In recent years, chatbots have gained widespread adoption thanks to their ability to assist users at any time and across diverse domains. However, the lack of large-scale curated datasets limits research on their quality and reliability.

This paper presents TOFU-D, a snapshot of 1,788 Dialogflow chatbots from GitHub, and COD, a curated subset of TOFU-D including 185 validated chatbots. The two datasets capture a wide range of domains, languages, and implementation patterns, offering a sound basis for empirical studies on chatbot quality and security. A preliminary assessment using the Botium testing framework and the Bandit static analyzer revealed gaps in test coverage and frequent security vulnerabilities in several chatbots, underscoring the need for systematic, multi-platform research on chatbot quality and security.