Classify YouTube Videos & Generate Email Summaries with GPT-4 and Gmail

Description

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.

🎯 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.

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

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

🔑 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)

🧩 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**

🧪 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.

🧾 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.

✅ 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

📸 Suggested Screenshots (optional)
Full canvas overview (entire workflow)
LLM output (expanded) showing generated summary
Send‑node result with messageId/status
Optional: aggregated briefing preview

📜 License & Support
License: MIT
Support/Contact: [email protected]