by Khairul Muhtadin
Decodo Amazon Product Recommender delivers instant, AI-powered shopping recommendations directly through Telegram. Send any product name and receive Amazon product analysis featuring price comparisons, ratings, sales data, and categorized recommendations (budget, premium, best value) in under 40 seconds—eliminating hours of manual research. Why Use This Workflow? Time Savings: Reduce product research from 45+ minutes to under 30 seconds Decision Quality: Compare 20+ products automatically with AI-curated recommendations Zero Manual Work: Complete automation from message input to formatted recommendations Ideal For E-commerce Entrepreneurs:** Quickly research competitor products, pricing strategies, and market trends for inventory decisions Smart Shoppers & Deal Hunters:** Get instant product comparisons with sales volume data and discount tracking before purchasing Product Managers & Researchers:** Analyze Amazon marketplace positioning, customer sentiment, and pricing ranges for competitive intelligence How It Works Trigger: User sends product name via Telegram (e.g., "iPhone 15 Pro Max case") AI Validation: Gemini 2.5 Flash extracts core product keywords and validates input authenticity Data Collection: Decodo API scrapes Amazon search results, extracting prices, ratings, reviews, sales volume, and product URLs Processing: JavaScript node cleans data, removes duplicates, calculates value scores, and categorizes products (top picks, budget, premium, best value, most popular) Intelligence Layer: AI generates personalized recommendations with Telegram-optimized markdown formatting, shortened product names, and clean Amazon URLs Output & Delivery: Formatted recommendations sent to user with categorized options and direct purchase links Error Handling: Admin notifications via separate Telegram channel for workflow monitoring Setup Guide Prerequisites | Requirement | Type | Purpose | |-------------|------|---------| | n8n instance | Essential | Workflow execution platform | | Decodo Account | Essential | Amazon product data scraping | | Telegram Bot Token | Essential | Chat interface for user interactions | | Google Gemini API | Essential | AI-powered product validation and recommendations | | Telegram Account | Optional | Admin error notifications | Installation Steps Import the JSON file to your n8n instance Configure credentials: Decodo API: Sign up at decodo.com → Dashboard → Scraping APIs → Web Advanced → Copy BASIC AUTH TOKEN Telegram Bot: Message @BotFather on Telegram → /newbot → Copy HTTP API token (format: 123456789:ABCdefGHI...) Google Gemini: Obtain API key from Google AI Studio for Gemini 2.5 Flash model Update environment-specific values: Replace YOUR-CHAT-ID in "Notify Admin" node with your Telegram chat ID for error notifications Verify Telegram webhook IDs are properly configured Customize settings: Adjust AI prompt in "Generate Recommendations" node for different output formats Set character limits (default: 2500) for Telegram message length Test execution: Send test message to your Telegram bot: "iPhone 15 Pro" Verify processing status messages appear Confirm recommendations arrive with properly formatted links Customization Options Basic Adjustments: Character Limit**: Modify 2500 in AI prompt to adjust response length (Telegram max: 4096) Advanced Enhancements: Multi-language Support**: Add language detection and translation nodes for international users Price Tracking**: Integrate Google Sheets to log historical prices and trigger alerts on drops Image Support**: Enable Telegram photo messages with product images from scraping results Troubleshooting Common Issues: | Problem | Cause | Solution | |---------|-------|----------| | "No product detected" for valid inputs | AI validation too strict or ambiguous query | Add specific product details (model number, brand) in user input | | Empty recommendations returned | Decodo API rate limit or Amazon blocking | Wait 60 seconds between requests; verify Decodo account status | | Telegram message formatting broken | Special characters in product names | Ensure Telegram markdown mode is set to "Markdown" (legacy) not "MarkdownV2" | Use Case Examples Scenario 1: E-commerce Store Owner Challenge: Needs to quickly assess competitor pricing and product positioning for new inventory decisions without spending hours browsing Amazon Solution: Sends "wireless earbuds" to bot, receives categorized analysis of 20+ products with price ranges ($15-$250), top sellers, and discount opportunities Result: Identifies $35-$50 price gap in market, sources comparable product, achieves 40% profit margin Scenario 2: Smart Shopping Enthusiast Challenge: Wants to buy a laptop backpack but overwhelmed by 200+ Amazon options with varying prices and unclear value propositions Solution: Messages "laptop backpack" to bot, gets AI recommendations sorted by budget ($30), premium ($50+), best value (highest discount + good ratings), and most popular (by sales volume) Result: Purchases "Best Value" recommendation with 35% discount, saves $18 and 45 minutes of research time Created by: Khaisa Studio Category: AI | Productivity | E-commerce | Tags: amazon, telegram, ai, product-research, shopping, automation, gemini Need custom workflows? 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by vinci-king-01
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.
