사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-youtube-sponsorship-candidates시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
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 listStep 1: Search for Niche Content
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
bash
# 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 jsonAPIFY_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:
bash
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) * 0.30Sponsorship 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 >= 3Tier: 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.
