YouTube Gap Finder

Finds underserved content topics in any YouTube niche — topics where competitors get high views but the overall supply of videos is low.

What it does

This Actor analyses YouTube competitor channels and niche search results to identify content gaps: topics with proven audience demand (high competitor views) but low competition (few videos/channels covering them).

Each result includes:

• Topic — 2-3 word phrase representing the gap
• Opportunity score — 0–10 combining demand signal and competition level
• Competition level — low, medium, or high
• Suggested video title — a ready-to-use title for the gap
• Example competitor titles — existing videos that prove demand

Sample output

{
  "topic": "budgeting apps review",
  "opportunity_score": 7.8,
  "competition": "low",
  "avg_niche_views": 420000,
  "competitor_videos_on_topic": 3,
  "niche_videos_on_topic": 1,
  "niche_channels_on_topic": 1,
  "suggested_title": "Best Budgeting Apps Review 2026 (The Underrated Guide)",
  "example_competitor_titles": [
    "Best budgeting apps review 2026",
    "Top 5 budgeting apps for beginners"
  ]
}

Input parameters

Parameter	Type	Default	Description
niche	string	""	The content niche (e.g. personal finance, Python programming)
competitor_channels	array	[]	YouTube channel URLs to analyse as competitors
niche_search_queries	array	[]	Extra search queries to build the competition dataset
max_competitor_videos	integer	30	Max videos to fetch per competitor channel
max_niche_videos	integer	50	Total niche search videos for competition analysis
min_opportunity_score	number	4.0	Minimum score to include in results
max_results	integer	30	Maximum gaps to return

How it works

1. Competitor analysis — fetches recent videos from the provided competitor channels using yt-dlp
2. Topic extraction — extracts 2-3 word topic phrases from competitor video titles, filtering stopwords
3. Competition mapping — for each topic, counts how many videos and channels in the broader niche cover it
4. Gap scoring — scores each topic: demand (avg competitor views) + low competition bonus → 0-10
5. Output — returns gaps sorted by opportunity score with a suggested title for each

Use cases

• Content creators — find your next video idea before the niche gets saturated
• YouTube strategists — identify whitespace in a client's niche
• Agencies — audit competitor coverage vs. niche supply for channel growth reports
• Marketers — discover high-demand, low-competition topics for YouTube ads targeting

Algorithm details

Opportunity score formula:

• Demand (0–5): min(avg_niche_views / 100,000, 1.0) × 5
• Competition bonus (0–5): 4.0 for 0 niche videos, down to 0.0 for 10+ niche videos
• Final score capped at 10.0

Competition level:

• low — ≤1 channel AND ≤3 videos on the topic
• medium — ≤3 channels OR ≤8 videos
• high — more than the above

Running locally

python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
python main.py

Custom input:

$ACTOR_INPUT='{"niche":"personal finance","competitor_channels":["https://www.youtube.com/@GrahamStephan"],"max_competitor_videos":20}' python main.py

Notes

• Uses yt-dlp to fetch YouTube metadata — no API key required
• Channel videos are fetched in flat/extract mode (no downloads)
• yt-dlp must be kept up to date (pip install -U yt-dlp) as YouTube periodically changes its internal API
• A ~1s delay is applied between requests to avoid rate limiting