cdeistopened YouTube Outlier Finder
Identify what's working on YouTube right now for any topic. Inspired by VidIQ's outlier score — but with pattern analysis on top.
Usage
/yt-outlier claude code skills
/yt-outlier sourdough bread baking
/yt-outlier "homeschool curriculum reviews" --window month
Core Concept: The Outlier Score
Outlier Score = Video Views / Channel Subscriber Count
A video with 100K views on a 10K-sub channel (10x) is a much stronger signal than 100K views on a 1M-sub channel (0.1x). The outlier score reveals which topics and title formats are punching above their weight — regardless of channel size.
| Score | Meaning |
|---|---|
| < 1x | Underperforming (below channel average) |
| 1-2x | Normal performance |
| 2-5x | Above average — topic resonated |
| 5-10x | Strong outlier — title/topic hit a nerve |
| 10x+ | Viral breakout — study this title closely |
Workflow
Step 1: Scrape YouTube Search Results
Primary method: yt-dlp (free, no API key, no rate limits)
# Search and get video metadata as JSON
yt-dlp "ytsearch30:<USER_KEYWORD>" --flat-playlist --dump-json 2>/dev/null
# Get subscriber count for a specific channel (flat-playlist doesn't include subs)
yt-dlp "https://www.youtube.com/@ChannelHandle" --playlist-items 1 --dump-json 2>/dev/null
Parse the JSON for: title, view_count, channel, upload_date, thumbnails[-1].url, webpage_url.
Note: --flat-playlist is fast but returns 0 for channel_follower_count. For outlier scores, you need a separate lookup per unique channel. Batch the top ~10 channels, not all 30.
Thumbnail URLs come from the thumbnails array in yt-dlp JSON. Use the last entry (highest resolution). Download with curl for visual analysis.
Fallback method: Apify YouTube scraper (if available, but check monthly limit first)
Tool: mcp__apify__streamers-slash-youtube-scraper
Input:
searchQueries: ["<USER_KEYWORD>"]
maxResults: 50
dateFilter: <WINDOW> (default: "month", options: "week", "month", "year")
sortingOrder: "relevance"
Time window mapping:
- "2 weeks" → use
dateFilter: "month"then filter by date in analysis - "1 month" →
dateFilter: "month" - "3 months" →
dateFilter: "year"then filter by date in analysis
If the keyword is broad, run a second search with a more specific variant to capture the long tail. For example, if the user searches "claude code", also try "claude code tutorial" or "claude code skills".
Step 2: Compute Outlier Scores
For each video returned, calculate:
outlier_score = video_views / channel_subscribers
Filter out:
- Videos with 0 subscriber count (data unavailable)
- Official brand channels (Anthropic, OpenAI, etc.) — they skew the data
- Non-English results (unless the user is targeting a specific language market)
- Videos clearly unrelated to the topic (noise from broad search)
Sort by outlier score descending.
Step 3: Analyze Title Patterns
Categorize every title in the top 30 by format. Look for these patterns:
| Pattern | Example | Signal |
|---|---|---|
| Listicle | "9 automations + 4 builds that work" | High outlier avg in test data |
| Time constraint | "in 18 minutes", "in 35 minutes" | Specificity = credibility |
| Cost/FREE | "FREE Forever", "Stop Paying $200/mo" | Strong for small channels |
| Credibility hook | "(Meta Staff Engineer Tips)" | Authority + curiosity |
| Build result | "I Built 5 Websites", "Trading Bot" | Concrete output > vague promise |
| Superlative | "INSANE", "DESTROYED" | Overused, low outlier avg |
| Negative/contrarian | "don't install", "has a big problem" | Works for big channels only |
| Comparison | "vs", "which is better" | Decision-stage viewers |
| Tutorial/How-to | "Full Guide", "Step-by-Step" | High volume, moderate outlier |
| Dollar claim | "$273/Day", "$450K Campaign" | Money = clicks (use carefully) |
Count how many of the top 30 titles fall into each pattern. Compute average outlier score per pattern.
