Channel Outlier Finder

YouTube Competitive Analysis

by ericosiu · MIT license · ★ 3,615 on GitHub

YouTube Competitive Analysis cover

Analyze YouTube channels for outlier videos and packaging patterns. Identifies what's working (2x+ average views) across any set of channels.

---
name: yt-competitive-analysis
description: >-
  Analyze YouTube channels for outlier videos and packaging patterns. Identifies
  what's working (2x+ average views) across any set of channels. Use when asked for
  YouTube competitive analysis, viral video patterns, or packaging/title inspiration.
---

# 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

Overview

Outlier detection and packaging pattern extraction for YouTube channels.

What you get

  • MultiplierTimes above channel average (2.0x = double normal)
  • Outlier threshold2x average. Study anything above this.
  • Title patternsCommon words in outlier titles indicate proven formats
  • CadenceVideos per week. Higher cadence creators may have lower per-video averages.
Manual install (npx, ZIP)
Claude Codenpx skills add ericosiu/ai-marketing-skills --skill yt-competitive-analysis -g -a claude-code
Claude Code (degit)npx degit ericosiu/ai-marketing-skills/yt-competitive-analysis#main ~/.claude/skills/yt-competitive-analysis
Claude.ai / desktopZip the folder and upload it under Settings → Capabilities → Skills.
Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

Files included

  • SKILL.md3.2 KB
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SKILL.md
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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:

  1. Log video ID, title, thumbnail concept, hook, topic bucket, retention devices, and publish date.
  2. Pull analytics after the chosen readback window.
  3. Compare baseline vs candidate.
  4. 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---
2name: yt-competitive-analysis
3description: >-
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 
11Outlier 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 `$YOUTUBE_API_KEY`
23 
24## Usage
25 
26```bash
27# Analyze specific channels
28python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30
29 
30# Use predefined sets
31python3 analyze.py "$YOUTUBE_API_KEY" --set ai
32python3 analyze.py "$YOUTUBE_API_KEY" --set business
33python3 analyze.py "$YOUTUBE_API_KEY" --set both
34 
35# Export formats
36python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json
37python3 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 
55Competitive 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 
57Before 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 
62After publishing:
631. Log video ID, title, thumbnail concept, hook, topic bucket, retention devices, and publish date.
642. Pull analytics after the chosen readback window.
653. Compare baseline vs candidate.
664. Patch title formulas, thumbnail rules, hook patterns, retention beats, chapter structure, Shorts selection, or repurposing guidance only if the candidate wins.
67 
68Readback 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 
73Do 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 
View all files on GitHub →

Details

License
MIT
Stars on the repo
★ 3,615
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Source
ericosiu/ai-marketing-skills ↗
Creator
ericosiu · 34 listings
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