What problem does it solve? Editing talking-head videos, interviews, podcasts, and courses requires deciding which spoken segments to keep, delete, or reorder. Without clear criteria, editors over-delete meaningful connective words, splice incompatible retakes, or strip natural breathing pauses, producing unnatural dialogue. ## Core Features & Use Cases - Semantic Unit Editing: Edits speech by complete sentences, answers, and ideas rather than keyword fragments, preserving connective words and context. - Filler Word Classification: Applies a three-category table to distinguish safe-to-delete fillers, context-dependent fillers, and functional connectives. - Retake and Pause Handling: Identifies true retakes, removes only fully covered failed attempts, and compresses long pauses to about 0.3 seconds without eliminating natural breath. - Use Case: Given a transcribed interview recording, use this Skill to clean filler words, remove failed retakes, tighten pauses, and restructure answers on an editable transcript before applying changes to the timeline. ## Quick Start Use the a-roll skill to clean up the transcribed talking-head footage by removing filler words and failed retakes while keeping complete semantic units.