youtube-video-analyst_skill

This skill analyzes YouTube transcripts to extract viral formulas, hooks, and retention strategies, enabling rapid creation of high-performing content
  • Python

5

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill shipshitdev/library --skill youtube-video-analyst

  • plugin.json345 B
  • SKILL.md9.5 KB

Overview

This skill performs forensic deconstruction of YouTube videos to extract viral formulas, hooks, retention mechanics, and emotional engineering. It converts transcripts into a modular blueprint you can reuse to clone high-performing patterns and build new content that reliably retains and converts.

How this skill works

Provide a video transcript or a YouTube URL (auto-fetch option available). The skill reads the full transcript, identifies hooks, maps pacing and emotional beats, then outputs reusable templates, timing guidance, and a ranked list of virality drivers. Results are actionable: fill-in-the-blank scripts, CTA sequences, and an implementation playbook.

When to use it

  • You want to reverse-engineer a viral YouTube video from its transcript
  • You need hook and retention templates to craft new videos
  • You want step-by-step timing and pacing guidance for max watch time
  • You need emotional triggers and language patterns to boost shares
  • You’re creating content across platforms and want a transferable formula

Best practices

  • Always provide niche, tone, platform, and target length before analysis
  • Use exact transcript text (auto-fetch or paste) for precise micro-pattern detection
  • Apply templates literally on first pass, then adapt voice and specificity
  • A/B test one variable at a time (hook, CTA, thumbnail alignment)
  • Track retention heatmaps and iterate based on real watch-time data

Example use cases

  • Analyze a 12-minute explainer to create a 60s teaser with the same hook
  • Extract three fill-in-the-blank hook variations to test in thumbnails
  • Map emotional arc of an industry talk to adapt for a marketing series
  • Generate a complete short-form script using the video’s retention mechanics
  • Produce CTA timing and objection handling for a product launch video

FAQ

Yes—full transcripts yield the most precise micro-patterns, though summaries can still produce high-level blueprints.

Can this be used for TikTok or Shorts?

Yes. The analysis adapts timing and hook intensity for short-form platforms while preserving core retention mechanics.

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