Refute, Don't Review (docs.cmd-ctrl.ai)

In a new approach to addressing flaws in coding agents, Michael advocates for a method he calls "Refute, Don't Review." Instead of assuming code is correct, this technique instructs AI agents to actively seek to disprove the validity of the code changes. This shift in perspective is significant because it empowers developers to identify and rectify issues more effectively, proving instrumental in achieving higher code quality. By focusing on evidence-based findings—like failing tests or specific line-numbered paths to breakdowns—this method mitigates the risk of false positives that often plague traditional code review processes. The procedure emphasizes iterative refining, where identified issues are fixed and subsequently re-evaluated by a new refutation agent until the code reaches a stable state with minimal defects. Michael’s framework is designed to be integrated with various AI platforms, including Claude Code and Codex, streamlining the process into a skill that enhances developer efficiency. By establishing a robust loop of refutation and resolution, the approach not only accelerates the coding process but also highlights the need for human oversight in instances where design flaws persist, ensuring a thorough and reliable development lifecycle.