YouTube Competitive Analysis skill
Analyze YouTube channels for outlier videos and packaging patterns.
by ericosiu·MIT license·★ 3,615 Stars on the repo·GitHub ↗
Use now
npx degit ericosiu/ai-marketing-skills/yt-competitive-analysis#main ~/.claude/skills/yt-competitive-analysisChecked ·commit main
Files of YouTube Competitive Analysis
SKILL.md
Show the full text88 lines
YouTube Competitive Analysis
Outlier detection and packaging pattern extraction for YouTube channels.
When to Use
- User asks for YouTube competitive analysis
- User wants to find viral video patterns
- User wants packaging/title inspiration from specific creators
- User wants to track competitor YouTube performance
Prerequisites
- YouTube Data API v3 key set as
$YOUTUBE_API_KEY
Usage
# Analyze specific channels
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30
# Use predefined sets
python3 analyze.py "$YOUTUBE_API_KEY" --set ai
python3 analyze.py "$YOUTUBE_API_KEY" --set business
python3 analyze.py "$YOUTUBE_API_KEY" --set both
# Export formats
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output console
Predefined Channel Sets
AI Creators: Jeff Su, Alex Finn, Riley Brown, Dan Martell, Matt Wolfe, Nate Herk, Grace Leung, Matt Berman
Business Creators: Alex Hormozi, Gary Vaynerchuk, Patrick Bet-David, Codie Sanchez, Leila Hormozi, Iman Gadzhi, My First Million
Output Interpretation
- Multiplier: Times above channel average (2.0x = double normal)
- Outlier threshold: 2x average. Study anything above this.
- Title patterns: Common words in outlier titles indicate proven formats
- Cadence: Videos per week. Higher cadence creators may have lower per-video averages.
Channel Analytics Feedback Loop
Competitive analysis is only half the loop. When you have access to the channel's own analytics, compare candidate packaging against actual performance after publish.
Before recommending a package:
- Pull channel baseline by topic, title pattern, thumbnail pattern, length, publish day/time, and format.
- Check impressions, CTR, average view duration, retention curve, watch time, subscribers gained, comments, and traffic source.
- Compare the proposed title/thumbnail/hook against similar historical videos and competitor outliers.
After publishing:
- Log video ID, title, thumbnail concept, hook, topic bucket, retention devices, and publish date.
- Pull analytics after the chosen readback window.
- Compare baseline vs candidate.
- Patch title formulas, thumbnail rules, hook patterns, retention beats, chapter structure, Shorts selection, or repurposing guidance only if the candidate wins.
Readback windows:
- 24-48 hours for CTR and early retention
- 7 days for average view duration and watch time
- 28 days for topic durability and subscriber gain
Do not call packaging validated until analytics are checked. Otherwise it is a screenplay wearing a lab coat.
Packaging Skeletons (Proven Formats)
Long-form:
- "X, Clearly Explained"
- "X hours of Y in Z minutes"
- "The Laziest Way to X"
- "Give me X minutes and I'll Y"
- "X INSANE Use Cases for Y"
Shorts:
- "2024 vs 2025 X" (year comparison)
- "Bad Good Great X" (tier ranking)
- "Stop doing X, do Y instead" (contrarian)
| 1 | |
| 2 | name yt-competitive-analysis |
| 3 | description >- |
| 4 | Analyze YouTube channels for outlier videos and packaging patterns. Identifies |
| 5 | what's working (2x+ average views) across any set of channels. Use when asked for |
| 6 | YouTube competitive analysis, viral video patterns, or packaging/title inspiration. |
| 7 | |
| 8 | |
| 9 | # YouTube Competitive Analysis |
| 10 | |
| 11 | Outlier detection and packaging pattern extraction for YouTube channels. |
| 12 | |
| 13 | ## When to Use |
| 14 | |
| 15 | User asks for YouTube competitive analysis |
| 16 | User wants to find viral video patterns |
| 17 | User wants packaging/title inspiration from specific creators |
| 18 | User wants to track competitor YouTube performance |
| 19 | |
| 20 | ## Prerequisites |
| 21 | |
| 22 | YouTube Data API v3 key set as `$YOUTUBE_API_KEY` |
| 23 | |
| 24 | ## Usage |
| 25 | |
| 26 | |
| 27 | # Analyze specific channels |
| 28 | python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30 |
| 29 | |
| 30 | # Use predefined sets |
| 31 | python3 analyze.py "$YOUTUBE_API_KEY" --set ai |
| 32 | python3 analyze.py "$YOUTUBE_API_KEY" --set business |
| 33 | python3 analyze.py "$YOUTUBE_API_KEY" --set both |
| 34 | |
| 35 | # Export formats |
| 36 | python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json |
| 37 | python3 analyze.py "$YOUTUBE_API_KEY" --set both --output console |
| 38 | |
| 39 | |
