In reinforcement learning applications, agents usually need to deal with various input/output features when specified with different state and action spaces by their developers or physical restrictions, indicating re-training from scratch and considerable sample inefficiency, especially when agents follow similar solution steps to achieve tasks.In this paper, we aim to transfer pre-trained skills to alleviate the above challenge. Specifically, we propose PILoT, i.e., Planning Immediate Landmarks of Targets. PILoT utilizes the universal decoupled policy optimization to learn a goal-conditioned state planner; then, we distill a goal-planner to plan immediate landmarks in a model-free style that can be shared among different agents. In our experiments, we show the power of PILoT on various transferring challenges, including few-shot transferring across action spaces and dynamics, from low-dimensional vector states to image inputs, from simple robot to complicated morphology; and we also illustrate PILoT provides a zero-shot transfer solution from a simple 2D navigation task to the harder Ant-Maze task.