Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. Existing skill-based approaches often assume a fixed parametric prior (e.g., a single Gaussian), limiting their ability to capture diverse and multi-modal skill structures required for complex tasks. We propose a Bayesian non-parametric skill prior that models temporally extended skills in a structured latent space using a Dirichlet Process Mixture, enabling adaptive skill discovery without predefining the number of components. Integrated into a hierarchical RL framework, the learned prior guides high-level skill selection while a pretrained decoder generates temporally abstracted actions, improving exploration efficiency in sparse-reward settings. Experiments on Franka Kitchen, LIBERO-Long, Meta-World, and a real robot demonstrate consistent gains in long-horizon manipulation, achieving over 0.8 success rate within 1.5M steps, whereas SAC fails to converge even after 5M steps ($<$0.1). Compared to a single-Gaussian prior baseline, our model yields an average improvement of 21.8\%.

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