by Yasser Sami
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.
by Rapiwa
Who Is This For? This n8n workflow enables automated cross-selling by identifying each WooCommerce customer's most frequently purchased product, finding a related product to recommend, and sending a personalized WhatsApp message using the Rapiwa API. It also verifies whether the user's number is WhatsApp-enabled before sending, and logs both successful and unsuccessful attempts to Google Sheets for tracking. What This Workflow Does Retrieves all paying customers from your WooCommerce store Identifies each customer's most purchased product Finds the latest product in the same category as their most purchased item Cleans and verifies customer phone numbers for WhatsApp compatibility Sends personalized WhatsApp messages with product recommendations Logs all activities to Google Sheets for tracking and analysis Handles both verified and unverified numbers appropriately Key Features Customer Segmentation:** Automatically identifies paying customers from your WooCommerce store Product Analysis:** Determines each customer's most purchased product Smart Recommendations:** Finds the latest products in the same category as customer favorites WhatsApp Integration:** Uses Rapiwa API for message delivery Phone Number Validation:** Verifies WhatsApp numbers before sending messages Dual Logging System:** Tracks both successful and failed message attempts in Google Sheets Rate Limiting:** Uses batching and wait nodes to prevent API overload Personalized Messaging:** Includes customer name and product details in messages Requirements WooCommerce store with API access Rapiwa account with API access for WhatsApp verification and messaging Google account with Sheets access Customer phone numbers in WooCommerce (stored in billing.phone field) How to Use — Step-by-Step Setup 1. Credentials Setup WooCommerce API: Configure WooCommerce API credentials in n8n (e.g., "WooCommerce (get customer)" and "WooCommerce (get customer data)") Rapiwa Bearer Auth: Create an HTTP Bearer credential with your Rapiwa API token Google Sheets OAuth2: Set up OAuth2 credentials for Google Sheets access 2. Configure Google Sheets Ensure your sheet has the required columns as specified in the Google Sheet Column Structure section 3. Verify Code Nodes Code (get paying_customer): Filters customers to include only those who have made purchases Get most buy product id & Clear Number: Identifies the most purchased product and cleans phone numbers 4. Configure HTTP Request Nodes Get customer data: Verify the WooCommerce API endpoint for retrieving customer orders Get specific product data: Verify the WooCommerce API endpoint for product details Get specific product recommend latest product: Verify the WooCommerce API endpoint for finding latest products by category Check valid WhatsApp number Using Rapiwa: Verify the Rapiwa endpoint for WhatsApp number validation Rapiwa Sender: Verify the Rapiwa endpoint for sending messages Google Sheet Required Columns You’ll need two Google Sheets (or two tabs in one spreadsheet): A Google Sheet formatted like this ➤ sample The workflow uses a Google Sheet with the following columns to track coupon distribution: Both must have the following headers (match exactly): | name | number | email | address1 | price | suk | title | product link | validity | staus | | ---------- | ------------- | ----------------------------------------------- | ----------- | ----- | --- | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------- | -------- | | Abdul Mannan | 8801322827799 | contact@spagreen.net | mirpur dohs | 850 | | Sharp Most Demanding Hoodie x Nike | https://your_shop_domain/p-img-nike | verified | sent | | Abdul Mannan | 8801322827799 | contact@spagreen.net | mirpur dohs | 850 | | Sharp Most Demanding Hoodie x Nike | https://your_shop_domain/p-img-nike | unverified | not sent | | Abdul Mannan | 8801322827799 | contact@spagreen.net | mirpur dohs | 850 | | Sharp Most Demanding Hoodie x Nike | https://your_shop_domain/p-img-nike | verified | sent | Important Notes Phone Number Format:** The workflow cleans phone numbers by removing all non-digit characters. Ensure your WooCommerce phone numbers are in a compatible format. API Rate Limits:** Rapiwa and WooCommerce APIs have rate limits. Adjust batch sizes and wait times accordingly. Data Privacy:** Ensure compliance with data protection regulations when sending marketing messages. Error Handling:** The workflow logs