cross-niche-outliers

name	cross-niche-outliers
description	Find viral YouTube videos from adjacent business niches to extract content patterns and hooks. Use when user asks to find content inspiration, YouTube outliers, viral video patterns, or cross-niche content ideas.
allowed-tools	Bash, Read, Write, Edit, Glob, Grep

Cross-Niche Outlier Detection

Goal

Identify high-performing videos from adjacent business niches to extract transferable content patterns, hooks, and structures. These outliers provide inspiration for content ideation without being directly competitive.

Two Approaches

1. TubeLab API (RECOMMENDED)

# Default: 1 query = 5 credits, ~100 outliers from last 30 days
python3 ./scripts/scrape_cross_niche_tubelab.py

# Custom search term
python3 ./scripts/scrape_cross_niche_tubelab.py --terms "business strategy"

# Skip transcripts (faster, cheaper)
python3 ./scripts/scrape_cross_niche_tubelab.py --skip_transcripts

Pros: Pre-calculated scores, no rate limiting, fast Cons: 5 credits per query

2. yt-dlp Scraping (LEGACY)

python3 ./scripts/scrape_cross_niche_outliers.py

Use only if TubeLab credits are exhausted. Often fails due to rate limiting.

Scripts

• ./scripts/scrape_cross_niche_tubelab.py - TubeLab API (recommended)
• ./scripts/scrape_cross_niche_outliers.py - yt-dlp direct scraping
• ./scripts/generate_title_variants.py - Generate title variants for outliers

Process

1. Video Discovery

• Search keywords (50 videos per keyword)
• Monitor business channels (15 videos per channel)
• Deduplicate and filter noise

2. Outlier Scoring

• Base score: video views / channel average views
• Recency boost: <1 day = 2x, <3 days = 1.5x, <7 days = 1.2x
• Threshold: 1.1x or higher (10% above average)

3. Cross-Niche Scoring

Modifiers applied to base score:

• -20% per technical term (API, Python, code, SDK)
• +30% for money hooks ($, revenue, income, profit)
• +20% for time hooks (faster, productivity)
• +20% for curiosity gaps (?, "this changed everything")
• +10% for listicles (numbers in title)

4. Transcript & Summary

• Fetches transcript (youtube-transcript-api, Apify fallback)
• Claude summarizes: hook, structure, how to adapt
• Raw transcript saved for deeper analysis

5. Title Variant Generation

For each outlier, generates 3 title variants adapted to your niche.

6. Output to Google Sheet (19 columns)

Cross-Niche Score, Outlier Score, Days Old, Category, Title, Video Link, Views, Duration, Channel, Thumbnail, Summary, Title Variants 1-3, Raw Transcript, Publish Date, Source

TubeLab Options

Flag	Description	Default
--queries N	Number of searches (5 credits each)	1
--terms "a" "b"	Custom search terms	entrepreneur
--min_views N	Minimum views	10,000
--max_days N	Max video age	30
--skip_transcripts	Skip transcripts	False

Keyword Tiers

Tier 1: Adjacent Business/Tech

• "AI for business", "ChatGPT business use cases", "no-code automation"

Tier 2: Broad Business

• "scale your business", "solopreneur success", "founder productivity"

Tier 3: Money/Revenue Hooks

• "increase revenue", "passive income systems", "10x your income"

Monitored Channels

Alex Hormozi, My First Million, Starter Story, Colin and Samir, Ali Abdaal, Think Media, Iman Gadzhi, Pat Flynn, GaryVee, MrBeast, Justin Welsh, Charlie Morgan

Output

• Google Sheet: "Cross-Niche Outliers v2 - [timestamp]"
• ~100 outliers with 19 columns
• Sorted by publish date (most recent first)
• 3 title variants + raw transcript per outlier

Environment

TUBELAB_API_KEY=your_key
ANTHROPIC_API_KEY=your_key
APIFY_API_TOKEN=your_token (optional fallback)

Workflow

1. Run weekly for ~100 outliers
2. Review by Cross-Niche Score
3. Pick outlier with good thumbnail/title
4. Use title variants as starting points
5. Recreate thumbnail with your face (see recreate-thumbnails skill)