agent-evaluation

Name: agent-evaluation
Availability: InStock
Author: Ludwig

Evaluate LLM agent behavior across repeated runs and adversarial conditions.

Updated Apr 8, 2026

Search Tags:#benchmarking #reliability #regression testing #agent testing #llm agents #behavioral analysis

Ready to run

npx skills add https://github.com/LudwigAJ/swell --skill agent-evaluation-ludwigaj

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/LudwigAJ/swell/tree/main/.factory/skills/agent-evaluation
Command: npx skills add https://github.com/LudwigAJ/swell --skill agent-evaluation-ludwigaj

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you evaluate LLM agents realistically instead of relying on one-off benchmark scores, so you can detect failures that only appear under repeated runs, adversarial prompts, or production conditions.

Core Features & Use Cases

Behavioral Testing: Define invariants and expected agent behaviors that should hold across runs.
Capability Assessment: Measure whether an agent can complete real tasks, not just synthetic benchmarks.
Reliability Metrics: Track flakiness, variance, regressions, and production-quality performance.
Use Case: Use it when a multi-agent workflow looks good in demos but needs repeated evaluation, regression coverage, and adversarial stress tests before deployment.

Quick Start

Ask the skill to design a robust evaluation plan for your LLM agent, including behavioral tests, repeated benchmark runs, and 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 evaluate LLM agent reliability across repeated runs?▼

To evaluate LLM agent reliability, you must track flakiness, variance, and regressions across multiple runs using statistical evaluation and behavioral invariants rather than relying on one-off benchmark scores. This detects failures that only appear under repeated execution conditions.

What is behavioral testing for autonomous or multi-agent systems?▼

Behavioral testing for autonomous or multi-agent systems involves defining invariants and expected agent behaviors that must hold true across repeated runs and adversarial test conditions. It measures whether an agent can complete real tasks instead of just passing synthetic benchmarks.

How do I run regression testing and adversarial stress tests on LLM agents before deployment?▼

Regression testing and adversarial stress tests for LLM agents require applying multi-dimensional metrics and adversarial prompts to simulate production conditions. This process evaluates capability assessment and provides safeguards against metric gaming and data leakage before deployment.

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

LLM agents often pass benchmarks but fail in production because one-off scores do not account for flakiness, variance, or edge cases under adversarial prompts. Realistic evaluation requires repeated runs, behavioral invariants, and reliability metrics to catch these hidden failures.

Can I use statistical evaluation to detect metric gaming in agent testing?▼

Yes, statistical evaluation can detect metric gaming in agent testing by applying multi-dimensional metrics and behavioral invariants across repeated runs. This approach identifies unnatural score variations and provides safeguards against data leakage.