Step 4: Surface Insights
Generate the report with these sections:
1. Top 15 Outliers Table
Score | Views | Subs | Title | Channel
21.6x | 122,089 | 5,640 | I Built 5 Websites in 18 Minutes... | Luke Carter
2. Pattern Scorecard
Format | Count | Avg Outlier | Avg Views
Listicle (N ways/tips/cases) | 4 | 9.3x | 131,834
Time constraint (N minutes) | 6 | 6.9x | 192,334
Rank patterns by average outlier score, not count.
3. Channel Size Breakdown
Group videos into Small (<50K subs), Mid (50K-500K), Big (500K+):
- Which tier is producing the most outliers?
- What patterns work at each tier?
4. Thumbnail Analysis
Download thumbnails for the top 10 outliers and analyze visually using Claude's image reading:
# Download thumbnail
curl -s -o "channel-title-slug.jpg" "<THUMBNAIL_URL>"
Then read each image and categorize by pattern:
| Pattern | Description | When It Works |
|---|---|---|
| Face + Bold Text | Close-up emotional face, 3-5 word overlay, color-blocked key word | Big channels, interview formats |
| Minimal Face + Short Phrase | Approachable person, simple text, soft background | Small/mid channels building trust |
| Nature/Lifestyle Scene | Calming environment, minimal text, aesthetic > personality | Breathwork, meditation, wellness |
| Provocative/Edgy | Warning labels, psychedelic overtones, shirtless intensity | Established brands, edgy niches |
| Pure Illustration | No face, text-only or animated, clean design | Utility content (follow-along exercises) |
Note: color palette, text placement, facial expression, and whether the thumbnail "matches" the target audience.
5. Content Gap Analysis
Look for what's NOT being covered well:
- Topics mentioned but with weak titles/low production
- Angles that exist in 1-2 videos but not widely covered
- Audience questions visible in titles that nobody's answering well
5. Title Ideas
Based on the top-performing patterns, generate 5-10 concrete title ideas for the user. Combine the winning patterns:
- Listicle + time constraint: "7 Claude Code Skills You Can Build in 20 Minutes"
- Credibility + build result: "I Automated My Entire Workflow (Here's the Setup)"
- Cost angle + specific: "Stop Paying for [Tool] — Claude Code Does It Free"
Important: Title ideas should reflect the USER'S channel size and niche, not just copy what big channels do. Negative hooks work at 500K+ subs but not for small channels. Listicles and time constraints work at every level.
Step 5: Cross-Reference with Niche Scout (Optional)
If the user is also evaluating this topic for a book:
"This topic also has strong KDP potential. Run
/niche-scout <keyword>to check Amazon demand and BSR competition."
If the YouTube data shows high engagement but the topic is very competitive on Amazon, that's a signal to use YouTube as the distribution channel and monetize differently (course, affiliate, community).
Interpreting Results
Strong niche signals:
- Multiple small channels (<50K) achieving 5x+ outlier scores
- Consistent demand across multiple title formats
- Viewers engaging with both tutorial AND opinion content (mature audience)
Weak niche signals:
- Only big channels (500K+) getting views (brand-driven, not topic-driven)
- All outliers are "reaction" or news content (no staying power)
- Low total video count (nobody's making content = might mean nobody's watching)
Red flags:
- All top videos are from one channel (not a niche, it's a creator)
- Outlier scores cluster at 1x or below (topic is saturated)
- Views are high but engagement (likes/comments) is very low (clickbait, not real interest)
Tool Dependencies
- Apify — YouTube scraper via MCP (
streamers/youtube-scraper), APIFY_TOKEN in env - Claude analysis — Pattern recognition, categorization, title generation (no external tool needed)
Related Skills
- niche-scout — Evaluate the same keyword on Amazon KDP (book potential)
- deep-research — For deeper competitive analysis on borderline niches
- skill-extractor — If video transcripts reveal skills worth building