Move beyond generic AI-generated content and create articles that are high-quality, factually reliable, and aligned with your unique expertise. This template orchestrates a sophisticated "research-first" content creation process. Instead of simply asking an AI to write an article from scratch, it first uses an AI planner to break your topic down into logical sub-questions. It then queries a Super assistant—which you've connected to your own trusted knowledge sources like Notion, Google Drive, or PDFs—to build a comprehensive research brief. Only then is this fact-checked brief handed to a powerful AI writer to compose the final article, complete with source links. This is the ultimate workflow for scaling expert-level content creation. Who is this for? Content marketers & SEO specialists:** Scale the creation of authoritative, expert-level blog posts that are grounded in factual, source-based information. Technical writers & subject matter experts:** Transform your complex internal documentation into accessible public-facing articles, tutorials, and guides.
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Structured Output ParserRelated workflows
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BForce AI to use a specific output format
This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.
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
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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