Agent skill

youtube-comment-analyzer

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/drshailesh88/integrated_content_OS/tree/main/skills/cardiology/youtube-comment-analyzer

SKILL.md

YouTube Comment Analyzer

Trigger phrases:

  • "Analyze comments for [URL]"
  • "Analyze this video: [URL]"
  • "What are people asking about [URL]"
  • "Comment analysis for [VIDEO_ID]"
  • "YouTube comment insights for [URL]"

Source: Based on https://github.com/drshailesh88/youtube-analyzer

What This Does

Analyzes YouTube video comments to extract audience insights:

  • Top Questions people are asking (with urgency ratings)
  • Top Myths and misconceptions (with danger ratings)
  • Pain Points and frustrations
  • Content Recommendations (must address, gaps, viral potential)
  • Sentiment Analysis (positive/negative/neutral breakdown)
  • Recurring Themes in discussions

How to Use (Claude Instructions)

Step 1: Extract Video ID

From URL patterns:

  • https://www.youtube.com/watch?v=VIDEO_ID
  • https://youtu.be/VIDEO_ID
  • https://youtube.com/watch?v=VIDEO_ID&...
  • Just VIDEO_ID (11 characters, alphanumeric with - and _)

Step 2: Run the Analyzer

bash
python skills/cardiology/youtube-comment-analyzer/scripts/analyze_comments.py VIDEO_URL_OR_ID

Options:

  • --max-comments 500 — Limit comments scraped (default: 500)
  • --output path/to/file.json — Custom output location
  • --json — Output raw JSON instead of formatted report

Examples:

bash
# Analyze by URL
python skills/cardiology/youtube-comment-analyzer/scripts/analyze_comments.py "https://youtube.com/watch?v=abc123xyz"

# Analyze by ID with limited comments
python skills/cardiology/youtube-comment-analyzer/scripts/analyze_comments.py abc123xyz --max-comments 200

# Get JSON output
python skills/cardiology/youtube-comment-analyzer/scripts/analyze_comments.py abc123xyz --json

Step 3: Read the Output

The script outputs a formatted report directly. Full JSON is saved to:

skills/cardiology/youtube-comment-analyzer/output/analysis_VIDEO_ID_TIMESTAMP.json

Output Format

## Comment Analysis: [Video Title]

**Analyzed:** 1,847 comments | **Time:** 45 seconds

### Top Questions (What viewers want to know)
1. [Question] — HIGH urgency
2. [Question] — MEDIUM urgency
3. ...

### Top Myths (Misconceptions to address)
1. [Myth] — HIGH danger
2. [Myth] — MEDIUM danger
3. ...

### Pain Points (Viewer frustrations)
1. [Pain point]
2. [Pain point]
3. ...

### Content Recommendations
- **Must Address:** [topics]
- **Content Gaps:** [topics]
- **Viral Potential:** [topics]

### Sentiment
Positive: X | Negative: Y | Neutral: Z
Summary: [one line]

### Recurring Themes
theme1, theme2, theme3, ...

API Keys Required

Set one of these in .env:

  • YOUTUBE_API_KEY or GOOGLE_API_KEY — For fetching comments via YouTube Data API v3
  • ANTHROPIC_API_KEY — For AI analysis (preferred)
  • OPENROUTER_API_KEY — Fallback for AI analysis (uses free Gemini model)

Error Handling

  • No comments found: Video may have comments disabled
  • API error: Check API key validity
  • Rate limited: Wait and retry, or reduce --max-comments

Technical Details

  • Uses YouTube Data API v3 for comment fetching
  • Uses Claude or OpenRouter (Gemini) for AI analysis
  • Handles 500+ comments via map-reduce chunking
  • Output saved to JSON for future reference
  • Works with any YouTube video with comments enabled

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