# templates

name: Automatically Import your Meta Threads Posts into Notion
author_name: Geekaz / Kazen
author_username: geekaz
categories: []
created_at: 2025-01-26T13:04:30.670Z
updated_at: 2025-01-26T13:04:30.670Z
url: https://n8n.io/workflows/2802
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?
  This template is designed for social media managers, content creators, data analysts, and anyone who wants to automatically save and analyze their Meta Threads posts in Notion.

  It’s particularly useful for:

  Building a personal archive of your Threads content.
  Training AI models using your social media data.
  Tracking your online presence and engagement.

  What this workflow does
  This workflow uses the Meta Threads API to automatically retrieve your posts and import them into a Notion database.

  It retrieves the post content, date, and time, and stores them in designated properties within your Notion database.

  Setup
  Get Threads Access Token and ID: Obtain a long-lived access token and your Threads ID from the Meta Threads developer platform. This token auto-refreshes, ensuring uninterrupted workflow operation.

  Configure Credentials and Date Range: In the “Set Credentials” node (using edit fields), enter your token and ID. 

  Set the since and until parameters in the “Set Date Range” node to specify the post import period.

  Connect to Notion and Create a Database: Connect to your Notion workspace and create a database with these properties (customize with the “Create Properties” node):

  a. Title: Threads post URL (Notion entry title).

  b. Threads ID: Unique post ID (prevents duplicate imports).

  c. Username: Post author (for future multi-account/source management).

  d. Post Date: Original post date.

  e. Source (Multi-Select): “Threads” tag (for future multi-platform import and filtering).

  f. Created: Import date and time.

  g. Import Check (Optional): For use with a separate post-categorization workflow.

nodes_used:
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workflow_json_compressed:
  [encoded data, 7332 characters]


name: Generate Instagram Content from Top Trends with AI Image Generation
author_name: mustafa kendigüzel
author_username: mustafakendiguzel
categories: []
created_at: 2025-01-26T14:36:11.785Z
updated_at: 2025-01-26T14:36:11.785Z
url: https://n8n.io/workflows/2803
scraped_at: 2025-12-24 13:02:41
description:
  How it works

  This automated workflow discovers trending Instagram posts and creates similar AI-generated content. Here's the high-level process:

  1. Content Discovery & Analysis
  Scrapes trending posts from specific hashtags
  Analyzes visual elements using AI
  Filters out videos and duplicates

  2. AI Content Generation
  Creates unique images based on trending content
  Generates engaging captions with relevant hashtags
  Maintains brand consistency while being original

  3. Automated Publishing
  Posts content directly to Instagram
  Monitors publication status
  Sends notifications via Telegram

  Set up steps

  Setting up this workflow takes approximately 15-20 minutes:

  1. API Configuration (7-10 minutes)
  Instagram Business Account setup
  Telegram Bot creation
  API key generation (OpenAI, Replicate, Rapid Api)

  2. Database Setup (3-5 minutes)
  Create required database table
  Configure PostgreSQL credentials

  3. Workflow Configuration (5-7 minutes)
  Set scheduling preferences
  Configure notification settings
  Test connection and permissions

  Detailed technical specifications and configurations are available in sticky notes within the workflow.

nodes_used:
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workflow_json_compressed:
  [encoded data, 9700 characters]


name: Analyse papers from Hugging Face with AI and store them in Notion
author_name: AI Native
author_username: ainative
categories: []
created_at: 2025-01-26T15:19:36.239Z
updated_at: 2025-01-26T15:19:36.239Z
url: https://n8n.io/workflows/2804
scraped_at: 2025-12-24 13:02:46
description:
  This workflow automates the process of retrieving Hugging Face paper summaries, analyzing them with OpenAI, and storing the results in Notion. Here’s a breakdown of how it works:

  ⏰ Scheduled Trigger: 
  The flow is set to run automatically at 8 AM on weekdays.
  📄 Fetching Paper Data: 
  It fetches Hugging Face paper summaries using their API.
  🔍 Data Check: 
  Before processing, the workflow checks if the paper already exists in Notion to avoid duplicates.
  🤖 Content Analysis with OpenAI: 
  If the paper is new, it extracts the summary and uses OpenAI to analyze the content.
  📥 Store Results in Notion: 
  After analysis, the summarized data is saved in Notion for easy reference.

  ⚙️ Set Up Steps for Automation

  Follow these steps to set up this automated workflow with Hugging Face, OpenAI, and Notion integration:

  🔑 Obtain API Tokens: 
  You’ll need the Notion and OpenAI API tokens to authenticate and connect these services with n8n.
  🔗 Integration in n8n: 
  Link Hugging Face, OpenAI, and Notion by configuring the appropriate nodes in n8n.
  🔧 Configure Workflow Logic:
     Set up a cron trigger for automatic execution at 8 AM on weekdays.
     Use an HTTP request node to fetch Hugging Face paper data.
     Add logic to check if the data already exists in Notion.
     Set up the OpenAI integration to analyze the paper’s summary.
     Store the results in Notion for easy access and reference.

