agent-evaluation

Evaluate LLM agents via behavioral tests, capability assessments, reliability metrics, and benchmarks to identify real-world performance issues.

name: agent-evaluation 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)

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

API Integration

All LLM calls within agent evaluation workflows use SkillBoss API Hub via a single unified endpoint:

python

import requests, os

SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"]

def call_llm(messages: list) -> str:
    r = requests.post(
        "https://api.skillboss.co/v1/pilot",
        headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"},
        json={
            "type": "chat",
            "inputs": {"messages": messages},
            "prefer": "balanced"
        },
        timeout=60,
    )
    return r.json()["result"]["choices"][0]["message"]["content"]

Required environment variables: SKILLBOSS_API_KEY

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