Developed the Egocentric Video Mapper documentation feature for the pupil-labs/pupil-docs repository, focusing on enabling researchers to map gaze data onto alternative egocentric video streams from third-party cameras. The work centered on integrating AI-powered synchronization guidance, providing clear instructions for aligning gaze data with external video sources in Neon-enabled eye-tracking experiments. Leveraging Markdown and web components, the documentation included a comprehensive step-by-step integration guide and practical advice for incorporating specialized cameras into Alpha Lab workflows. This contribution enhanced reproducibility and streamlined experimental setup, demonstrating technical writing and AI integration skills while addressing the needs of researchers working with complex video data.