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agent-evaluation

Test and benchmark LLM agents using behavioral contracts, statistical evaluation, and reliability metrics.

1|Updated May 10, 2026
Search Tags:#benchmarking#regression testing#adversarial testing#behavioral testing#llm testing#agent evaluation#reliability metrics
One-click install
npx skills add https://github.com/Tgoldi/claude-skills --skill agent-evaluation-tgoldi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/Tgoldi/claude-skills/tree/main/agent-evaluation
Command: npx skills add https://github.com/Tgoldi/claude-skills --skill agent-evaluation-tgoldi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? LLM agents behave non-deterministically, so traditional single-run software tests fail to catch regressions—agents that ace benchmarks often fail in production. This Skill provides a structured evaluation framework covering behavioral testing, capability assessment, and reliability metrics. ## Core Features & Use Cases - Statistical Test Evaluation: Run tests multiple times and analyze result distributions instead of relying on single pass/fail outcomes. - Behavioral Contract Testing: Define and enforce agent behavioral invariants, plus adversarial testing to actively break agent behavior. - Anti-Pattern Detection: Avoid single-run testing, happy-path-only tests, and brittle output string matching. - Use Case: Before deploying a customer-support agent, run a regression suite across repeated trials, measure score distributions, and verify the agent is not gaming a single metric while failing the actual task. ## Quick Start Evaluate my LLM agent with a behavioral regression test suite that runs each test multiple times and reports reliability metrics.

Frequently Asked Questions about agent-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I test an LLM agent that gives different outputs each run?▼

Run each test multiple times and analyze the distribution of results rather than a single pass/fail outcome. Statistical test evaluation measures success rates and variance, which reflects the non-deterministic nature of LLM agents.

What is behavioral contract testing for AI agents?▼

Behavioral contract testing defines invariants the agent must always satisfy—such as never leaking data or always citing sources—and verifies them across many runs. It catches violations that output string matching would miss.

Why does my agent pass benchmarks but fail in production?▼

Benchmarks often differ from real-world conditions, and agents can be optimized for the metric rather than the actual task. Bridge the gap with multi-dimensional evaluation and production monitoring alongside benchmark scores.

How do I handle flaky tests in LLM agent evaluation?▼

Treat flakiness as a signal, not noise: repeat tests, track pass-rate distributions, and set reliability thresholds instead of binary assertions. Avoid output string matching, which amplifies flakiness from harmless phrasing differences.

What are the risks of test data leaking into agent training or prompts?▼

Data leakage inflates evaluation scores because the agent has effectively seen the answers. Keep test sets isolated from training data and prompt context, and audit for accidental overlap before trusting benchmark results.

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