This work aims to reconstruct a complete and detailed clothed human from a single-view input. Implicit function is suitable for this task because it represents fine shape details and varied topology. Current methods, however, often suffer from artifacts such as broken or disembodied body parts, missing details, or depth ambiguity due to the ambiguity and complexity of human articulation. We observe the main issue is structure-agnostic. To address these problems, we fully utilize the SMPL body model and propose the method using the skeleton-aware implicit function. To alleviate the broken or disembodied body parts, the proposed skeleton-aware structure prior makes the skeleton awareness into implicit function, which consists of a  bone-guided sampling strategy and a skeleton-relative encoding strategy. To deal with the missing details and depth ambiguity problems, our body-guided pixel-aligned feature exploits the SMPL to enhance 2D normal and depth semantic features, and the proposed feature aggregation uses the extra geometry-aware prior to enable a more plausible merging with less noisy geometry. Additionally, SIF is also adapted to the RGB-D input, and experimental results show that SIF outperforms the SOTA methods on challenging dataset from Twindom and Thuman3.0.