Imported from apidojo-io/apidojo-skills (skills/intent/finding-youtube-micro-influencers/SKILL.md). Install upstream with npx skills add apidojo-io/apidojo-skills --skill finding-youtube-micro-influencers. Copyright stays with the author (Apache-2.0).

Finding YouTube Micro-Influencers

Searches YouTube for channels in a specific niche, then filters by subscriber count to surface micro-influencers (typically under 100K subscribers) who have high engagement relative to their audience size. Ideal for affiliate programs and brand sponsorships where micro audiences convert better.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Workflow

Progress:
- [ ] Step 1: Define niche and subscriber range
- [ ] Step 2: Build keyword and search term list
- [ ] Step 3: Run youtube-scraper
- [ ] Step 4: Filter and calculate view-to-sub ratio
- [ ] Step 5: Deliver ranked channel list

Step 1: Clarify Parameters

Ask the user for:

• Niche/topic (e.g., "personal finance for millennials", "vegan cooking", "PC gaming")
• Max subscribers (default: 100,000 — micro-influencer threshold)
• Min subscribers (default: 5,000 — filters out very new channels)
• Min videos (default: 20 — ensures established presence)
• Number of channels to return (default: 30)

Step 2: Build Search Term List

Generate 3-5 YouTube search queries for the niche. Think like a viewer, not a marketer:

• Personal finance → "how to save money", "budget for beginners", "investing in your 20s"
• Vegan cooking → "easy vegan recipes", "vegan meal prep", "vegan on a budget"
• PC gaming → "budget PC build", "PC gaming tips", "indie game reviews"

Step 3: Run the Actor

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~youtube-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~youtube-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~youtube-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~youtube-scraper"
Input:
{
  "searchQuery": "[SEARCH_TERM]",
  "maxResults": 50,
  "scrapeVideoDetails": false,
  "scrapeChannelDetails": true
}

Run for each search query. Merge channel results, deduplicate by channel ID.

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~youtube-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQuery": "[SEARCH_TERM]",
    "maxResults": 50,
    "scrapeChannelDetails": true
  }'

Wait for SUCCEEDED. Fetch dataset.

Step 4: Filter and Score

Apply subscriber range filter. Then calculate view-to-subscriber ratio as the micro-influencer quality signal:

view_ratio = (total_views / subscriber_count)
recent_momentum = avg_views_last_10_videos / subscriber_count

High recent_momentum (>0.5) = channel punching above its weight — ideal micro-influencer.

Step 5: Format Output

Output Format

# YouTube Micro-Influencer Discovery: [NICHE]
Subscriber range: [MIN]–[MAX] | Results: [N] channels | Date: [DATE]

| # | Channel | Subscribers | Avg Views | View/Sub Ratio | Videos | Contact | URL |
|---|---------|-------------|-----------|----------------|--------|---------|-----|
| 1 | [name]  | [N]         | [N]       | [X.Xx]         | [N]    | [email?]| [url] |

## Top 5 Picks for Outreach

1. **[Channel Name]** — [N] subs | [X.Xx] view ratio | Posts [X]x/month
   Why: [brief reason — high engagement, relevant niche, consistent posting]
   Contact: [email from About page if available]
   Latest video: "[title]" — [N] views

2–5. [same structure]

## Notes
- [N] channels found total across all search queries
- [N] filtered out (outside subscriber range or too few videos)
- View/Sub Ratio > 1.0 = exceptional engagement (views exceed subscriber count per video avg)

Troubleshooting

Only large channels in results: YouTube search ranks by relevance + views, surfacing big channels first. Try more niche-specific search terms or filter more aggressively on subscriber count. No contact info: Most YouTubers list business email in the About tab. If missing from scraper output, check the channel URL manually. Stale subscriber counts: YouTube data on Apify can be 24-72h old. For final outreach list, verify top picks manually.