Agent Evaluation
SkillMonitoring & opsAgent evaluation is a skill that guides an AI agent through testing another LLM agent the way a quality engineer would. It covers statistical evaluations across repeated runs, behavioral contract checks, adversarial probing, and reliability metrics, while steering clear of common mistakes like judging by exact output strings or relying only on happy-path tests.
Available today. Use it from your connected AI after setup.
No other account needed.
Have an agent that knows testing fundamentals and LLM fundamentals, which this skill builds on.
Then ask your AI: use the Agent Evaluation skill
What your AI can do with it
- Run statistical test evaluations across multiple runs and analyze result distributions
- Define and test behavioral invariants with behavioral contract testing
- Probe agent behavior with adversarial test cases
- Design benchmarks and assess agent capabilities
- Track reliability metrics and run regression tests
- Avoid pitfalls like single-run testing, output string matching, and data leakage
Getting started
- Have an agent that knows testing fundamentals and LLM fundamentals, which this skill builds on.
- Add the agent-evaluation skill to the agent that will do the testing.
- Ask the agent to evaluate a target LLM agent using statistical, behavioral, or adversarial tests.
- Review the results and watch for sharp edges such as flaky tests, metric gaming, and test data leaking into prompts.
What this skill tells your AI
The instructions your AI receives, as published by davila7/claude-code-templates in cli-tool/components/skills/ai-research/agent-evaluation/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 32k
- Forks
- 4k
- Last commit
- Sep 2026
Others that do the same job
Questions
- What is agent testing?
- It is evaluating an LLM agent's behavior through repeated statistical runs, behavioral contract checks, capability assessments, and adversarial cases, rather than treating it like traditional software with one correct output.
- What is agent evaluation?
- It is the process of measuring how an LLM agent performs on real tasks using benchmarks, reliability metrics, and regression tests, recognizing that the same input can produce different outputs.
Advanced
- Item type
- skill
- Key
agent-evaluation-davila7- Source
- github.com/davila7/claude-code-templates