Real Time Strategy (RTS) games can be very challenging, especially to novice users, who are normally overwhelmed by the dynamic, distributed, and multi-objective structure of these games. In this paper we present RTSMate, an advice system designed to help the player of an RTS game. Using inference mechanisms to reason about the game state and a decision tree to encode its knowledge, RTSMate helps the player by giving him/her tactical and strategical tips about the best actions to be taken according to the current game state, aiming at improving player's performance. This paper describes the main ideas behind the system, its implementation, and the experiments performed using the system in a real game environment. Results show that RTSMate fulfills its objective: most players considered the system useful and were able to improve their performance by using it.