About agent-evaluation
agent-evaluation is a skill for quality engineers assessing LLM agents. It addresses variable outputs and open-ended correctness through statistical evaluation, behavioral contracts, capability assessment, and adversarial tests.
Key features
- 01Agent and regression testing
- 02Benchmark design
- 03Capability assessment
- 04Reliability metrics
- 05Statistical test evaluation
- 06Behavioral contracts and adversarial testing
Use cases
- 01Running tests repeatedly and analyzing result distributions
- 02Checking agent behavioral invariants
- 03Probing agent failure boundaries
- 04Investigating reliability issues in production
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/agent-evaluationin Claude Code to confirm it loaded.
Details
From the project READMEAgent 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