agent-evaluation
Framework for testing and benchmarking LLM agent reliability through behavioral and regression testing.
Install
mkdir -p .claude/skills/agent-evaluation && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1043" && unzip -o skill.zip -d .claude/skills/agent-evaluation && rm skill.zipInstalls to .claude/skills/agent-evaluation
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
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.Key capabilities
- →Perform behavioral regression testing
- →Assess agent capability via benchmarks
- →Calculate agent reliability metrics
- →Execute adversarial testing
- →Analyze result distributions through statistical testing
How it works
It employs statistical analysis, behavioral contracts, and adversarial inputs to evaluate agent performance, acknowledging that LLM outputs are non-deterministic.
Inputs & outputs
When to use agent-evaluation
- →Define behavioral invariants for agents
- →Perform agent regression testing
- →Benchmarking agent capability for production
About this skill
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
When not to use it
- →Single-run testing
- →Testing only the happy path
- →Output string matching as the sole evaluation method
Prerequisites
Limitations
- →High risk of flaky tests in LLM environments
- →Potential for data leakage if test data is used in training
How it compares
It moves beyond simple benchmark scores to assess production reliability through behavioral invariants and multi-dimensional evaluation.
Compared to similar skills
agent-evaluation side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| agent-evaluation (this skill) | 3 | 6mo | No flags | Advanced |
| create-eval | 1 | 4mo | Review | Intermediate |
| llama-factory | 15 | 8mo | No flags | Advanced |
| grpo-rl-training | 5 | 7mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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