agent-evaluation

by @ngxtm in AI & LLM

# Install this skill:

npx skills add ngxtm/devkit --skill "agent-evaluation"

Install specific skill from multi-skill repository

# 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.

# 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

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

# Supported AI Coding Agents

This skill is compatible with the SKILL.md standard and works with all major AI coding agents:

⚡ Amp 🚀 Antigravity 🤖 Claude Code 🦀 Clawdbot 📝 Codex ▶️ Cursor 🤖 Droid 💎 Gemini CLI 🐙 GitHub Copilot 🪿 Goose 📊 Kilo Code 🔧 Kiro CLI 💻 OpenCode 🦘 Roo Code 🌲 Trae 🏄 Windsurf

Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.