# YouTube Competitive Analysis > Analyze any YouTube channel set, find outlier videos 2x+ above average, and extract winning title patterns. Data-driven competitive analysis in minutes. - Canonical: https://nanoskill.ai/skills/youtube-competitive-analysis-skill - Markdown: https://nanoskill.ai/skills/youtube-competitive-analysis-skill.md - Author: ericosiu - Published: 2026-08-20T02:30:00.000Z - Updated: 2026-08-20T08:13:11.818Z - Language: en - Source type: github - Popularity signal: 2755 ## Sources - https://github.com/ericosiu/ai-marketing-skills/tree/main/yt-competitive-analysis ## Install ```shell npx skills add https://github.com/ericosiu/ai-marketing-skills/tree/main/yt-competitive-analysis ``` ## 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 - **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 - **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. ## Result preview 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. ![the demo of YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-demo-1.png) ![the demo of YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-demo-2.png) ![the demo of YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-demo-3.png) ![the demo of YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-demo-4.png) ## Result walkthrough ### Step 1: Install Add the YouTube Competitive Analysis Skill to your AI agent. ![a simple demonstration of the first step in using YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-step-1.png) ### Step 2: Describe Task Describe the YouTube channels or competitive analysis task you want to explore. ![a simple demonstration of the second step in using YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-step-2.png) ### Step 3: Review Analysis Get a competitive analysis report with outlier videos and winning content patterns. ![a simple demonstration of the third step in using YouTube Competitive Analysis Skill](https://file.nanoskill.ai/youtube-competitive-analysis-skill-step-3.png) ## Skill definition # 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 ```bash # 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](https://console.cloud.google.com/apis/api/youtube.googleapis.com) 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 | Metric | What It Means | |--------|--------------| | **Multiplier** | How many times above channel average (2.0x = double normal) | | **Outlier threshold** | 2x average. Videos above this are worth studying. | | **Title patterns** | Common words in outlier titles = proven formats | | **Cadence** | Videos 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: ```bash # 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 ### What is this YouTube competitive analysis tool? It's a Python-based utility that analyzes any YouTube channels, flags outlier videos performing 2x+ above average, and extracts title patterns to reveal what's working. ### How does the analysis work? It fetches recent videos via the YouTube API, calculates per-channel averages, then flags any video with views 2x+ above that average as an outlier, and counts word frequency in outlier titles. ### What do I need to run it? You need Python 3.8+, a YouTube Data API v3 key, and the channels you want to analyze. ### Is it free? Yes, the code is open-source under MIT license. You only need a YouTube API key, which comes with free daily quota. ### Can I use my own list of channels? Yes, pass custom handles with the --channels flag, or edit the dictionaries in analyze.py for permanent sets. ### How do I interpret the outlier multiplier? A multiplier of 2.0 means the video got twice the channel's average views. Higher multipliers indicate stronger performance.