unverified numbers but doesn't have extensive error handling. Consider adding error notifications for failed API calls. Product Availability:** The workflow recommends the latest product in a category, but doesn't check if it's in stock. Consider adding stock status verification. Testing:** Always test with a small batch before running the workflow on your entire customer list. Useful Links Dashboard:** https://app.rapiwa.com Official Website:** https://rapiwa.com Documentation:** https://docs.rapiwa.com Support & Help WhatsApp**: Chat on WhatsApp Discord**: SpaGreen Community Facebook Group**: SpaGreen Support Website**: https://spagreen.net Developer Portfolio**: Codecanyon SpaGreen
by Dr. Firas
💥 Generate UGC Promo Videos with Blotato and Sora 2 for eCommerce 🧩 Who is this for? This workflow is perfect for eCommerce brands, content creators, and marketing teams who want to automatically generate short, eye-catching videos from their product images — without editing software or manual work. 🚀 What problem does this workflow solve? Creating engaging promotional videos manually can be time-consuming and expensive. This automation eliminates that friction by combining Blotato, Sora 2, and AI scripting to turn static product images into dynamic UGC-style videos ready for TikTok, Instagram Reels, and YouTube Shorts. ⚙️ What this workflow does This workflow: Receives a product image directly from Telegram or another input source. Analyzes the image with OpenAI Vision to understand the product’s features and audience. Generates a natural, short UGC-style script using GPT-based AI. Sends the image and script to Sora 2 via the Fal API to generate a vertical promotional video. Monitors the video status every 15 seconds until completion. Downloads or automatically publishes the final video to your social platforms. 🧠 Setup Create a Fal.ai API key and set it in your n8n credentials (Authorization: Key YOUR_FAL_KEY). Connect your Telegram, OpenAI, and HTTP Request nodes as shown in the workflow. Make sure the Build Public Image URL node outputs a valid, public image link. In the HTTP Request node for Sora 2, set: Method: POST URL: https://fal.run/fal-ai/sora-2/image-to-video Headers: Authorization: Key YOUR_FAL_KEY Content-Type: application/json Body: Raw JSON with parameters like prompt, image_url, duration, and aspect_ratio. Run the workflow and monitor the execution logs for your video URL. Blotato → API key for social media publishing 🎨 How to customize this workflow to your needs 🧾 Change the video tone: Edit the OpenAI prompt to produce educational, emotional, or luxury-style scripts. 🎬 Adjust duration or format: Use Sora 2’s supported durations (4, 8, or 12 seconds) and aspect ratios (e.g., 9:16 for social media). 📲 Auto-publish your videos: Connect the TikTok, Instagram, or YouTube upload nodes for full automation. ✨ Add branding: Include overlays, logos, or end screens via CapCut or an external API integration. 🎥 Watch This Tutorial 👋 Need help or want to customize this? 📩 Contact: LinkedIn 📺 YouTube: @DRFIRASS 🚀 Workshops: Mes Ateliers n8n 📄 Documentation: Notion Guide Need help customizing? Contact me for consulting and support : Linkedin / Youtube / 🚀 Mes Ateliers n8n
by Samir Saci
Tags: ESL, English Learning, Podcasts, RSS, AI Exercises, ElevenLabs Context Hi! I’m Samir, Supply Chain Engineer and Data Scientist based in Paris, and founder of the startup LogiGreen. I created this workflow for my mother, who is currently learning English, to turn the BBC 6 Minute English podcast into ready-to-use English lessons. The lesson includes vocabulary, exercises and discussion questions along with links to access the podcast content (audio and transcript). > Use this assistant to automatically share English lessons from a renowned podcast. 📬 For business inquiries, you can find me on LinkedIn Who is this template for? This template is designed for: ESL teachers** who want a fresh, structured lesson every week from real-life audio Independent learners** who want a guided way to study English with podcasts Language schools or content creators** who send regular English lessons by email What does this workflow do? This workflow acts as an AI-powered English lesson generator from podcast episodes. Runs every Sunday at 20:00 using a Schedule Trigger and reads the BBC 6 Minute English RSS feed Checks a Data Table of archived episodes and filters out those already sent (using their guid) Keeps the latest unsent episode and loads its web page