Workflow Templates

This workflow transforms any video you drop into a Google Drive folder into a ready-to-publish YouTube upload. It analyzes the video with AI to craft 3 high-CTR title ideas, 3 long SEO-friendly descriptions (with timestamps), and 10–15 optimized tags. It then generates 4 thumbnail options using your face and lets you pick your favorite before auto-publishing to YouTube via Upload-Post. Who Is This For? YouTube Creators & Editors:** Ship videos with winning titles, thumbnails, and SEO in minutes. Agencies & Media Teams:** Standardize output and speed across channels and clients. Founders & Solo Makers:** Maintain consistent publishing with minimal manual work. What Problem Does It Solve? Producing SEO metadata and high-performing thumbnails is slow and inconsistent. This flow: Generates High-CTR Options:** 3 distinct angles for title/description/tags. Creates Thumbnails with Your Face:** 4 options ready for review in one pass. Auto-Publishes Safely:** Human selection gates reduce risk before going live. How It Works Google Drive Trigger: Watches a folder for new video files. AI Video Analysis (Gemini): Produces an in-depth Spanish description and timestamps. Concept Generation: Returns 3 JSON concepts (title, thumbnail prompt, description, tags). User Review #1: Pick your favorite concept in a simple form. Thumbnail Generation (fal.ai): Creates 4 thumbnails using your face (provided image URL). User Review #2: Choose the best thumbnail. Upload to YouTube (Upload-Post): Publishes the video with your chosen title, description, tags, and thumbnail. Setup Credentials (all offer free trials, no credit card required): Google Gemini (chat/vision for analysis) fal.ai API (thumbnail generation) Upload-Post ( Connect your Youtube channel and generate api keys) Google Drive OAuth (folder watch + file download) Provide Your Face Image URL(s): Used by fal.ai to integrate your face into thumbnails. Select the Google Drive Folder: Where you’ll drop videos to process. Pick & Publish: Use the built-in forms to choose concept and thumbnail. Requirements Accounts:** Google (Drive + Gemini), fal.ai, Upload-Post, n8n. API Keys:** Gemini, fal.ai; Upload-Post credentials; Google Drive OAuth. Assets:** At least one clear face image for thumbnails. Features Three SEO Angles:** Distinct title/description sets to test different intents. Rich Descriptions with Timestamps:** Ready for YouTube SEO and viewer navigation. Face-Integrated Thumbnails:** 4 options aligned with the selected title. Human-in-the-Loop Controls:** Approve concepts and thumbnails before publishing. Auto-Publish via Upload-Post:** One click to push live to YouTube. Start Free:** All API calls can run on free trials, no credit card required. Video demo https://www.youtube.com/watch?v=EOOgFveae-U

What this workflow does This workflow automatically monitors eBay Deals and sends Telegram alerts when relevant, high-quality deals are detected. It combines: Web scraping with Decodo** JavaScript pre-processing (no raw HTML sent to the LLM)** AI-based product classification and deal scoring** Rule-based filtering using price and score** Only valuable deals reach the final notification. How it works (overview) The workflow runs manually or on a schedule. The eBay Deals page is scraped using Decodo, which handles proxies and anti-bot protections. Decodo – Web Scraper for n8n JavaScript extracts only key product data (ID, title, price, URL, image). An AI Agent classifies each product and assigns a deal quality score (0–10). Price and score rules are applied. Matching deals are sent to Telegram. How to configure it 1. Decodo Add your Decodo API credentials to the Decodo node. Optionally change the target eBay URL. 2. AI Agent Add your LLM credentials (e.g. Google Gemini). No HTML is sent to the model — only compact, structured data. 3. Telegram Add your Telegram Bot Token. Set your chat_id in the Telegram node. Customize the alert message if needed. 4. Filtering rules Adjust price limits and minimum deal score in the IF node

Product Price Monitor with Mailchimp and Baserow ⚠️ COMMUNITY TEMPLATE DISCLAIMER: This is a community-contributed template that uses ScrapeGraphAI (a community node). Please ensure you have the ScrapeGraphAI community node installed in your n8n instance before using this template. This workflow scrapes multiple e-commerce sites for product pricing data, stores the historical prices in Baserow, analyzes weekly trends, and emails a neatly formatted seasonal report to your Mailchimp audience. It is designed for retailers who need to stay on top of seasonal pricing patterns to make informed inventory and pricing decisions. Pre-conditions/Requirements Prerequisites Running n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Mailchimp account with at least