance for integrating with top-tier benchmarks including AgentBench, Tau-bench (Sierra), ToolEmu, and evaluation platforms like Langsmith.

How does behavioral contract testing work?

It defines a set of 'must-do' and 'must-not-do' rules for the agent, using automated detectors to ensure the agent stays within prescribed operational and ethical boundaries.

What is LLM agent evaluation?

It is the systematic process of testing an autonomous agent's ability to complete tasks, follow safety protocols, and maintain consistency across multiple runs using statistical and behavioral metrics.

Agent Evaluation & Benchmarking

Name: Agent Evaluation & Benchmarking
Author: lingxling

bylingxling

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보안 및 테스팅

Evaluates and benchmarks LLM agents through behavioral testing, reliability metrics, and production monitoring frameworks.

The Agent Evaluation skill provides a specialized framework for assessing the performance, safety, and reliability of autonomous LLM agents. Recognizing that even top-tier agents often struggle with real-world benchmarks, this skill implements sophisticated patterns like statistical evaluation for stochastic behaviors, behavioral contract testing to ensure operational invariants, and adversarial testing to surface edge-case failures. It serves as a comprehensive guide for developers to move beyond manual testing by integrating industry-standard tools and metrics to validate agent capabilities before and during production deployment.

주요 기능

01Statistical distribution analysis for measuring stochastic agent consistency

0246 GitHub stars

03Integration with major benchmarks like AgentBench and Tau-bench

04Behavioral contract enforcement to define and test agent invariants

05Production monitoring metrics for latency, token usage, and pass rates

06Adversarial testing patterns to identify failure modes and prompt injections

사용 사례

01Benchmarking different LLM models for specific autonomous agent tasks

02Validating agent reliability and safety before production deployment

03Regression testing for complex agentic workflows and tool-use scenarios

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lingxling/awesome-skills-cn agent-evaluation

For use in Claude.ai and ChatGPT

주요 기능

01Statistical distribution analysis for measuring stochastic agent consistency

0246 GitHub stars

03Integration with major benchmarks like AgentBench and Tau-bench

04Behavioral contract enforcement to define and test agent invariants

05Production monitoring metrics for latency, token usage, and pass rates

06Adversarial testing patterns to identify failure modes and prompt injections

사용 사례

01Benchmarking different LLM models for specific autonomous agent tasks

02Validating agent reliability and safety before production deployment

03Regression testing for complex agentic workflows and tool-use scenarios

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lingxling/awesome-skills-cn agent-evaluation

For use in Claude.ai and ChatGPT