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YouTube Competitive Analysis

byericosiu3KGitHub starsGitHub

Analyze any YouTube channel set, find outlier videos 2x+ above average, and extract winning title patterns. Data-driven competitive analysis in minutes.

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Result preview

Full Demo

See a data-driven competitive analysis report on AI Agent YouTube content, with channel benchmarks, high-performing outliers, and winning content patterns generated by this Agent Skill.

Get started

Run Your First Task

  1. a simple demonstration of the first step in using YouTube Competitive Analysis Skill
    01

    Install

    Add the YouTube Competitive Analysis Skill to your AI agent.

  2. a simple demonstration of the second step in using YouTube Competitive Analysis Skill
    02

    Describe Task

    Describe the YouTube channels or competitive analysis task you want to explore.

  3. a simple demonstration of the third step in using YouTube Competitive Analysis Skill
    03

    Review Analysis

    Get a competitive analysis report with outlier videos and winning content patterns.

Install command

$ npx skills add https://github.com/ericosiu/ai-marketing-skills/tree/main/yt-competitive-analysis

Installation signals

Installation activity

About

Competitive analysis on YouTube is critical for creators and marketers who want to grow. This tool analyzes any set of channels, automatically identifies outlier videos with 2x+ average views, and extracts the title patterns that make winners stand out, giving you a roadmap for your own content strategy.

Unlike manual spreadsheet tracking, this Python utility separates long-form from Shorts, calculates per-channel averages, and surfaces proven packaging formats—all in seconds. Predefined creator sets for AI and business niches let you benchmark instantly, while custom channel inputs and flexible exports (console, JSON, Google Sheets) fit any workflow.

Whether you're a content creator reverse-engineering competitors, a marketing agency preparing client reports, or a business owner tracking trends, the weekly automation via cron ensures you never miss a shift. It's built with zero external dependencies, runs on Python 3.8+, and uses the free YouTube API quota, so you can start today with minimal setup.

Key features

What makes it powerful

  • Real-Time Competitive Analysis

    Pull channel data and instantly identify videos that outperform, giving you a competitive edge.

  • Winning Pattern Extraction

    Extract title patterns and common words from outlier videos to replicate proven formats.

  • Channel Video Pulling

    Fetch recent videos from any set of YouTube channels automatically.

  • Flexible Export Options

    Output results to console, JSON, or Google Sheets for seamless integration.

  • Predefined Channel Sets

    Use built-in AI and business creator lists or customize your own for targeted analysis.

Use cases

When to reach for it

  • Analyze Competitor Channels

    Input any YouTube handles to uncover top-performing videos and competitive strategies.

  • Benchmark Against AI Creators

    Use built-in AI creator lists for quick competitive analysis of the AI niche.

  • Automate Weekly Reports

    Schedule via cron to receive updated outlier detection and patterns every week.

  • Export Insights to Your Stack

    Output to JSON or Google Sheets to feed competitive data into your reporting tools.

SKILL.md

YouTube Competitive Analysis

Find what's actually working on YouTube. Analyzes any set of channels, identifies outlier videos (2x+ average views), and extracts packaging patterns from winners.

No manual spreadsheet work. No guessing. Data-driven competitive intel in minutes.

What It Does

  1. Pulls recent videos from any YouTube channel(s)
  2. Separates long-form from Shorts
  3. Calculates per-channel averages
  4. Flags outliers (videos performing 2x+ above the channel average)
  5. Extracts title patterns from winners
  6. Exports to console, JSON, or Google Sheets

Quick Start

# Set your YouTube API key
export YOUTUBE_API_KEY="your-key-here"

# Analyze specific channels
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@AlexHormozi,@garyvee,@CodieSanchezCT"

# Use predefined channel sets
python3 analyze.py "$YOUTUBE_API_KEY" --set ai          # AI creator set
python3 analyze.py "$YOUTUBE_API_KEY" --set business    # Business creator set
python3 analyze.py "$YOUTUBE_API_KEY" --set both        # All channels

# Export to JSON for programmatic use
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json > results.json

# Custom lookback period
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@mkbhd" --days 60

Setup

  1. Get a YouTube Data API v3 key
  2. Set it as an environment variable: export YOUTUBE_API_KEY="your-key"
  3. Install dependencies: pip install -r requirements.txt

Output

Console (default)

================================================================================
  AI Creators — LAST 30 DAYS
================================================================================

📊 CHANNEL SUMMARY:
  Alex Hormozi             |  2,400,000 subs |  12 videos (3.0/wk) | L:  8 S:  4

🔥 LONG-FORM OUTLIERS (top 15):
  4.2x |  1,200,000 | [Alex Hormozi] How I'd Start a Business in 2025
       2025-01-15 | https://youtube.com/watch?v=...

📦 TOP TITLE PATTERNS:
   8x  business
   6x  money
   5x  started

JSON

Full structured data including all video metadata, scores, and multipliers.

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

Edit the channel dictionaries in analyze.py to customize.

Interpreting Results

MetricWhat It Means
MultiplierHow many times above channel average (2.0x = double normal)
Outlier threshold2x average. Videos above this are worth studying.
Title patternsCommon words in outlier titles = proven formats
CadenceVideos per week. Higher cadence may mean lower per-video averages.

Proven Packaging Formats

Based on outlier analysis, these title formats consistently overperform:

Long-form:

  • "X, Clearly Explained" (definitive explainer)
  • "X hours of Y in Z minutes" (condensed value)
  • "The Laziest Way to X" (low-effort promise)
  • "Give me X minutes and I'll Y" (time-boxed promise)
  • "X INSANE Use Cases for Y" (listicle + power word)

Shorts:

  • "2024 vs 2025 X" (year comparison)
  • "Bad Good Great X" (tier ranking)
  • "Stop doing X, do Y instead" (contrarian)

Weekly Automation

Run weekly to surface new outliers automatically:

# Cron: every Sunday at 8am
0 8 * * 0 YOUTUBE_API_KEY="your-key" python3 /path/to/analyze.py "$YOUTUBE_API_KEY" --set both --output json > /path/to/weekly-results.json

Requirements

  • Python 3.8+
  • YouTube Data API v3 key
  • No external Python dependencies (uses only stdlib)

License

MIT

FAQ