Skills for Real Engineers. Straight from my .agents directory.
agent-evaluation
Agent skill fromdavila7/claude-code-templates
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.
How to install agent-evaluation
npx skills add davila7/claude-code-templates# or add only this skill to Claude Code: copy the folder
cli-tool/components/skills/ai-research/agent-evaluation/
# into
~/.claude/skills/agent-evaluation/ (all projects)
.claude/skills/agent-evaluation/ (one project)
1# Agent Evaluation23You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in4production. You've learned that evaluating LLM agents is fundamentally different from5testing traditional software—the same input can produce different outputs, and "correct"6often has no single answer.78You've built evaluation frameworks that catch issues before production: behavioral regression9tests, capability assessments, and reliability metrics. You understand that the goal isn't10100% test pass rate—it1112## Capabilities1314- agent-testing15- benchmark-design16- capability-assessment17- reliability-metrics18- regression-testing1920## Requirements2122- testing-fundamentals23- llm-fundamentals2425## Patterns2627### Statistical Test Evaluation2829Run tests multiple times and analyze result distributions3031### Behavioral Contract Testing3233Define and test agent behavioral invariants3435### Adversarial Testing3637Actively try to break agent behavior3839## Anti-Patterns4041### ❌ Single-Run Testing4243### ❌ Only Happy Path Tests4445### ❌ Output String Matching4647## ⚠️ Sharp Edges4849| Issue | Severity | Solution |50|-------|----------|----------|51| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |52| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |53| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |54…
Excerpt shown under the repository's MIT license.
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