one audience list Baserow workspace with edit rights Product URLs or SKU list from target e-commerce platforms Required Credentials | Credential | Used By | Scope | |------------|---------|-------| | ScrapeGraphAI API Key | ScrapeGraphAI node | Web scraping | | Mailchimp API Key & Server Prefix | Mailchimp node | Sending emails | | Baserow API Token | Baserow node | Reading & writing records | Baserow Table Setup Create a table named price_tracker with the following fields: | Field Name | Type | Example | |------------|------|---------| | product_name | Text | “Winter Jacket” | | product_url | URL | https://example.com/winter-jacket | | current_price | Number | 59.99 | | scrape_date | DateTime | 2023-11-15T08:21:00Z | How it works This workflow scrapes multiple e-commerce sites for product pricing data, stores the historical prices in Baserow, analyzes weekly trends, and emails a neatly formatted seasonal report to your Mailchimp audience. It is designed for retailers who need to stay on top of seasonal pricing patterns to make informed inventory and pricing decisions. Key Steps: Schedule Trigger**: Fires every week (or custom CRON) to start the monitoring cycle. Code (Prepare URLs)**: Loads or constructs the list of product URLs to monitor. SplitInBatches**: Processes product URLs in manageable batches to avoid rate-limit issues. ScrapeGraphAI**: Scrapes each product page and extracts the current price and name. If (Price Found?)**: Continues only if scraping returns a valid price. Baserow**: Upserts the scraped data into the price_tracker table. Code (Trend Analysis)**: Aggregates weekly data to detect price increases, decreases, or stable trends. Set (Mail Content)**: Formats the trend summary into an HTML email body. Mailchimp**: Sends the seasonal price-trend report to the selected audience segment. Sticky Note**: Documentation node explaining business logic in-workflow. Set up steps Setup Time: 10-15 minutes Clone the template: Import the workflow JSON into your n8n instance. Install ScrapeGraphAI: n8n-nodes-scrapegraphai via the Community Nodes panel. Add credentials: a. ScrapeGraphAI API Key b. Mailchimp API Key & Server Prefix c. Baserow API Token Configure Baserow node: Point it to your price_tracker table. Edit product list: In the “Prepare URLs” Code node, replace the sample URLs with your own. Adjust schedule: Modify the Schedule Trigger CRON expression if weekly isn’t suitable. Test run: Execute the workflow manually once to verify credentials and data flow. Activate: Turn on the workflow for automatic weekly monitoring. Node Descriptions Core Workflow Nodes: Schedule Trigger** – Initiates the workflow on a weekly CRON schedule. Code (Prepare URLs)** – Generates an array of product URLs/SKUs to scrape. SplitInBatches** – Splits the array into chunks of 5 URLs to stay within request limits. ScrapeGraphAI** – Scrapes each URL, using XPath/CSS selectors to pull price & title. If (Price Found?)** – Filters out failed or empty scrape results. Baserow** – Inserts or updates the price record in the database. Code (Trend Analysis)** – Calculates week-over-week price changes and flags anomalies. Set (Mail Content)** – Creates an HTML table with product, current price, and trend arrow. Mailchimp** – Sends or schedules the email campaign. Sticky Note** – Provides inline documentation and edit hints. Data Flow: Schedule Trigger → Code (Prepare URLs) → SplitInBatches SplitInBatches → ScrapeGraphAI → If (Price Found?) → Baserow Baserow → Code (Trend Analysis) → Set (Mail Content) → Mailchimp Customization Examples Change scraping frequency // Schedule Trigger CRON for daily at 07:00 UTC 0 7 * * * Add competitor comparison column // Code (Trend Analysis) item.competitor_price_diff = item.current_price - item.competitor_price; return item; Data Output Format The workflow outputs structured JSON data: { "product_name": "Winter Jacket", "product_url": "https://example.com/winter-jacket", "current_price": 59.99, "scrape_date": "2023-11-15T08:21:00Z", "weekly_trend": "decrease" } Troubleshooting Common Issues Invalid ScrapeGraphAI key – Verify the API key and ensure your subscription is active. Mailchimp “Invalid Audience” error – Double-check the audience ID and that the API key has correct permissions. Baserow “Field mismatch” – Confirm your table fields match the names/types in the workflow. Performance Tips Limit each SplitInBatches run to ≤10 URLs to reduce scraping timeouts. Enable caching in ScrapeGraphAI to avoid repeated requests to the same URL within short intervals. Pro Tips: Use environment variables for all API keys to avoid hard-coding secrets. Add an extra If node to alert you if a product’s price drops below a target threshold. Combine with n8n’s Slack node for real-time alerts in addition to Mailchimp summaries.

