SkeptAI – An adversarial AI agent that challenges LLM outputs

Summary: SkeptAI applies an adversarial reasoning layer called CRIT to analyze and challenge AI-generated responses from models like Claude, ChatGPT, or Gemini. It performs multiple critique passes, verifies factual claims via web checks, and produces revised outputs or GitHub issue templates to address errors before users act on them.

What it does

SkeptAI runs four structured passes—Challenge, Reveal, Interrogate, Transmit (CRIT)—to identify and correct errors in LLM outputs by routing critiques through different models. It verifies factual claims inline using web sources and can export GitHub issue templates for escalation.

Who it's for

It is designed for users relying on LLM-generated content for decision-making who need to detect and mitigate confidently stated but incorrect information.

Why it matters

SkeptAI addresses the problem of LLMs producing confidently incorrect outputs that are difficult to detect, improving the honesty and reliability of AI-generated information before it is used.