Reel Deep Dive

The problem

Understanding why a video went viral is usually guesswork. Manually breaking down transcripts to find hook mechanics, retention patterns, and pacing metrics is time-consuming for content teams and growth engineers.

What it does

• Cleans raw transcripts by removing timestamps, filler words like [Music] or [Applause], and formatting speaker blocks.
• Performs forensic analysis on hooks to identify specific emotional triggers, promises, and curiosity gaps.
• Maps retention peaks to specific timestamps or narrative shifts to identify structural patterns.
• Calculates pacing metrics including average sentence length, question density, and topic shifts per minute.
• Extracts viral formulas and replication requirements to turn successful content into repeatable templates.

Why this beats prompting it yourself

Generic prompts often result in vague summaries rather than actionable data. This skill enforces a structured forensic framework that extracts concrete metrics like words-per-sentence and specific rhetorical devices, ensuring you get a technical breakdown rather than a high-level overview.

Use cases

• Reverse-engineer high-performing competitor videos for content strategy.
• Clean up messy automated transcripts for documentation or blog repurposing.
• A/B test hook variations by comparing emotional engineering across multiple transcripts.
• Build a library of viral templates based on proven content structures.

Known limitations

This skill processes text only. It cannot download video files, analyze visual editing, or interpret on-screen text not present in the transcript.