Medical Research Tracker with Matrix and Pipedrive ⚠️ COMMUNITY TEMPLATE DISCLAIMER: This is a community-contributed template that uses ScrapeGraphAI (a community node). Please ensure you have the ScrapeGraphAI community node installed in your n8n instance before using this template. This workflow automatically monitors selected government and healthcare-policy websites, extracts newly published or updated policy documents, logs them as deals in a Pipedrive pipeline, and announces critical changes in a Matrix room. It gives healthcare administrators and policy analysts a near real-time view of policy developments without manual web checks. Pre-conditions/Requirements Prerequisites n8n instance (self-hosted or n8n cloud) ScrapeGraphAI community node installed Active Pipedrive account with at least one pipeline Matrix account & accessible room for notifications Basic knowledge of n8n credential setup Required Credentials ScrapeGraphAI API Key** – Enables the scraping engine Pipedrive OAuth2 / API Token** – Creates & updates deals Matrix Credentials** – Homeserver URL, user, access token (or password) Specific Setup Requirements | Variable | Description | Example | |----------|-------------|---------| | POLICY_SITES | Comma-separated list of URLs to scrape | https://health.gov/policies,https://who.int/proposals | | PD_PIPELINE_ID | Pipedrive pipeline where deals are created | 5 | | PD_STAGE_ID_ALERT | Stage ID for “Review Needed” | 17 | | MATRIX_ROOM_ID | Room to send alerts (incl. leading !) | !policy:matrix.org | Edit the initial Set node to provide these values before running. How it works This workflow automatically monitors selected government and healthcare-policy websites, extracts newly published or updated policy documents, logs them as deals in a Pipedrive pipeline, and announces critical changes in a Matrix room. It gives healthcare administrators and policy analysts a near real-time view of policy developments without manual web checks. Key Steps: Scheduled Trigger**: Runs every 6 hours (configurable) to start the monitoring cycle. Code (URL List Builder)**: Generates an array from POLICY_SITES for downstream batching. SplitInBatches**: Iterates through each policy URL individually. ScrapeGraphAI**: Scrapes page titles, publication dates, and summary paragraphs. If (New vs Existing)**: Compares scraped hash with last run; continues only for fresh content. Merge (Aggregate Results)**: Collects all “new” policies into a single payload. Set (Deal Formatter)**: Maps scraped data to Pipedrive deal fields. Pipedrive Node**: Creates or updates a deal per policy item. Matrix Node**: Posts a formatted alert message in the specified Matrix room. Set up steps Setup Time: 15-20 minutes Install Community Node – In n8n, go to Settings → Community Nodes → Install and search for ScrapeGraphAI. Add Credentials – Create New credentials for ScrapeGraphAI, Pipedrive, and Matrix under Credentials. Configure Environment Variables – Open the Set (Initial Config) node and replace placeholders (POLICY_SITES, PD_PIPELINE_ID, etc.) with your values. Review Schedule – Double-click the Schedule Trigger node to adjust the interval if needed. Activate Workflow – Click Activate. The workflow will run at the next scheduled interval. Verify Outputs – Check Pipedrive for new deals and the Matrix room for alert messages after the first run. Node Descriptions Core Workflow Nodes: stickyNote** – Provides an at-a-glance description of the workflow logic directly on the canvas. scheduleTrigger** – Fires the workflow periodically (default 6 hours). code (URL List Builder)** – Splits the POLICY_SITES variable into an array. splitInBatches** – Ensures each URL is processed individually to avoid timeouts. scrapegraphAi** – Parses HTML and extracts policy metadata using XPath/CSS selectors. if (New vs Existing)** – Uses hashing to ignore unchanged pages. merge** – Combines all new items so they can be processed in bulk. set (Deal Formatter)** – Maps scraped fields to Pipedrive deal properties. matrix** – Sends formatted messages to a Matrix room for team visibility. pipedrive** – Creates or updates deals representing each policy update. Data Flow: scheduleTrigger → code → splitInBatches → scrapegraphAi → if → merge → set → pipedrive → matrix Customization Examples 1. Add another data field (e.g., policy author) // Inside ScrapeGraphAI node → Selectors { "title": "//h1/text()", "date": "//time/@datetime", "summary": "//p[1]/text()", "author": "//span[@class='author']/text()" // new line } 2. Switch notifications from Matrix to Email // Replace Matrix node with “Send Email” { "to": "policy-team@example.com", "subject": "New Healthcare Policy Detected: {{$json.title}}", "text": "Summary:\n{{$json.summary}}\n\nRead more at {{$json.url}}" } Data Output Format The workflow outputs structured JSON data for each new policy article: { "title": "Affordable Care Expansion Act – 2024", "url": "https://health.gov/policies/acea-2024", "date": "2024-06-14T09:00:00Z", "summary": "Proposes expansion of coverage to rural areas...", "source": "health.gov", "hash": "2d6f1c8e3b..." } Troubleshooting Common Issues ScrapeGraphAI returns empty objects – Verify selectors match the current HTML structure; inspect the site with developer tools and update the node configuration. Duplicate deals appear in Pipedrive – Ensure the “Find or Create” option is enabled in the Pipedrive node, using the page hash or url as a unique key. Performance Tips Limit POLICY_SITES to under 50 URLs per run to avoid hitting rate limits. Increase Schedule Trigger interval if you notice ScrapeGraphAI rate-limiting. Pro Tips: Store historical scraped data in a database node for long-term audit trails. Use the n8n Workflow Executions page to replay failed runs without waiting for the next schedule. Add an Error Trigger node to emit alerts if scraping or API calls fail.

