Effective skill learning in an unsupervised manner is one of the capabilities an intelligent agent or robot should have. The discovered task-agnostic skills can be fine-tuned to downstream long-horizon tasks to improve execution efficiency. Unfortunately, the self-learning of locomotion skills, which occurs naturally in infancy, has been slow to develop in robotics. The instability exhibited by existing skill-learning methods makes it difficult to directly apply to complex control tasks, such as humanoid robots. To acquire reliable robotic locomotion skills, this article proposes a controllable latent space partition framework to assist reinforcement learning in accomplishing practicability-oriented unsupervised skill discovery (PoSD). Specifically, we use the distance similarity measure of the trajectory feature space to introduce the indicative information of the expert demonstrations into the partitioning and mapping process of the latent space. In addition, the intrinsic subrewards based on contrastive learning and particle entropy are designed to promote skill diversity and encourage exploration. Finally, reinforcement learning completes the generation of skill-conditioned policy driven by composite intrinsic rewards. The performance investigation of our method is conducted on five robots with more than 15 skills. The results indicate that PoSD achieves noticeable improvements in adaptation efficiency and practicability compared with other SOTA unsupervised skill discovery methods.