Software quality is one of the most important research sub-areas of software engineering. Hence, a plethora of research has focused on the prediction of software quality. Much of the software analytics and prediction work has proposed metrics, models and novel approaches that can predict quality with high levels of accuracy. However, adoption of such techniques remain low; one of the reasons for this low adoption of the current analytics and prediction technique is the lack of actionable and publicly available tools. We present Commit Guru, a language agnostic analytics and prediction tool that identifies and predicts risky software commits. Commit Guru is publicly available and is able to mine any GIT SCM repository. Analytics are generated at both, the project and commit levels. In addition, Commit Guru automatically identifies risky (i.e., bug-inducing) commits and builds a prediction model that assess the likelihood of a recent commit introducing a bug in the future. Finally, to facilitate future research in the area, users of Commit Guru can download the data for any project that is processed by Commit Guru with a single click. Several large open source projects have been successfully processed using Commit Guru. Commit Guru is available online at commit.guru. Our source code is also released freely under the MIT license.