AI Documentation Crawler & Knowledge Base Builder This n8n template automatically crawls technical documentation websites, scrapes their content, and converts it into clean, structured, developer-friendly documentation. Each page is organized into folders and saved as Google Docs, making it easy to build or maintain an internal knowledge base. Who’s it for Developer teams maintaining internal or external documentation SaaS companies onboarding users or support teams AI builders creating documentation-based knowledge bases Anyone who wants to turn raw docs into structured, readable references How it works / What it does Manual Trigger The workflow starts manually whenever you want to crawl or refresh documentation. Documentation Discovery (Crawler) The workflow crawls a root documentation URL and generates a sitemap of all discoverable documentation pages. URL Processing The sitemap is split into individual URLs. The workflow dynamically analyzes URL depth to recreate the documentation hierarchy. Folder Structure Creation A parent folder is created in Google Drive for the service. Subfolders are automatically generated to mirror the documentation structure (based on URL paths). Content Scraping Each documentation page is scraped using the Olostep API. Clean markdown content is extracted from the page. Information Extraction AI extracts structured technical details such as: API summaries cURL examples Authentication methods Key notes and pitfalls AI Documentation Generation An AI agent transforms the scraped content into a polished, human-readable API reference or guide. Document Creation A Google Doc is created for each documentation page. The generated content is inserted into the document and saved in the correct folder. Rate Control A wait step prevents API throttling during large documentation crawls. The result is a fully structured documentation library generated automatically from live documentation websites. How to set up Import the template into your n8n workspace. Set the root documentation URL you want to crawl. Connect your Google Drive and Google Docs accounts. Add your Olostep API key and AI model credentials. Execute the workflow to generate your documentation library. Requirements n8n account (cloud or self-hosted) Olostep API key Google Drive & Google Docs access AI model provider (OpenAI or Gemini) How to customize the workflow Limit the number of pages crawled per run. Adjust AI prompts to match your documentation style. Store results in Notion, Confluence, or Markdown files instead of Google Docs. Add vector storage (Pinecone, Supabase) to turn docs into an AI knowledge base. Schedule automatic re-crawls to keep documentation up to date. 👉 This template turns complex technical documentation into an organized, searchable knowledge base — automatically.

🛍️ Pick Best-Value Products from Any Website Using Dumpling AI, GPT-4o, and Google Sheets Who’s it for This workflow is for eCommerce researchers, affiliate marketers, and anyone who needs to compare product listings across sites like Amazon. It’s perfect for quickly identifying top product picks based on delivery speed, free shipping, and price. What it does Just submit a product listing URL. The workflow will crawl it using Dumpling AI, take screenshots of the pages, and pass them to GPT-4o to extract up to 3 best-value picks. It analyzes screenshots visually—no HTML scraping needed. Each result includes: product name price review count free delivery date (if available) How it works 📝 Receives a URL through a web form 🧠 Uses Dumpling AI to crawl the website 📸 Takes screenshots of each product listing 🔍 GPT-4o analyzes each image to pick top products 🔧 A code node parses and flattens the output 📊 Google Sheets stores the result 📧 Sends the spreadsheet link via email Requirements Dumpling AI token** OpenAI key** (GPT-4o) Google Sheet** with columns: product name, price, reviews no., free_delivery_date > You can customize the AI prompt to extract other visual insights (e.g., ratings, specs).

