Influencer Analyzer

Know what's working, find where to differentiate. This skill tracks cardiology content creators and identifies opportunities for your content.

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WHAT IT DOES

Step	Action	Output
1	Find influencer content via Perplexity/DuckDuckGo	URLs, articles, videos
2	Scrape and extract content patterns	Topics, formats, frequency
3	Analyze engagement signals	What resonates with audience
4	Generate gap analysis	Where you can differentiate

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TRIGGERS

Use this skill when you say:

• "What is [Topol/Attia/competitor] posting about?"
• "Find gaps in cardiology content"
• "Analyze my competition"
• "What topics should I cover?"
• "Track cardiology influencers"

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TARGET INFLUENCERS

International (English)

Name	Platform	Focus	Why Track
@EricTopol	Twitter, Substack	Trials, digital health	Voice model, Ground Truths style
Peter Attia	Podcast, YouTube	Longevity, CVD prevention	Deep-dive style
York Cardiology	YouTube	Patient education	Clear explanations
Dr. Sanjay Gupta (York)	YouTube	ECG, clinical cases	Educational format

Indian (Hindi/English)

Name	Platform	Focus	Why Track
Dr Navin Agrawal	YouTube	Patient education	Competition
Cardiac Second Opinion	YouTube	Second opinions	Competition
Dr. Devi Shetty	Videos	Affordable care	Authority

Anti-Patterns (What NOT to do)

Name	Platform	Why Track
SAAOL	YouTube	Misinformation to counter
Dr Biswaroop Roy Chowdhury	YouTube	Dangerous claims to debunk

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USAGE

In Claude Code (Recommended)

"Analyze what Eric Topol is posting about this week"

"Find gaps between Topol's content and Indian cardiology YouTube"

"What cardiology topics are trending that I haven't covered?"

"Compare my content strategy with Peter Attia"

CLI Mode

# Analyze single influencer
python scripts/analyze_influencer.py --name "Eric Topol" --platform twitter

# Compare multiple influencers
python scripts/analyze_influencer.py --compare "Topol,Attia,York Cardiology"

# Find content gaps
python scripts/analyze_influencer.py --gaps --domain "Cardiology"

# Track specific topic
python scripts/analyze_influencer.py --topic "GLP-1" --influencers "Topol,Attia"

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OUTPUT FORMATS

1. Influencer Profile

## Eric Topol (@EricTopol)

**Recent Focus (Last 30 days):**
- Clinical trials: 45%
- Digital health/AI: 30%
- COVID updates: 15%
- Book promotion: 10%

**Top Performing Topics:**
1. REDUCE-IT controversy (high engagement)
2. Apple Watch AFib detection (viral)
3. AI in diagnosis (consistent interest)

**Posting Patterns:**
- Frequency: 5-10 tweets/day
- Best times: 6AM, 12PM, 6PM PST
- Thread usage: Weekly deep-dives

**Style Notes:**
- Links to primary sources (PubMed, NEJM)
- Quotes key statistics
- Engages with critics
- Retweets junior researchers

2. Gap Analysis Report

## CONTENT GAP ANALYSIS

**What Topol Covers That You Don't:**
- [ ] Weekly trial breakdowns
- [ ] Digital health intersection
- [ ] International guideline comparisons

**What You Cover That Topol Doesn't:**
- [x] Hinglish explanations
- [x] Indian patient context
- [x] Cost-conscious alternatives
- [x] Cultural nuances (vegetarian diets, family dynamics)

**OPPORTUNITY ZONES:**
1. **Translate English trials for Indian context**
   - Topol covers REDUCE-IT → You explain what it means for Indian patients

2. **Bridge the gap**
   - International guidelines → Indian applicability

3. **Underserved topics in English space**
   - Rheumatic heart disease (rare topic in US)
   - Tropical cardiology
   - Resource-limited settings

