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.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add davila7/claude-code-templates --skill "agent-evaluation" -g -a claude-code -yOr manually — copy the SKILL.md below into:
~/.claude/skills/agent-evaluation-davila7/SKILL.md---
name: agent-evaluation
description: "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."
source: vibeship-spawner-skills (Apache 2.0)
---
# 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`
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code