This n8n workflow automatically scrapes TechCrunch articles, filters for AI-related content using OpenAI, and delivers curated summaries to your Slack channels. Perfect for individuals or teams who need to stay current on artificial intelligence developments without manually browsing tech news sites. Who's it for AI product teams tracking industry developments and competitive moves Tech investors monitoring AI startup coverage and funding announcements Marketing teams following AI trends for content and positioning strategies Executives needing daily AI industry briefings without manual research overhead Development teams staying current on AI tools, frameworks, and breakthrough technologies How it works The workflow runs on a daily schedule, crawling a specificed amount of TechCrunch articles from the current year. Firecrawl extracts clean markdown content while bypassing anti-bot measures and handling JavaScript rendering automatically. Each article gets analyzed by an AI research assistant that determines if the content relates to artificial intelligence, machine learning, AI companies, or AI technology. Articles marked as "NOT_AI_RELATED" get filtered out automatically. For AI-relevant articles, OpenAI generates focused 3-bullet-point summaries that capture key insights. These summaries get delivered to your specified Slack channel with the original TechCrunch article title and source link for deeper reading. How to set up Configure Firecrawl: Add your Firecrawl API key to the HTTP Request node Set OpenAI credentials: Add your OpenAI API key to the AI Agent node Connect Slack: Configure your Slack webhook URL and target channel Adjust scheduling: Set your preferred trigger frequency (daily recommended) Test the workflow: Run manually to verify article extraction and Slack delivery Requirements Firecrawl account** with API access for TechCrunch web scraping OpenAI API key** for AI content analysis and summarization Slack workspace** with webhook permissions for message delivery n8n instance** (cloud or self-hosted) for workflow execution How to customize the workflow Source expansion: Modify the HTTP node URL to target additional tech publications beyond TechCrunch, or adjust the article limit and date filtering for different coverage needs. AI focus refinement: Update the OpenAI prompt to focus on specific AI verticals like generative AI, robotics, or ML infrastructure. Add company names or technology terms to the relevance filtering logic. Summary formats: Change from 3-bullet summaries to executive briefs, technical analyses, or competitive intelligence reports by modifying the OpenAI summarization prompt. Multi-channel delivery: Extend beyond Slack to email notifications, Microsoft Teams, or database storage for historical trend analysis and executive dashboards.

Automated B2B Lead Generation: Google Places, Scrape.do & AI Enrichment This workflow is a powerful, fully automated B2B lead generation engine. It starts by finding businesses on Google Maps based on your criteria (e.g., "dentists" in "Istanbul"), assigns a quality score to each, and then uses Scrape.do to reliably access their websites. Finally, it leverages an AI agent to extract valuable contact information like emails and social media profiles. The final, enriched data is then neatly organized and saved directly into a Google Sheet. This template is built for reliability, using Scrape.do to handle the complexities of web scraping, ensuring you can consistently gather data without getting blocked. 🚀 What does this workflow do? Automatically finds businesses using the Google Places API based on a category and location you define. Calculates a leadScore for each business based on its rating, website presence, and operational status to prioritize high-quality leads. Filters out low-quality leads** to ensure you only focus on the most promising prospects. Reliably scrapes the website of each high-quality lead using Scrape.do to bypass common blocking issues and retrieve the raw HTML. Uses an AI Agent (OpenAI) to intelligently parse the website's HTML and extract hard-to-find contact details (emails, social media links, phone numbers). Saves all enriched lead data** to a Google Sheet, creating a clean, actionable list for your sales or marketing team. Runs on a schedule**, continuously finding new leads without any manual effort. 