content via HTTP Parses the HTML in a Code node to extract the episode description, full transcript and BBC vocabulary list Calls three AI nodes (OpenAI) to generate: a motivational email hook message fill-in-the-blank vocabulary exercises discussion questions related to the topic Combines all vocabulary words and sends them to ElevenLabs to generate a slow-paced audio track for listening practice Builds a prettify HTML email that includes: title, description, hook, vocabulary list, exercises, discussion questions and resource links Sends the final lesson by email via the Gmail node, with the vocabulary audio attached For example, this is the latest email generated by the workflow: P.S.: You can customise the footer to your school or company identity. 🎥 Tutorial I advise you to check the tutorial on my YouTube channel for the details on how to set up the nodes and customise the content: Next Steps Follow the stickers to set up all the nodes: Replace the Data Table reference with your own (storing at least guid, title, link, processed_date) Set up your OpenAI credentials in the three Model nodes Set up your ElevenLabs credentials and choose a voice in the audio node Configure your Gmail credentials and recipient email address in the Send Email node Adapt the RSS feed URL if you want to track another podcast or source Customise the HTML email (colours, logo, footer text) in the Prepare Email Code node Adjust the schedule (time or frequency) if you prefer another cadence Submitted: 18 November 2025 Template designed with n8n version 1.116.2
by Yang
🛍️ 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).
by Colton Randolph
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.
by Robert Breen
This n8n workflow template automatically processes phone interview transcripts using AI to evaluate candidates against specific criteria and saves the results to Google Sheets. Perfect for HR departments, recruitment agencies, or any business conducting phone screenings. What This Workflow Does This automated workflow: Receives phone interview transcripts via webhook Uses OpenAI GPT models to analyze candidate responses against predefined qualification criteria Extracts key information (name, phone, location, qualification status) Automatically saves structured results to a Google Sheet for easy review and follow-up The workflow is specifically designed for driving job interviews but can be easily adapted for any position with custom evaluation criteria. Tools & Services Used N8N** - Workflow automation platform OpenAI API** - AI-powered transcript analysis (GPT-4o-mini) Google Sheets** - Data storage and management Webhook** - Receiving transcript data Prerequisites Before implementing this workflow, you'll need: N8N Instance - Self-hosted or cloud version OpenAI API Account - For AI transcript processing Google Account - For Google Sheets integration Phone Interview System - That can send webhooks (like Vapi.ai) Step-by-Step Setup Instructions Step 1: Set Up OpenAI API Access Visit OpenAI's API platform Create an account or log in Navigate to API Keys section Generate a new API key Copy and securely store your API key Step 2: Create Your Google Sheet Option 1: Use Our Pre-Made Template (Recommended) Copy our template: Driver Interview Results Template Click "File" → "Make a copy" to create your own version Rename it as desired Copy your new sheet's URL - you'll need this for the workflow Option 2: Create From Scratch Go to Google Sheets Create a new spreadsheet Name it "Driver Interview Results" (or your preferred name) Set up the following column headers in row 1: A1: name B1: phone C1: cityState D1: qualifies E1: reasoning Copy the Google Sheet URL - you'll need this for the workflow Step 3: Import and Configure the N8N Workflow Import the Workflow Copy the workflow JSON from the template In your N8N instance, go to Workflows → Import from JSON Paste the JSON and import Configure OpenAI Credentials Click on either "OpenAI Chat Model" node Set up credentials using your OpenAI API key Test the connection to ensure it works Configure Google Sheets Integration Click on the "Save to Google Sheets" node Set up Google Sheets OAuth2 credentials Select your spreadsheet from the dropdown Choose the correct sheet (usually "Sheet1") Update the Webhook Click on the "Webhook" node Note the webhook URL that n8n generates This URL will receive your transcript data Step 4: Customize Evaluation Criteria The workflow includes predefined criteria for a