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.
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.
Similar popular skills
Related neighbors and high-traction skills in the same topics — useful to compare before installing.
Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.
Claude CodeNot declared
CursorNot declared
CodexNot declared
GitHub CopilotNot declared
WindsurfNot declared
Gemini CLINot declared
ClineNot declared
OpenCodeNot declared
Package contents
Files included with this skill beyond the listing page.
skill mdSKILL.md2,089 B
docsSUMMARY.md322 B
History
First recorded snapshot · 0 installs
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