🎯 Who is this for? Sales & Business Development Teams:** Automate prospecting and build targeted lead lists. Marketing Agencies:** Generate leads for clients in specific industries and locations. Freelancers & Consultants:** Quickly find potential clients for your services. Startups & Small Businesses:** Build a customer database without spending hours on manual research. ✨ Benefits Full Automation:** Set it up once and let it run on a schedule to continuously fill your pipeline. AI-Powered Enrichment:** Go beyond basic business info. Get actual emails and social profiles that aren't available on Google Maps. Reliable Website Access:* Leverages *Scrape.do** to handle proxies and prevent IP blocks, ensuring consistent data gathering from target websites. High-Quality Leads:** The built-in scoring and filtering system ensures you don't waste time on irrelevant or incomplete listings. Centralized Database:** All your leads are automatically organized in a single, easy-to-access Google Sheet. ⚙️ How it Works Schedule Trigger: The workflow starts automatically at your chosen interval (e.g., daily). Set Parameters: You define the business type (searchCategory) and location (locationName) in a central Set node. Find Businesses: It calls the Google Places API to get a list of businesses matching your criteria. Score & Filter: A custom Function node scores each lead. An IF node then separates high-quality leads from low-quality ones. Loop & Enrich: The workflow processes each high-quality lead one by one. It uses a scraping service (Scrape.do) to reliably fetch the lead's website content. An AI Agent (OpenAI) analyzes the website's footer to find contact and social media links. Save Data: The final, enriched lead information is appended as a new row in your Google Sheet. 📋 n8n Nodes Used Schedule Trigger Set HTTP Request (for Google Places & Scrape.do) Function If Split in Batches (Loop Over Items) HTML Langchain Agent (with OpenAI Chat Model & Structured Output Parser) Google Sheets 🔑 Prerequisites An active n8n instance. Google Cloud Project* with the *Places API** enabled. Google Places API Key**, stored in n8n's Header Auth credentials. A Scrape.do Account and API Token**. This is essential for reliably scraping websites without your n8n server's IP getting blocked. OpenAI Account & API Key** for the AI-powered data extraction. Google Account** with access to Google Sheets. Google Sheets API Credentials (OAuth2)** configured in n8n. A Google Sheet* prepared with columns to store the lead data (e.g., BusinessName, Address, Phone, Website, Email, Facebook, etc.*). 🛠️ Setup Import the workflow into your n8n instance. Configure Credentials: Create and/or select your credentials for: Google Places API: In the 2. Find Businesses (Google Places) node, select your Header Auth credential containing your API key. Scrape.do: In the 6a. Scrape Website HTML node, configure credentials for your Scrape.do account. OpenAI: In the OpenAI Chat Model node, select your OpenAI credentials. Google Sheets: In the 7. Save to Google Sheets node, select your Google Sheets OAuth2 credentials. Define Your Search: In the 1. Set Search Parameters node, update the searchCategory and locationName values to match your target market. Link Your Google Sheet: In the 7. Save to Google Sheets node, select your Spreadsheet and Sheet Name from the dropdown lists. Map the incoming data to the correct columns in your sheet. Set Your Schedule: Adjust the Schedule Trigger to run as often as you like (e.g., once a day). Activate the workflow! Your automated lead generation will begin on the next scheduled run.

Deep Multiline Icebreaker — AI-driven research + personalized cold outreach Deep Multiline Icebreaker automates high-quality, research-led outreach. Feed it a list of leads (emails + websites) and a short product brief via the built-in form; the workflow scrapes each company's site, extracts meaningful page content, uses GPT to produce concise page abstracts, aggregates insights, and then generates tailored, multi-line cold email bodies (JSON). Final outreach rows are appended automatically to a Google Sheet so you can review, sequence, or plug into your outreach stack. This template is built for SDRs, growth folks, and agencies who want dramatically better reply rates by replacing generic blasts with short, highly-specific icebreakers that reference subtle site signals. It’s opinionated (focuses on non-obvious details and concise, credible tone) but easy to tweak — prompts, output format, and the Google Sheet mapping are all editable inside n8n. How it works Form trigger — you submit product details, target designation, location, etc. Leads fetch — the workflow calls an external leads scraper (Apify act) to retrieve potential contacts. Filter & normalize — only rows with website + email proceed; links are normalized (relative/absolute handling). Scrape & convert — homepage and linked pages are fetched and converted to Markdown for clean input. Summarize (GPT) — each page is summarized into a two-paragraph abstract. Aggregate & generate — abstracts are aggregated and GPT generates a tailored multi-line icebreaker JSON (subject + body). Append to Google Sheets — resulting outreach content + lead metadata is appended to your sheet. Nodes of interest you can edit On form submission1 Leads Scraper1 Scrape Home1 Summarize Website Page1 Generate Multiline Icebreaker1 Add Row1 Quick Setup Guide 👉 Demo & Setup Video 👉 Sheet Template 👉 Course What you’ll need (credentials) OpenAI API key (used by Summarize Website Page1 and Generate Multiline Icebreaker1). Google Sheets OAuth (write access for Add Row1). Apify (or your leads-source) API token for Leads Scraper1 (the template calls an Apify act). Optional: outbound HTTP access from your n8n host to target websites. Recommended