Massachusetts driving job. To customize for your needs: Click on the "Evaluate Candidate" node Modify the system message to include your specific requirements Update the evaluation criteria checklist Adjust the JSON output format if needed Current Evaluation Criteria: Valid Massachusetts driver's license No felony convictions Clean driving record (no recent tickets/accidents) Willing to complete background check Can pass drug test (including marijuana) Available full-time Monday-Friday Lives in Massachusetts Step 5: Connect to Vapi.ai (Phone Interview System) This workflow is specifically designed to work with Vapi.ai's phone interview system. Here's how to connect it: Setting Up the Vapi Integration Copy Your N8N Webhook URL In your n8n workflow, click on the "Webhook" node Copy the webhook URL (it should look like: https://your-n8n-instance.com/webhook-test/351ffe7c-69f2-4657-b593-c848d59205c0) Configure Your Vapi Assistant Log into your Vapi.ai dashboard Create or edit your phone interview assistant In the assistant settings, find the "Server" section Set the Server URL to your n8n webhook URL Set timeout to 20 seconds (as configured in the workflow) Configure Server Messages In your Vapi assistant settings, enable these server messages: end-of-call-report transcript[transcriptType="final"] Set Up the Interview Script Use the provided interview script in your Vapi assistant (found in the workflow's system message) This ensures consistent data collection for the AI evaluation Expected Data Format from Vapi The workflow expects Vapi to send data in this specific format: { "body": { "message": { "artifact": { "transcript": "AI: Hi. Are you interested in driving for Bank of Transport?\nUser: Yes.\nAI: Great. Before we go further..." } } } } Vapi Configuration Checklist ✅ Webhook URL set in Vapi assistant server settings ✅ Server messages enabled: end-of-call-report, transcript[transcriptType="final"] ✅ Interview script configured in assistant ✅ Assistant set to send webhooks on call completion Alternative Phone Systems If you're not using Vapi.ai, you can adapt this workflow for other phone systems by: Modifying the "Edit Fields2" node to extract transcripts from your system's data format Updating the webhook data structure expectations Ensuring your phone system sends the complete interview transcript Step 6: Test the Workflow Test with Sample Data Use the "Execute Workflow" button with test data Verify that data appears correctly in your Google Sheet Check that the AI evaluation logic works as expected End-to-End Testing Send a test webhook with a real transcript Monitor each step of the workflow Confirm the final result is saved to Google Sheets Workflow Node Breakdown Webhook - Receives transcript data from your phone system Edit Fields2 - Extracts the transcript from the incoming data Evaluate Candidate - AI analysis using GPT-4o-mini to assess qualification Convert to JSON - Ensures proper JSON formatting with structured output parser Save to Google Sheets - Automatically logs results to your spreadsheet Customization Options Modify Evaluation Criteria Edit the system prompt in the "Evaluate Candidate" node Add or remove qualification requirements Adjust the scoring logic Change Output Format Modify the JSON schema in the "Structured Output Parser" node Update Google Sheets column mapping accordingly Add Additional Processing Insert nodes for email notifications Add Slack/Discord alerts for qualified candidates Integrate with your CRM or ATS system Troubleshooting Common Issues: OpenAI API Errors**: Check API key validity and billing status Google Sheets Not Updating**: Verify OAuth permissions and sheet access Webhook Not Receiving Data**: Confirm URL and POST format from your phone system AI Evaluation Inconsistencies**: Refine the system prompt with more specific criteria Usage Tips Monitor Token Usage**: OpenAI charges per token, so monitor your usage Regular Review**: Periodically review AI evaluations for accuracy Backup Data**: Export Google Sheets data regularly for backup Privacy Compliance**: Ensure transcript handling complies with local privacy laws Need Help with Implementation? For professional setup, customization, or troubleshooting of this workflow, contact: Robert - Ynteractive Solutions Email**: rbreen@ynteractive.com Website**: www.ynteractive.com LinkedIn**: linkedin.com/in/robert-interactive Specializing in AI-powered workflow automation, business process optimization, and custom integration solutions.
