How to use
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npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-youtube-content-strategyBefore you start
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Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
Analyzing Competitor YouTube Content Strategy
Reverse-engineers a competitor's YouTube channel by analyzing their last 20-50 videos. Identifies which video topics, formats, and lengths drive the most views and engagement.
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: Scrape competitor's recent videos
- [ ] Step 2: Classify video types and topics
- [ ] Step 3: Calculate performance metrics
- [ ] Step 4: Identify patterns and top performers
- [ ] Step 5: (Optional) Compare with own channel
- [ ] Step 6: Deliver strategy reportStep 1: Scrape Channel
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:
{
"startUrls": [{"url": "[COMPETITOR_CHANNEL_URL]"}],
"maxResults": 30,
"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 '{"startUrls": [{"url": "https://www.youtube.com/@competitorhandle"}], "maxResults": 30, "type": "video"}'Step 2: Classify Videos
video_type:
TUTORIAL = title contains "how to", "step by step", "guide", "tutorial"
LIST = title contains "top [N]", "[N] best", "[N] things", "mistakes"
REVIEW = title contains "review", "tested", "worth it", "vs"
THOUGHT_LEADERSHIP = opinion, trend analysis, "the future of", "why"
NEWS = title contains news, announcement, breaking
CASE_STUDY = "how [brand] grew", "inside", "behind the scenes"
video_length_tier:
SHORT = < 5 min
MEDIUM = 5–15 min
LONG = 15–30 min
DEEP_DIVE = > 30 minStep 3: Performance Metrics
view_ratio = viewCount / subscriberCount # corrected by subscriberCount at time of analysis
engagement_rate = (likeCount + commentCount) / viewCount * 100
publish_cadence = total_videos / (date_range_weeks) # videos per weekTop performer: Sort by viewCount; also identify hidden gems where engagement_rate > 2× channel average despite lower views.
Step 4: Edge Cases
- Channel is very new (< 6 months, < 20 videos): Reduce
videos_to_analyzeto all available; note limited sample - Views are all very low (< 1K per video): Channel may be struggling or niche is very small; provide absolute numbers, not just ratios
- One mega-viral video skews averages: Report median views alongside mean; flag outlier
- Channel posts in multiple languages: Group by language; analyze each cohort separately
Output Format
# Competitor YouTube Strategy: [CHANNEL_NAME]
Videos analyzed: [N] | Subscribers: [N] | Avg Views: [N] | Avg Eng Rate: [X%] | Date: [DATE]
## Content Mix
| Video Type | % of Videos | Avg Views | Avg Eng Rate | Best Example |
|-----------|------------|-----------|-------------|-------------|
| Tutorial | [X%] | [N] | [X%] | [title] |
| List | [X%] | [N] | [X%] | |
| Review | [X%] | [N] | [X%] | |
## Video Length Performance
| Length Tier | % | Avg Views | Avg Eng Rate |
|------------|---|-----------|-------------|
| Short (< 5min) | [X%] | [N] | [X%] |
## Top 5 Videos (by Views)
| # | Title | Type | Views | Likes | Comments | Length | Eng Rate |
|---|-------|------|-------|-------|----------|--------|---------|
## Publishing Cadence
Videos/week: [X] | Best day to publish: [Day] | Monthly trend: [↑/↓/flat]
## Key Opportunities vs. Their Strategy
1. [Gap: e.g. "No case studies — this format gets 3× their avg views when they do it"]
2. [Opportunity]
3. [Their weakness]Troubleshooting
Scraper returns channel page but no videos: Try direct video search for channel name as keyword; some channel URLs need the /videos suffix. View counts are very low for an established channel: Channel may have declined — check publish date of latest video; may be dormant. Can't determine channel subscriber count from scrape: Use the video view-to-video count ratio as a proxy for channel health when subscriber count is unavailable.