settings & best practices Limit batch sizes** (the template uses Limit1 set to 3 by default) — ramp the maxItems up slowly to respect rate limits and token costs. Prompt tweaks** — open the Generate Multiline Icebreaker1 prompt to tune tone, cost framing, or add product-specific selling points. Deduplication** — Remove Duplicate URLs1 is included; keep it ON to avoid repeated scraping. Privacy** — don’t store PII longer than necessary; if you store outreach drafts, ensure your Google Sheet access is restricted. Cost control** — set temperature lower (0–0.6) for more consistent outputs and monitor your OpenAI usage. Customization ideas Swap GPT model name or change prompt to produce shorter cold SMS or LinkedIn messages. Replace Apify with your own lead source (CSV upload, CRM query, or Airtable). Add an approval step (Slack/Email) before rows are appended to Google Sheets. Add a follow-up sequence generator that writes 2–3 follow-up messages per lead. Troubleshooting quick tips If pages return empty abstracts, check Request web page for URL1 and network access / user-agent restrictions. If outputs are malformed JSON, open the Generate Multiline Icebreaker1 node and validate the JSON output option. If Google Sheets fails, re-authorize the Google Sheets credential and ensure the sheet ID & sheet name are correct. Tags / Suggested listing fields outreach, lead-gen, sales-automation, openai, web-scraping, google-sheets

Overview AI-powered n8n workflow that creates viral LinkedIn posts by learning from successful content. Features two modules: (1) Telegram-based scraper that builds a vector database of viral LinkedIn posts, and (2) Web form that generates optimized posts using multi-agent AI with RAG (Retrieval-Augmented Generation) from your curated viral content library. Key Capabilities: Scrapes LinkedIn post content via Telegram bot Stores posts in Supabase vector database with OpenAI embeddings 3-agent system analyzes hooks, structures outlines, and generates posts RAG integration retrieves similar viral posts for pattern matching Auto-publishes to LinkedIn or provides formatted output How It Works Module 1: Viral Post Collection (Telegram Bot) Step 1: URL Validation User sends LinkedIn post URL to Telegram bot Workflow validates URL contains "linkedin.com" Shows typing indicator for better UX Step 2: Content Scraping HTTP request fetches post HTML CSS selector extracts main commentary: [data-test-id="main-feed-activity-card__commentary"] Handles scraping failures with error messages Step 3: Vector Storage Converts post text to OpenAI embeddings (text-embedding-ada-002) Stores in Supabase linkedin_post table with vector indexing Sends success confirmation via Telegram Module 2: AI Post Generation (Web Form) Stage 1: Hook Analysis Agent Input**: User-provided hook text Process**: AI extracts topic, niche/industry, emotional tone, and 3-5 key points Output**: Structured JSON with analyzed elements Models**: GPT-4o-mini or Gemini 2.5-flash (dual fallback) Stage 2: Post Structure Agent Input**: Analyzed hook data Process**: Creates 5-section outline (Hook, Problem, Value/Lesson, Solution, CTA) Output**: Structured framework for final post Models**: GPT-4o-mini or Gemini 2.5-flash Stage 3: Post Generator Agent (RAG) Input**: Post structure + topic RAG Process**: Queries Supabase vector store for 5 most similar viral posts Analyzes patterns: hooks, storytelling, CTAs, engagement metrics Identifies optimal length, formatting, and emotional triggers Output**: Complete LinkedIn post applying viral patterns Models**: GPT-4o-mini or Gemini 2.5-flash with GPT-5-NANO for structured output Stage 4: Publication Auto-publishes to LinkedIn via API Or returns formatted post text for manual posting How To Use Setup 1. Configure Supabase Vector Database Create Supabase project Create table: linkedin_post with vector column (1536 dimensions for OpenAI embeddings) Enable vector extension: CREATE EXTENSION vector; Update credentials in "Upload Document" and "Supabase Vector Store" nodes 2. Set Up Telegram Bot (Module 1) Create bot via @BotFather Get bot token and update "On Telegram Message" credentials Start bot and get your chat ID Activate workflow 3. Configure OpenAI API Add API key to "Embeddings" nodes (both modules) Configure language model credentials (GPT-4o-mini, GPT-5-NANO) 4. Set Up LinkedIn API (Optional for Module 2) Create LinkedIn app with member permissions Configure OAuth2 credentials in "Create a post" node Or remove node to get text output only 5. Access Web Form Get form URL from "LinkedIn Form" webhook Bookmark for easy access

