Reddi Agent Evaluation

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version: 1.0.1 name: reddi-agent-evaluation description: > reddi.tech fork of agent-evaluation. Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring. Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent. source: vibeship-spawner-skills (Apache 2.0) fork-of: agent-evaluation metadata: openclaw: emoji: "📋" requires: bins: - python3 env: [] primaryEnv: null network: outbound: true reason: "Calls configured LLM API for agent evaluation scoring."

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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