As spoken dialogue systems take on diverse personas—authoritative instructors, uncooperative merchants, distracted workers—they need distinct, human-like turn-taking to stay immersive. Yet current full-duplex systems default to a rigid "always-yield" policy during overlap, undermining character consistency for non-submissive roles, and evaluating persona-specific alternatives through user studies demands substantial real-time engineering. We present PersonaKit (PK), an open-source, low-latency web platform that simulates full-duplex turn-taking pragmatics—via a cascade pipeline and prompt-level control rather than a jointly trained speak-while-listening model. Through intuitive JSON configurations, researchers define personas, specify probabilistic interruption handling (yield, hold, bridge, override), and auto-deploy comparative A/B surveys. An in-the-wild evaluation with 8 personas shows that PK is an extensible, end-to-end framework for studying complex sociolinguistic behaviors in next-generation spoken agents.