by Pratyush Kumar Jha
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
by Rohit Dabra
Jira MCP Server Integration with n8n Overview Transform your Jira project management with the power of AI and automation! This n8n workflow template demonstrates how to create a seamless integration between chat interfaces, AI processing, and Jira Software using MCP (Model Context Protocol) server architecture. What This Workflow Does Chat-Driven Automation**: Trigger Jira operations through simple chat messages AI-Powered Issue Creation**: Automatically generate detailed Jira issues with descriptions and acceptance criteria Complete Jira Management**: Get issue status, changelogs, comments, and perform full CRUD operations Memory Integration**: Maintain context across conversations for smarter automations Zero Manual Entry**: Eliminate repetitive data entry and human errors Key Features ✅ Natural Language Processing: Use Google Gemini to understand and process chat requests ✅ MCP Server Integration: Secure, efficient communication with Jira APIs ✅ Comprehensive Jira Operations: Create, read, update, delete issues and comments ✅ Smart Memory: Context-aware conversations for better automation ✅ Multi-Action Workflow: Handle multiple Jira operations from a single trigger Demo Video 🎥 Watch the Complete Demo: Automate Jira Issue Creation with n8n & AI | MCP Server Integration Prerequisites Before setting up this workflow, ensure you have: n8n instance** (cloud or self-hosted) Jira Software** account with appropriate permissions Google Gemini API** credentials MCP Server** configured and accessible Basic understanding of n8n workflows Setup Guide Step 1: Import the Workflow Copy the workflow JSON from this template In your n8n instance, click Import > From Text Paste the JSON and click Import Step 2: Configure Google Gemini Open the Google Gemini Chat Model node Add your Google Gemini API credentials Configure the model parameters: Model: gemini-pro (recommended) Temperature: 0.7 for balanced creativity Max tokens: As per your requirements Step 3: Set Up MCP Server Connection Configure the MCP Client node: Server URL: Your MCP server endpoint Authentication: Add required credentials Timeout: Set appropriate timeout values Ensure your MCP server supports Jira operations: Issue creation and retrieval Comment management Status updates Changelog access Step 4: Configure Jira Integration Set up Jira credentials in n8n: Go to Credentials > Add Credential Select Jira Software API Add your Jira instance URL, email, and API token Configure each Jira node: Get Issue Status: Set project key and filters Create Issue: Define issue type and required fields Manage Comments: Set permissions and content rules Step 5: Memory Configuration Configure the Simple Memory node: Set memory key for conversation context Define memory retention duration Configure memory scope (user/session level) Step 6: Chat Trigger Setup Configure the When Chat Message Received trigger: Set up webhook URL or chat platform integration Define message filters if needed Test the trigger with sample messages Usage Examples Creating a Jira Issue Chat Input: Can you create an issue in Jira for Login Page with detailed description and acceptance criteria? Expected Output: New Jira issue created with structured description Automatically generated acceptance criteria Proper labeling and categorization Getting Issue Status Chat Input: What's the status of issue PROJ-123? Expected Output: Current issue status Last updated information Assigned user details Managing Comments Chat Input: Add a comment to issue PROJ-123: "Ready for testing in staging environment" Expected Output: Comment added to specified issue Notification sent to relevant team members Customization Options Extending Jira Operations Add more Jira operations (transitions, watchers, attachments) Implement custom field handling Create multi-project workflows AI Enhancement Fine-tune Gemini prompts for better issue descriptions Add custom validation rules Implement approval workflows Integration Expansion Connect to Slack, Discord, or Teams Add email notifications Integrate with time tracking tools Troubleshooting Common Issues MCP Server Connection Failed Verify server URL and credentials Check network connectivity Ensure MCP server is running and accessible Jira API Errors Validate Jira credentials and permissions Check project access rights Verify issue type and field configurations AI Response Issues Review Gemini API quotas and limits Adjust prompt engineering for better results Check model parameters and settings Performance Tips Optimize memory usage for long conversations Implement rate limiting for API calls Use error handling and retry mechanisms Monitor workflow execution times Best Practices Security: Store all credentials securely using n8n's credential system Testing: Test each node individually before running the complete workflow Monitoring: Set up alerts for workflow failures and API limits Documentation: Keep track of custom configurations and modifications Backup: Regular backup of workflow configurations and credentials Happy Automating! 🚀 This workflow template is designed to boost productivity and eliminate manual Jira management tasks. Customize it according to your team's specific needs and processes.
