Classify YouTube Trends and Generate Email Summaries with GPT-4 and Gmail

Monitor YouTube channels, fetch stats, classify videos as viral (≥ 1000 likes) or normal, and auto‑generate LinkedIn/email summaries with GPT‑4. Deliver via Gmail or SMTP. Clear node names, examples, and auditable fields.

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🎯 Overview

This template monitors YouTube channels via RSS or the YouTube Data API, retrieves video stats, classifies each video as viral (≥ 1000 likes) or normal, and produces concise LinkedIn/email summaries with OpenAI (GPT‑4 family). It can send a compact weekly briefing via Gmail (OAuth2) or SMTP. Built for creators, marketing teams, and agencies who want automated trend alerts and ready‑to‑use content.

This screenshot shows the Gmail-ready weekly briefing generated by the Generate Weekly Briefing (HTML) node in my YouTube Trend Detector workflow, confirming the end-to-end pipeline: RSS/API → stats → like-based classification (≥ 1000 = viral) → LLM summaries → HTML email.

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🧭 How It Works (Node Map)

1. Manual Run – ad‑hoc execution
2. Set Channel IDs – provide one or more YouTube channelId values
3. Split Channels – process channels one by one
4. Fetch Latest Videos (RSS) – pull recent uploads via channel RSS
5. Filter: Published in Last 72h – only recent items are kept
6. Get Video Stats (YouTube API) – request snippet,statistics for likes and details
7. Classify by Likes (Code) – sets classification to viral or normal
8. Branch: Normal / Branch: Viral – separate LLM prompts per relevance
9. Write Post (Normal / Viral) – generate LinkedIn‑style notes via OpenAI
10. Aggregate Posts for Briefing – merge all texts into one block
11. Generate Weekly Briefing (HTML) – produce a Gmail‑robust HTML email via GPT
12. Send Weekly Briefing (Gmail/SMTP) – deliver briefing (you set recipients)

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⚙️ Quick Start (≈ 3 minutes)

1. Import the sanitized JSON into n8n (Menu → Import).
2. Create credentials (use exact names):
   • YouTube_API_Key — Generic credential (field: apiKey)
   • OpenAi account — OpenAI API Key
   • Gmail account (OAuth2) or SMTP_Default (SMTP)
3. Configure channels: In Set Channel IDs, list your YouTube channelId values (e.g., UC…).
4. Set recipients: In Send Weekly Briefing, add your target email(s).
5. Test: Run Execute Workflow and review outputs from the LLM and send nodes.

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🔑 Required Credentials

• YouTube_API_Key — YouTube Data API v3 key (field apiKey)
• OpenAi account — OpenAI API key for LLM nodes
• Gmail account (OAuth2, recommended) or SMTP_Default (server/port/TLS + app password if 2FA)

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🧩 Key Parameters & Adjustments

• Viral threshold: In Classify by Likes (Code) → const THRESHOLD = 1000;
• YouTube API parts: Use part=snippet,statistics to obtain likeCount
• Time window: The filter keeps videos from the last 72 hours

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🧪 Troubleshooting

• Missing likeCount / classification = "unknown" → ensure part=statistics and a valid API key credential.
• Gmail OAuth redirect_mismatch / access_denied → redirect must be https://<your-n8n-host>/rest/oauth2-credential/callback and test users added if restricted.
• SMTP auth issues → set correct server/port/TLS and use an app password when 2FA is enabled.
• Empty LLM output → verify OpenAI key/quota and inspect node logs.

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🧾 Example Outputs

1) Classification (single video)

{
  "videoId": "abc123XYZ",
  "title": "How to Ship an n8n Workflow with OpenAI",
  "likeCount": 1587,
  "classification": "viral",
  "needsStatsFetch": false
}

2) LinkedIn draft (viral)

Did you know how much faster prompt workflows get with structured inputs?
• Setup: n8n + YouTube API + OpenAI for auto-briefs
• Tip: include `part=statistics` for reliable like counts
Useful for teams tracking trending how-to content.
What’s your best “viral” signal besides likes?
#n8n #YouTubeAPI #OpenAI #Automation #Growth

3) Plain‑text email preview

Subject: Weekly AI Briefing — YouTube Trend Highlights

Hi team,
Highlights from our tracked channels:
• Viral: “How to Ship an n8n Workflow with OpenAI” (1.6k likes)
• Normal: “RSS vs API: What’s Best for Monitoring?”
Generated via n8n + GPT‑4.

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✅ Submission Checklist (meets the guidelines)

• Title clarity: Mentions GPT‑4 and Gmail
• Language: Entire document in English
• Node naming: Descriptive, non‑generic labels
• HTML → Markdown: No HTML in this description; badges are Markdown images
• Examples: Included (JSON, LinkedIn draft, email)
• Security: No secrets in JSON; uses credentials by name

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📸 Suggested Screenshots (optional)

1. Full canvas overview (entire workflow)
2. LLM output (expanded) showing generated summary
3. Send‑node result with messageId/status
4. Optional: aggregated briefing preview

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📜 License & Support

License: MIT
Support/Contact: [email protected]