The KSL Pose Dataset is designed for computational analysis of Kenyan Sign Language (KSL) movements, leveraging pose estimation using MediaPipe. Each word in the dataset has a corresponding stickman video—generated from skeletal keypoints detected by MediaPipe—and NumPy files storing precise pose data, including joint coordinates, temporal trajectories, and kinematic descriptors. These pose representations are extracted frame-by-frame, capturing fine-grained motion patterns. The dataset enables spatiotemporal modeling for sign language recognition, facilitating pose-based feature extraction, sequence learning (e.g., LSTMs, Transformers), and multi-modal fusion techniques. It serves as a critical resource for computer vision, gesture analysis, and sign language processing research.