by WeblineIndia
Real-Time WooCommerce Return Surge Detection with Slack Alerts & Airtable Logging This n8n workflow monitors WooCommerce refund activity to detect unusual spikes in product returns at the SKU level. It compares return volumes across rolling 24-hour windows, alerts teams in Slack when defined thresholds are exceeded and logs all detected events into Airtable for tracking and analysis. 🚀 Quick Start – Get This Running Fast Import the workflow into n8n. Connect your WooCommerce API credentials. Configure Slack and Airtable credentials. Set your preferred schedule interval. Activate the workflow and start monitoring returns automatically. What It Does This workflow is designed to automatically detect abnormal return behavior in a WooCommerce store. On every scheduled run, it fetches recent orders and refunds directly from the WooCommerce REST API. Refund records are mapped back to their original orders to accurately identify affected SKUs. Using a rolling time-window comparison, the workflow calculates current versus previous return counts per SKU. It identifies significant increases—either large percentage spikes or unusually high absolute return volumes. This ensures early detection of potential product quality, packaging or fulfillment issues. When a return surge is detected, the workflow sends a structured alert to a Slack channel and stores the alert data in Airtable. This creates a searchable, historical log that supports investigations, trend analysis and operational decision-making. Who’s It For This workflow is ideal for: eCommerce operations teams. Quality assurance and product managers. Customer support leads. Supply chain and fulfillment teams. Store owners running WooCommerce at scale. Requirements to Use This Workflow To use this workflow, you will need: An active WooCommerce store with REST API access. WooCommerce API credentials** (Consumer Key & Secret). An active Slack workspace with permission to post messages. An Airtable base and table for logging alerts. An n8n instance (self-hosted or cloud). How It Works & How To Set Up Workflow Execution Flow Schedule Trigger runs the workflow at a fixed interval. Time Window node defines current and previous 24-hour comparison windows. HTTP Orders fetches recent WooCommerce orders. HTTP Refunds fetches refund records. Orders_Fetch (Code) maps refunds to parent orders and extracts SKU-level data. Refund_details (Code) aggregates returns, compares windows, and calculates increases. IF Node checks surge conditions: ≥100% increase OR ≥25 current returns Set Fields enriches data with status, run date, and cooldown key. Slack Node sends a formatted alert message. Code Node normalizes Slack output into structured fields. Airtable Node stores alert records for future reference. Setup Instructions Replace {your_woocommerce_domain} with your actual store domain. Verify WooCommerce API permissions allow order and refund access. Select the correct Slack channel in the Slack node. Ensure Airtable column names match the workflow mappings. How To Customize Nodes You can easily adapt this workflow by: Changing the schedule frequency in the Schedule Trigger. Adjusting WINDOW_HOURS in the Code nodes. Modifying alert thresholds in the IF node. Customizing the Slack message format. Adding or removing Airtable fields for reporting needs. Add-ons (Optional Enhancements) This workflow can be extended with: Email or Microsoft Teams notifications. Jira or Linear ticket creation. Product auto-pause for extreme return spikes. Dashboard reporting using BI tools. Cooldown logic to prevent repeated alerts per SKU. Use Case Examples Common use cases include: Detecting defective product batches early. Identifying packaging or shipping damage trends. Monitoring supplier quality issues. Supporting refund root-cause analysis. Improving customer satisfaction metrics. There can be many more operational and analytical use cases based on your business needs. Troubleshooting Guide | Issue | Possible Cause | Solution | |------|---------------|----------| | No Slack alerts | Threshold not met | Lower IF condition limits | | Empty SKU values | Missing SKU in WooCommerce | Use product name or ID fallback | | No data in Airtable | Column mismatch | Verify field names and types | | API errors | Invalid credentials | Re-authorize WooCommerce API | | Duplicate alerts | Frequent schedule | Add cooldown or deduplication logic | Need Help? Need assistance setting this up or customizing it for your business? WeblineIndia can help you implement, extend or build similar automation workflows tailored to your operational needs. Whether you want advanced alerting, deeper analytics or cross-system integrations, our team is ready to help you get the most out of n8n automation.