AI Job Matcher with Decodo, Gemini AI & Resume Analysis Sign up for Decodo — get better pricing here Who’s it for This workflow is built for job seekers, recruiters, founders, automation builders, and data engineers who want to automate job discovery and intelligently match job listings against resumes using AI. It’s ideal for anyone building job boards, candidate matching systems, hiring pipelines, or personal job alert automations using n8n. What this workflow does This workflow automatically scrapes job listings from SimplyHired using Decodo residential proxies, extracts structured job data with a Gemini AI agent, downloads resumes from Google Drive, extracts and summarizes resume content, and surfaces the most relevant job opportunities. The workflow stores structured results in a database and sends real-time notifications via Telegram, creating a scalable and low-maintenance AI-powered job matching pipeline. How it works A schedule trigger starts the workflow automatically Decodo fetches job search result pages from SimplyHired Job card HTML is extracted from the page A Gemini AI agent converts raw HTML into structured job data Resume PDFs are downloaded from Google Drive Resume text is extracted from PDF files A Gemini AI agent summarizes key resume highlights Job and resume data are stored in a database Matching job alerts are sent via Telegram How to set up Add your Decodo API credentials Add your Google Gemini API key Connect Google Drive for resume access Configure your Telegram bot Set up your database (Google Sheets by default) Update the job search URL with your keywords and location Requirements Self-hosted n8n instance Decodo account (community node) Google Gemini API access Google Drive access Telegram Bot token Google Sheets or another database > Note: This template uses a community node (Decodo) and is intended for self-hosted n8n only. How to customize the workflow Replace SimplyHired with another job board or aggregator Add job–resume matching or scoring logic Extend the resume summary with custom fields Swap Google Sheets for PostgreSQL, Supabase, or Airtable Route notifications to Slack, Email, or Webhooks Add pagination or multi-resume processing

Icebreaker Generator powered with ChatGPT This n8n template crawls a company website, distills the content with AI, and produces a short, personalized icebreaker you can drop straight into your cold emails or CRM. Perfect for SDRs, founders, and agencies who want “real research” at scale. Good to know Works from a Google Sheet of leads (domain + LinkedIn, etc.). Handles common scrape failures gracefully and marks the lead’s Status as Error. Uses ChatGPT to summarize pages and craft one concise, non-generic opener. Output is written back to the same Google Sheet (IceBreaker, Status). You’ll need Google credentials (for Sheets) and OpenAI credentials (for GPT). How it works Step 1 — Discover internal pages Reads a lead’s website from Google Sheets. Scrapes the home page and extracts all links. A Code node cleans the list (removes emails/anchors/social/external domains, normalizes paths, de-duplicates) and returns unique internal URLs. If the home page is unreachable or no links are found, the lead is marked Error and the workflow moves on. Step 2 — Convert pages to text Visits each collected URL and converts the response into HTML/Markdown text for analysis. You can cap depth/amount with the Limit node. Step 3 — Summarize & generate the icebreaker A GPT node produces a two-paragraph abstract for each page (JSON output). An Aggregate node merges all abstracts for the company. Another GPT node turns the merged summary into a personalized, multi-line icebreaker (spartan tone, non-obvious details). The result is written back to Google Sheets (IceBreaker = ..., Status = Done). The workflow loops to the next lead. How to use Prepare your sheet Include at least: organization_website_url, linkedin_url, and any other lead fields you track. Keep an empty IceBreaker and Status column for the workflow to fill. Connect credentials Google Sheets: use the Google account that owns the sheet and link it in the nodes. OpenAI: add your API key to the GPT nodes (“Summarize Website Page”, “Generate Multiline Icebreaker”). Run the workflow Start with the Manual Trigger (or replace with a schedule/webhook). Adjust Limit if you want fewer/more pages per company. Watch Status (Done/Error) and IceBreaker populate in your sheet. Requirements n8n instance Google Sheets account & access to the leads sheet OpenAI API key (for summarization + icebreaker generation) Customizing this workflow Tone & format: tweak the prompts (both GPT nodes) to match your brand voice and structure. Depth: change the Limit node to scan more/less pages; add simple rules to prioritize certain paths (e.g., /about, /blog/*). Fields: write additional outputs (e.g., Company Summary, Key Products, Recent News) back to new sheet columns. Lead selection: filter rows by Status = "" (or custom flags) to only process untouched leads. Error handling: expand the Error branch to retry with www./HTTP→HTTPS or to log diagnostics in a separate tab. Tips Keep icebreakers short, specific, and free of clichés—small, non-obvious details from the site convert best. Start with a small batch to validate quality, then scale up. Consider adding a rate limit if target sites throttle requests. In short: Sheet → crawl internal pages → AI abstracts → single tailored icebreaker → write back to the sheet, then repeat for the next lead. This automation can work great with our automation for automated cold emailing.