agent-evaluation
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...
Best use case
agent-evaluation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...
Teams using agent-evaluation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agent-evaluation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agent-evaluation Compares
| Feature / Agent | agent-evaluation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 to Use This skill is applicable to execute the workflow or actions described in the overview.
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