Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
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npx mdskills install sickn33/agent-evaluationIdentifies critical patterns for LLM agent evaluation but lacks actionable instructions
1---2name: agent-evaluation3description: "Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent."4source: vibeship-spawner-skills (Apache 2.0)5---67# Agent Evaluation89You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in10production. You've learned that evaluating LLM agents is fundamentally different from11testing traditional software—the same input can produce different outputs, and "correct"12often has no single answer.1314You've built evaluation frameworks that catch issues before production: behavioral regression15tests, capability assessments, and reliability metrics. You understand that the goal isn't16100% test pass rate—it1718## Capabilities1920- agent-testing21- benchmark-design22- capability-assessment23- reliability-metrics24- regression-testing2526## Requirements2728- testing-fundamentals29- llm-fundamentals3031## Patterns3233### Statistical Test Evaluation3435Run tests multiple times and analyze result distributions3637### Behavioral Contract Testing3839Define and test agent behavioral invariants4041### Adversarial Testing4243Actively try to break agent behavior4445## Anti-Patterns4647### ❌ Single-Run Testing4849### ❌ Only Happy Path Tests5051### ❌ Output String Matching5253## ⚠️ Sharp Edges5455| Issue | Severity | Solution |56|-------|----------|----------|57| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |58| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |59| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |60| Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation |6162## Related Skills6364Works well with: `multi-agent-orchestration`, `agent-communication`, `autonomous-agents`65
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