3. Competitive Comparison Table

| Aspect | Eric Topol | Peter Attia | York Cardiology | You |
|--------|------------|-------------|-----------------|-----|
| Platform | Twitter/Substack | Podcast/YouTube | YouTube | YouTube |
| Language | English | English | English | Hinglish |
| Depth | Expert-level | Deep-dive | Patient-friendly | Expert→Patient |
| Frequency | Daily | Weekly | 2-3x/week | ? |
| Unique Angle | Trials/Digital | Longevity | ECG teaching | Indian context |

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INTEGRATION WITH YOUR SYSTEM

Feeds Into:

• research-engine/data/target_channels.json - Channel tracking
• youtube-script-master - Topic selection
• viral-content-predictor - Content scoring
• content-repurposer - Multi-platform adaptation

Data Flow:

influencer-analyzer
       ↓
[Gap Analysis Report]
       ↓
research-engine (topic prioritization)
       ↓
youtube-script-master (script creation)
       ↓
YOUR CONTENT (unique angle)

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HOW CLAUDE SHOULD USE THIS SKILL

When the user asks about competitors or content strategy:

Step 1: Identify Target

User: "What is Topol posting about?"
→ Target: Eric Topol
→ Platforms: Twitter, Substack

Step 2: Research with Perplexity

Use Perplexity MCP or web search to find:

• Recent posts/articles
• Engagement metrics
• Topic distribution

Step 3: Analyze Patterns

• What topics repeat?
• What gets most engagement?
• What's the posting frequency?

Step 4: Generate Gap Analysis

Compare with user's existing content:

• What's covered vs. uncovered?
• Where can user differentiate?
• What's the unique angle?

Step 5: Actionable Recommendations

• Specific topics to cover
• Formats to try
• Timing suggestions

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SAMPLE WORKFLOW

User: "Find content gaps in cardiology YouTube"

Claude:
1. Uses Perplexity to search:
   - "Eric Topol recent tweets cardiology 2025"
   - "Peter Attia podcast topics 2025"
   - "York Cardiology recent videos"
   - "Indian cardiology YouTube channels"

2. Analyzes results:
   - Topic frequency
   - Engagement patterns
   - Content gaps

3. Cross-references with user's content:
   - What has user covered?
   - What's missing?
   - What's unique to user?

4. Outputs:
   - Gap analysis report
   - Priority topics list
   - Differentiation strategy

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DEPENDENCIES

# Already have
anthropic>=0.18.0
python-dotenv>=1.0.0
rich>=13.0.0

# For web scraping (optional)
requests>=2.31.0
beautifulsoup4>=4.12.0

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API KEYS NEEDED

Key	Purpose	Status
PERPLEXITY_API_KEY	Web search	Already have (via OpenRouter)
ANTHROPIC_API_KEY	Analysis	Already have

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PRE-CONFIGURED INFLUENCER PROFILES

Located in data/influencers.json:

{
  "influencers": [
    {
      "name": "Eric Topol",
      "handle": "@EricTopol",
      "platforms": ["twitter", "substack"],
      "focus": ["clinical_trials", "digital_health", "AI_medicine"],
      "style": "expert_commentary",
      "track_for": "voice_model"
    },
    {
      "name": "Peter Attia",
      "handle": "peterattiamd",
      "platforms": ["podcast", "youtube", "newsletter"],
      "focus": ["longevity", "metabolic_health", "CVD_prevention"],
      "style": "deep_dive",
      "track_for": "format_inspiration"
    },
    {
      "name": "York Cardiology",
      "handle": "@YorkCardiology",
      "platforms": ["youtube"],
      "focus": ["ECG", "patient_education", "clinical_cases"],
      "style": "educational",
      "track_for": "competitor"
    },
    {
      "name": "Dr Navin Agrawal",
      "handle": null,
      "platforms": ["youtube"],
      "focus": ["patient_education", "hindi"],
      "style": "simple_explanations",
      "track_for": "competitor"
    }
  ]
}

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NOTES

• Privacy: Only analyze public content
• Frequency: Run weekly for trend tracking
• Focus: Gap analysis, not copying
• Goal: Find YOUR unique angle, not imitate others

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This skill helps you understand the competitive landscape so you can differentiate, not duplicate.