Auto-Generate YouTube Chapters with AI-Powered Transcript Analysis Overview This workflow uses YouTube Data API v3 and Google Gemini 1.5 Flash AI to automatically generate timestamped chapters for videos by analyzing SRT captions. It enhances viewer navigation, improves SEO , and saves creators time by automating manual tasks. Prerequisites YouTube API Setup Create a Google Cloud Project Go to the Google Cloud Console. Click Select a project > New Project and name it (e.g., "YouTube Chapters Automation") . Enable YouTube Data API v3 Navigate to APIs & Services > Library. Search for "YouTube Data API v3" and click Enable . Configure OAuth Consent Screen Go to APIs & Services > OAuth consent screen. Select External (public) or Internal (testing), then add required details (app name, support email) . Generate OAuth 2.0 Credentials Under Credentials, click Create Credentials > OAuth client ID. Choose Web app, then download the JSON key file . Add Credentials to n8n Other Requirements Google Gemini API**: Configure access for the gemini-1.5-flash-8b-exp-0924 model by getting the api key.
Tags
Structured Output Parser
YouTubeRelated workflows
See all AI Automation→Generate weekly student progress reports with Gemini, Supabase, and WhatsApp
Quick overview This workflow runs weekly to pull student attendance and score data from Supabase, uses Google Gemini to generate per-student performance summaries and attention flags, sends immediate WhatsApp alerts for flagged students, then compiles all summaries into a PDF report, uploads it, and shares it with the coach via WhatsApp. How it works Runs weekly on a schedule. Fetches all student weekly metrics (attendance and scores) from Supabase. Uses Google Gemini to write a short performance summary per student and determine whether the student should be flagged for attention based on attendance and score-drop rules. If a student is flagged, sends a WhatsApp message to the coach and inserts a flag record into Supabase. Aggregates all students’ summaries into a single HTML weekly report. Converts the HTML report to a PDF via an HTTP-based HTML-to-PDF API, uploads the PDF to a file-hosting API, and sends the PDF link/document to the coach via WhatsApp. Logs the report URL to Supabase for recordkeeping. Setup Add Supabase credentials and create the required tables/columns (weekly_student_data, attention_flags, and weekly_reports) referenced by the workflow. Add a Google Gemini (P
Draft personalized cold emails from Google Sheets using Gemini and HubSpot
Quick overview This workflow batch-processes leads from Google Sheets, researches each person and company using Tavily plus Google Gemini, drafts a personalized cold email, then creates/updates the contact in HubSpot with a research note and a saved email draft before marking the lead as Done in the sheet. How it works Runs manually and sets a batch size that controls how many leads to process per run. Reads leads from Google Sheets, skips rows already marked as Done, and normalizes the selected batch into clean lead fields. Processes the batch one lead at a time and uses Tavily search (last 60 days) with Google Gemini to extract the most recent public activity, pain points, and a personalization hook. Cleans and parses the research JSON so it can be reused reliably downstream. Uses Google Gemini to draft a 90–120 word cold outreach email (subject and body) that incorporates the research and offers a free “Automated LinkedIn Post Creator” n8n workflow. Creates or reuses the HubSpot contact, logs a HubSpot note with the research and drafted email, and saves the email as a HubSpot engagement in DRAFT status. Updates the matching row in Google Sheets (by Email) to set Status to “Done
BW
Send and track LinkedIn outreach with Airtable, Unipile, and OpenRouter
Quick overview This workflow automates LinkedIn outreach by using Airtable as a CRM, Unipile to fetch profiles and send connection requests/messages, and OpenRouter to generate personalized connection notes and follow-ups, while tracking accepts and replies via Unipile webhooks. How it works Runs every weekday at 09:00 to pull up to 15 Airtable leads with Connection Status set to New. For each lead, derives the LinkedIn public identifier, fetches the LinkedIn profile from Unipile, and uses OpenRouter to generate a personalized connection note capped at 300 characters. Sends the LinkedIn connection request via Unipile, updates the Airtable record with request details (note, timestamps, invitation ID), and waits 30–90 seconds between sends. Receives Unipile new_relation webhook events, matches the lead by Provider ID, and marks the Airtable record as Accepted with an Accepted At timestamp. Receives Unipile message_received webhook events, filters to inbound replies, finds the matching Airtable lead, and marks it Replied while stopping the campaign. Runs daily at 10:00 to find accepted, no-reply leads due for follow-up, uses OpenRouter to write follow-up #1 or #2, sends the DM via Uni