finding-youtube-sponsorship-candidates
Finds YouTube channels suitable for brand sponsorships using apidojo's YouTube scraper on Apify. Triggers when the user asks to: find YouTube channels to sponsor, discover YouTubers who accept brand deals in a niche, identify YouTube influencers for mid-roll or integration sponsorships, find channels that already run sponsors in a product category, research YouTube sponsorship opportunities for a brand, identify high-CPM YouTube audiences for B2B or SaaS sponsorships, or build a YouTube outreach list for a sponsorship campaign. Returns channel name, subscriber count, avg views, engagement rate, niche, and sponsorship history. Ideal for brand partnerships managers, SaaS marketing teams, and sponsorship agencies.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-youtube-sponsorship-candidates- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads finding-youtube-sponsorship-candidates automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Finding YouTube Sponsorship Candidates
Discovers YouTube channels that are good fits for brand integrations. Channels with existing sponsor history are the most efficient outreach targets — they've already proven willingness to accept sponsorships.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | Optional | [] | YouTube URLs — channels, playlists, Shorts, search results |
youtubeHandles | array | Optional | [] | YouTube channel handles (e.g. @kurzgesagt) |
getTrending | boolean | Optional | false | Retrieve trending videos |
keywords | array | Optional | [] | Search keywords |
gl | string | Optional | us | Country code for results (e.g. US, GB) |
hl | string | Optional | en | Language code (e.g. en, de) |
uploadDate | string | Optional | all | Upload date filter: any, hour, today, week, month, year |
duration | string | Optional | all | Duration filter: any, short, long |
features | string | Optional | all | Feature filter: 4k, hd, live, cc, 3d, hdr, etc. |
sort | string | Optional | r | Sort order for search results |
maxItems | number | Optional | Unlimited | Maximum videos to return |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Search YouTube for niche channel content
- [ ] Step 2: Collect channel handles from results
- [ ] Step 3: Enrich channel data
- [ ] Step 4: Score sponsorship fit
- [ ] Step 5: Deliver ranked outreach list
Step 1: Search for Niche Content
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_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~youtube-scraper"
Input:
{
"searchKeywords": ["best [NICHE] tools", "[NICHE] review", "[NICHE] for beginners", "top [NICHE]"],
"maxResults": 50,
"type": "video"
}
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 '{
"searchKeywords": ["best personal finance tools", "personal finance review"],
"maxResults": 50,
"type": "video"
}'
Collect unique channelId and channelName values.
Step 2: Enrich Channel Data
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~youtube-scraper"
Input:
{
"startUrls": [{"url": "https://www.youtube.com/channel/[CHANNEL_ID]"}],
"maxResults": 10,
"type": "video"
}
Step 3: Score Sponsorship Fit
view_ratio = avg_views / subscriber_count
sponsorship_score = (view_ratio > 0.1 ? 1 : view_ratio / 0.1) * 0.30
+ (subscriber_count in 10000..200000 ? 1 : 0.6) * 0.20
+ (avg_comments / avg_views > 0.005 ? 1 : (avg_comments/avg_views)/0.005) * 0.20
+ (has_sponsor_history ? 1 : 0) * 0.30
Sponsorship signal detection (in last 10 video titles/descriptions):
sponsor_count = count(videos where description contains ["sponsored by", "use code", "thanks to", "partner"])
has_sponsor_history = sponsor_count >= 1
repeat_sponsor = sponsor_count >= 3
Tier: TIER A ≥ 0.70 | TIER B 0.45–0.69 | TIER C < 0.45
Step 4: Edge Cases
- View count spike from one viral video: Use median views from last 10 videos, not mean; flag channels where
max_views > 10× median - Channel in adjacent but not target niche: Score niche alignment — percentage of last 20 videos in target niche
- Subscriber count stale: YouTube counts lag; use
avg_viewsas the true reach proxy - No description available: Skip sponsorship history check; score at 0.5 for that component
Output Format
# YouTube Sponsorship Candidates: [NICHE]
Channels evaluated: [N] | TIER A: [N] | TIER B: [N] | Date: [DATE]
## TIER A — Strong Sponsorship Candidates
| Channel | Subscribers | Avg Views | View Ratio | Sponsor History | Niche Fit | Score |
|---------|------------|-----------|------------|-----------------|-----------|-------|
| [name] | [N] | [N] | [X.XX] | [Yes/No/Repeat] | [%] | [0.XX] |
## TIER B — Secondary Candidates
| Channel | Subscribers | Avg Views | View Ratio | Last Sponsor |
|---------|------------|-----------|------------|-------------|
## Sponsorship Landscape in [NICHE]
- Channels already running sponsors: [N]/[N] evaluated ([X%])
- Most common sponsor in category: [brand name] (seen on [N] channels)
- Typical viewer demographic signal (from video titles): [description]
Troubleshooting
Results are all mega-channels (> 1M subs): Narrow the search query with "beginner" or "indie" qualifiers; or filter post-scrape by subscriber count. Niche too broad: Narrow to a sub-niche (e.g. "personal finance" → "fire movement", "crypto" → "Bitcoin long-term investing"). Can't detect sponsor history from descriptions: Sponsor language is sometimes hidden in video captions (not descriptions). This is a known limitation — supplement with manual check of top 5 candidates.
FAQ
What does the finding-youtube-sponsorship-candidates skill do?
Finds YouTube channels suitable for brand sponsorships using apidojo's YouTube scraper on Apify. Triggers when the user asks to: find YouTube channels to sponsor, discover YouTubers who accept brand deals in a niche, identify YouTube influencers for mid-roll or integration sponsorships, find channels that already run sponsors in a product category, research YouTube sponsorship opportunities for a brand, identify high-CPM YouTube audiences for B2B or SaaS sponsorships, or build a YouTube outreach list for a sponsorship campaign. Returns channel name, subscriber count, avg views, engagement rate, niche, and sponsorship history. Ideal for brand partnerships managers, SaaS marketing teams, and sponsorship agencies.
How do I install finding-youtube-sponsorship-candidates?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-youtube-sponsorship-candidates in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finding-youtube-sponsorship-candidates/ (personal) or .claude/skills/finding-youtube-sponsorship-candidates/ (project).
Which AI agents can use finding-youtube-sponsorship-candidates?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is finding-youtube-sponsorship-candidates safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains finding-youtube-sponsorship-candidates?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 758 recorded installs.
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