Evaluate LLM agent behavior across repeated runs and adversarial conditions.
npx skills add https://github.com/LudwigAJ/swell --skill agent-evaluation-ludwigaj
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
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.
Ask the skill to design a robust evaluation plan for your LLM agent, including behavioral tests, repeated benchmark runs, and reliability metrics.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
NicolasPadovamDevelop, evaluate, and optimize custom agent skills within the Opensquad framework.
BradliebsMonitor live web apps for console errors, performance regressions, and visual anomalies.
bugrabilgeDesign benchmark test suites for evaluating LLM agent reliability.
Claude-Code-Community-IrelandEvaluate LLM agents through behavioral testing and reliability metrics.