| 40 | ## Predefined Channel Sets |
| 41 | |
| 42 | **AI Creators:** Jeff Su, Alex Finn, Riley Brown, Dan Martell, Matt Wolfe, Nate Herk, Grace Leung, Matt Berman |
| 43 | |
| 44 | **Business Creators:** Alex Hormozi, Gary Vaynerchuk, Patrick Bet-David, Codie Sanchez, Leila Hormozi, Iman Gadzhi, My First Million |
| 45 | |
| 46 | ## Output Interpretation |
| 47 | |
| 48 | **Multiplier**: Times above channel average (2.0x = double normal) |
| 49 | **Outlier threshold**: 2x average. Study anything above this. |
| 50 | **Title patterns**: Common words in outlier titles indicate proven formats |
| 51 | **Cadence**: Videos per week. Higher cadence creators may have lower per-video averages. |
| 52 | |
| 53 | ## Channel Analytics Feedback Loop |
| 54 | |
| 55 | Competitive analysis is only half the loop. When you have access to the channel's own analytics, compare candidate packaging against actual performance after publish. |
| 56 | |
| 57 | Before recommending a package: |
| 58 | Pull channel baseline by topic, title pattern, thumbnail pattern, length, publish day/time, and format. |
| 59 | Check impressions, CTR, average view duration, retention curve, watch time, subscribers gained, comments, and traffic source. |
| 60 | Compare the proposed title/thumbnail/hook against similar historical videos and competitor outliers. |
| 61 | |
| 62 | After publishing: |
| 63 | Log video ID, title, thumbnail concept, hook, topic bucket, retention devices, and publish date. |
| 64 | Pull analytics after the chosen readback window. |
| 65 | Compare baseline vs candidate. |
| 66 | Patch title formulas, thumbnail rules, hook patterns, retention beats, chapter structure, Shorts selection, or repurposing guidance only if the candidate wins. |
| 67 | |
| 68 | Readback windows: |
| 69 | 24-48 hours for CTR and early retention |
| 70 | 7 days for average view duration and watch time |
| 71 | 28 days for topic durability and subscriber gain |
| 72 | |
| 73 | Do not call packaging validated until analytics are checked. Otherwise it is a screenplay wearing a lab coat. |
| 74 | |
| 75 | ## Packaging Skeletons (Proven Formats) |
| 76 | |
| 77 | **Long-form:** |
| 78 | "X, Clearly Explained" |
| 79 | "X hours of Y in Z minutes" |
| 80 | "The Laziest Way to X" |
| 81 | "Give me X minutes and I'll Y" |
| 82 | "X INSANE Use Cases for Y" |
| 83 | |
| 84 | **Shorts:** |
| 85 | "2024 vs 2025 X" (year comparison) |
| 86 | "Bad Good Great X" (tier ranking) |
| 87 | "Stop doing X, do Y instead" (contrarian) |
| 88 |
Discussion
Alternatives
Company valuationEstimate the intrinsic value of a public company using DCF, relative (peer multiple) and sum-of-parts (SOTP) methods, then triangulate to an implied share price with upside/downside versus the current market price. Use this skill whenever the user asks: what is AAPL worth", "valuation of NVDA", "fair value of TSLA", "intrinsic value", DCF for MSFT", "build a DCF", "discounted cash flow", "WACC", "terminal value", implied share price", "upside to fair value", "is X overvalued/undervalued", relative valuation", "peer comparison valuation", "EV/EBITDA target", "SOTP", sum of the parts", "how much is [company] worth", "price target from fundamentals", value this company", or any ticker in the context of computing intrinsic or relative valuation. Default to running ALL three methods (DCF + relative + SOTP-if-applicable) and presenting a blended implied price with a sensitivity table. Do not answer valuation questions from memory — always run the workflow.
Competitor gapFind keywords a competitor ranks for that the user's site doesn't — the content gap, prioritized by volume and winnability. Use when asked "what does competitor.com rank for", "keyword gap vs X", "steal competitor keywords", or "why do they outrank us".
Ad angle minerMine the highest-converting ad angles from customer reviews, Reddit complaints, support tickets, and competitor ads. Extracts actual pain language, competitor weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank with proof quotes and recommended ad formats per angle.
X audience insightsRead your X (Twitter) audience and niche from real data. Pull a handle's recent tweets (yours or a competitor's) with likes, replies, and views, see which formats and hooks are working, read the repliers on a tweet (X gates likers, so repliers are the signal), and scan a niche query for top tweets. Powered by Apify, no login. Triggers on "analyze my tweets", "what is working on X", "read the replies", "competitor tweets", "who is engaging". Not for writing a tweet (use x-post-writer).Browse more free Claude skills or everything in Product.