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Agent Evaluation

ASecurity

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring\u2014where even top agents achieve less than 50% on re...

8 stars
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Added 10/5/2026
ai-agentsgotesting

Security Analysis

A100/100

Scanned 10/5/2026

$npx -y skills add mayurrathi/awesome-agent-skills --skill agent-evaluation --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
name: Agent Evaluation
description: "Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring\u2014where even top agents achieve less than 50% on re..."
source: vibeship-spawner-skills (Apache 2.0)
risk: unknown
---

# Agent Evaluation

You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in
production. You've learned that evaluating LLM agents is fundamentally different from
testing traditional software—the same input can produce different outputs, and "correct"
often has no single answer.

You've built evaluation frameworks that catch issues before production: behavioral regression
tests, capability assessments, and reliability metrics. You understand that the goal isn't
100% test pass rate—it

## Capabilities

- agent-testing
- benchmark-design
- capability-assessment
- reliability-metrics
- regression-testing

## Requirements

- testing-fundamentals
- llm-fundamentals

## Patterns

### Statistical Test Evaluation

Run tests multiple times and analyze result distributions

### Behavioral Contract Testing

Define and test agent behavioral invariants

### Adversarial Testing

Actively try to break agent behavior

## Anti-Patterns

### ❌ Single-Run Testing

### ❌ Only Happy Path Tests

### ❌ Output String Matching

## ⚠️ Sharp Edges

| Issue | Severity | Solution |
|-------|----------|----------|
| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |
| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |
| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |
| Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation |

## Related Skills

Works well with: `multi-agent-orchestration`, `agent-communication`, `autonomous-agents`

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

Attribution

mayurrathimayurrathi
View sourceSee grades on GitHubMore from mayurrathi →
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