What problem does it solve? Turning an Instagram reel or carousel into a reusable reference benchmark requires manual extraction of media, transcripts, music cues, and frame-level analysis, then writing it all up in a consistent wiki format. This Skill automates that pipeline so every ingested post becomes a comparable, greppable structure page. ## Core Features & Use Cases - End-to-end ingest pipeline: Detects reel vs carousel from the URL, runs the matching extractor, persists the full media bundle (audio, video, frames, metadata) into the vault, and writes both a source pointer and a synthesis page. - Multi-window Shazam scanning: Slides a fingerprinting window across reel audio to identify every music cue and map each one to narrative beats, instead of catching only a single track. - Shot Catalogue classification: Classifies every 2-second frame by distance, angle, motion, and treatment using a canonical taxonomy, flags creative shots, and maintains a cross-reel atomic shot library. - Use Case: Paste an instagram.com/reel/ URL and say "add this to the wiki" — the Skill extracts the media, identifies the music, writes a beat map and pattern rules, classifies all frames, and updates the wiki index and log. ## Quick Start Paste an Instagram reel or carousel URL and ask to ingest it into the Content wiki, specifying whether the brand type is business or personal.
