- Home
- Skills
- Shipshitdev
- Library
- Youtube Video Analyst
youtube-video-analyst_skill
- 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.