MCPInstall

analyzing-competitor-youtube-content-strategy

by apidojo-ioMarketing & SEO

Analyzes a competitor's YouTube channel content strategy and performance using apidojo's YouTube scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on YouTube, see what video types perform best for a competitor, reverse-engineer a competitor's YouTube content calendar, benchmark your YouTube channel against a competitor, identify content gaps vs. a competitor on YouTube, understand what topics drive views for a competitor, or compare subscriber growth and video performance between two YouTube channels. Returns video cadence, format mix, top-performing topics, view benchmarks, and engagement analysis. Ideal for content marketing teams, YouTube strategists, and brand video teams.

Installs
790
Last 24h
+12
Trending rank
#8127
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-youtube-content-strategy
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads analyzing-competitor-youtube-content-strategy automatically when relevant.

  3. 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.

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_TOKEN environment variable set
  • Optional: Apify MCP server installed

Inputs

ParameterTypeRequiredDefaultNotes
startUrlsarrayOptional[]YouTube URLs — channels, playlists, Shorts, search results
youtubeHandlesarrayOptional[]YouTube channel handles (e.g. @kurzgesagt)
getTrendingbooleanOptionalfalseRetrieve trending videos
keywordsarrayOptional[]Search keywords
glstringOptionalusCountry code for results (e.g. US, GB)
hlstringOptionalenLanguage code (e.g. en, de)
uploadDatestringOptionalallUpload date filter: any, hour, today, week, month, year
durationstringOptionalallDuration filter: any, short, long
featuresstringOptionalallFeature filter: 4k, hd, live, cc, 3d, hdr, etc.
sortstringOptionalrSort order for search results
maxItemsnumberOptionalUnlimitedMaximum videos to return
customMapFunctionstringOptional—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 report

Step 1: Scrape Channel

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:
{
  "startUrls": [{"url": "[COMPETITOR_CHANNEL_URL]"}],
  "maxResults": 30,
  "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 '{"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 min

Step 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 week

Top 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_analyze to 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.

FAQ

What does the analyzing-competitor-youtube-content-strategy skill do?

Analyzes a competitor's YouTube channel content strategy and performance using apidojo's YouTube scraper on Apify. Triggers when the user asks to: analyze what a competitor posts on YouTube, see what video types perform best for a competitor, reverse-engineer a competitor's YouTube content calendar, benchmark your YouTube channel against a competitor, identify content gaps vs. a competitor on YouTube, understand what topics drive views for a competitor, or compare subscriber growth and video performance between two YouTube channels. Returns video cadence, format mix, top-performing topics, view benchmarks, and engagement analysis. Ideal for content marketing teams, YouTube strategists, and brand video teams.

How do I install analyzing-competitor-youtube-content-strategy?

Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-youtube-content-strategy 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/analyzing-competitor-youtube-content-strategy/ (personal) or .claude/skills/analyzing-competitor-youtube-content-strategy/ (project).

Which AI agents can use analyzing-competitor-youtube-content-strategy?

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 analyzing-competitor-youtube-content-strategy 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 analyzing-competitor-youtube-content-strategy?

It is published by apidojo-io in apidojo-io/apidojo-skills. It has 790 recorded installs.

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