agent-evaluation

Behavioral testing and reliability metrics for LLM agents, catching production failures benchmarks miss. Covers five core evaluation areas: agent testing, benchmark design, capability assessment, reliability metrics, and regression testing Emphasizes statistical test evaluation (multiple runs, result distribution analysis) and behavioral contract testing over single-run or string-matching approaches Includes adversarial testing patterns to actively probe agent failure modes and identify brittleness Addresses critical sharp edges: benchmark-to-production gaps, flaky test handling, metric gaming, and test data leakage prevention

INSTALLATION
npx skills add https://github.com/davila7/claude-code-templates --skill agent-evaluation
Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

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

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