Dynamic pricing is pivotal for improving e-commerce efficiency and profitability. While offline reinforcement learning (RL) offers a data-driven approach to policy optimization, its practical adoption faces three core constraints: uninterpretable black-box policies, limited multi-level strategy abstraction, and distributional bias in static historical data. To address these issues, we propose SkillPrice, a hierarchical offline RL framework with automatic semantic skill discovery. SkillPrice extracts lifecycle-aware pricing skills from historical trajectories via discrete representation learning, iteratively refined through LLM-based semantic alignment to produce interpretable, reusable operational patterns. Building upon this structured skill space, SkillPrice constructs a two-level policy architecture, where the skill scheduler selects semantic skills, and the low-level policy executes atomic pricing actions under confidence-aware constraints with conservative regularization to ensure offline safety. Extensive experiments on real e-commerce datasets demonstrate SkillPrice consistently outperforms state-of-the-art baselines in both revenue optimization and interpretability, establishing a scalable and deployable paradigm for industrial dynamic pricing.