  Result:

   

nodes_used:
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name: Todoist weekly email of completed tasks
author_name: Martijn Smit
author_username: smitmartijn
categories: []
created_at: 2025-01-26T16:27:59.627Z
updated_at: 2025-01-26T16:27:59.627Z
url: https://n8n.io/workflows/2805
scraped_at: 2025-12-24 13:02:47
description:
  This workflow template helps Todoist users get a weekly overview of their completed tasks via email, making it easier to review their past week.

  Why use this workflow?
  Todoist doesn’t provide completed task reports or filters in its built-in reports or n8n app. This workflow solves that by using Todoist’s public API to fetch your completed tasks.

  How it works
  Runs every Friday afternoon (or manually).
  Uses the Todoist public API to retrieve completed tasks.
  Excludes specific projects you set (e.g., a grocery list).
  Sends an email summary, grouping tasks by the day they were completed.

  Set up steps
  Copy your Todoist API token (found here).
  Create a Todoist API credential in n8n.
  Create an SMTP credential in n8n.
     Alternatively, use a preferred email service like Brevo, Mailjet, etc.
  Import this workflow template.
  In the Get completed tasks via Todoist API step, select your Todoist API credential.
  In the Send Email step:
     Select your SMTP credential.
     Set the sender and recipient email addresses.
  Run the workflow manually and check your inbox!

  Ignoring specific projects
  If you do not want your grocery list, workouts, or other tasks from specific Todoist projects showing up in your weekly summary, modify the step called Optional: Ignore specific projects and change this line:

  const ignoredProjects = ['2335544024'];

  This should be an array with the id of each project you'd like to ignore. You can find a list of your projects (inc. their Ids) by visiting this link: https://api.todoist.com/rest/v2/projects 

nodes_used:
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name: Create QuickBooks Online Customers With Sales Receipts For New Stripe Payments
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-01-27T13:06:22.575Z
updated_at: 2025-01-27T13:06:22.575Z
url: https://n8n.io/workflows/2807
scraped_at: 2025-12-24 13:02:46
description:
  Streamline your accounting by automatically creating QuickBooks Online customers and sales receipts whenever a successful Stripe payment is processed. Ideal for businesses looking to reduce manual data entry and improve accounting efficiency.

  How it works

  Trigger: The workflow is triggered when a new successful payment intent event is received from Stripe.
  Retrieve Customer Data: Fetches customer details from Stripe associated with the payment.
  Check QuickBooks Customer: Searches QuickBooks Online to see if the customer already exists using their email address.
  Create or Use Existing Customer: If the customer doesn't exist in QuickBooks, they are created; otherwise, the existing customer is used.
  Generate Sales Receipt: A sales receipt is created in QuickBooks Online with payment details, including item descriptions, amounts, and currency.

  Set up steps

  Connect Accounts: Authenticate both your QuickBooks Online and Stripe accounts in n8n.
  Webhook Setup: Configure the Stripe webhook to send payment_intent.succeeded events to this workflow.
  Test the Workflow: Trigger a test payment in Stripe to validate the integration.
  Customize Details: Adjust item descriptions or other fields in the QuickBooks sales receipt JSON body as needed.

  This workflow requires basic familiarity with n8n, but setup can be completed in under 15 minutes for most users.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stripe","n8n-nodes-base.stripeTrigger","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.quickbooks","n8n-nodes-base.merge","n8n-nodes-base.merge","n8n-nodes-base.httpRequest"]

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name: Enrich Linkedin Profiles from Google Sheets via RapidAPI
author_name: PollupAI
author_username: Pollup
categories: []
created_at: 2025-01-27T14:52:02.869Z
updated_at: 2025-01-27T14:52:02.869Z
url: https://n8n.io/workflows/2808
scraped_at: 2025-12-24 13:02:46
description:
  LinkedIn Profile Enrichment Workflow

  Who is this for?

  This workflow is ideal for recruiters, sales professionals, and marketing teams who need to enrich LinkedIn profiles with additional data for lead generation, talent sourcing, or market research.

  What problem is this workflow solving?

  Manually gathering detailed LinkedIn profile information can be time-consuming and prone to errors. This workflow automates the process of enriching profile data from LinkedIn, saving time and ensuring accuracy.

  What this workflow does

  Input: Reads LinkedIn profile URLs from a Google Sheet.
  Validation: Filters out already enriched profiles to avoid redundant processing.
  Data Enrichment: Uses RapidAPI's Fresh LinkedIn Profile Data API to retrieve detailed profile information.
  Output: Updates the Google Sheet with enriched profile data, appending new information efficiently.

  Setup

  Google Sheet: Create a sheet with a column named linkedin_url and populate it with the profile URLs to enrich.
  RapidAPI Account: Sign up at RapidAPI and subscribe to the Real-Time Data Enrichment API.
  API Integration: Replace the x-rapidapi-key and x-rapidapi-host values with your credentials from RapidAPI.
  Run the Workflow: Trigger the workflow and monitor the updates to your Google Sheet.

  How to customize this workflow

  Filter Criteria**: Modify the filter step to include additional conditions for processing profiles.
  API Configuration**: Adjust API parameters to retrieve specific fields or extend usage.
  Output Format**: Customize how the enriched data is appended to the Google Sheet (e.g., format, column mappings).
  Error Handling**: Add steps to handle API rate limits or missing data for smoother automation.

  This workflow streamlines LinkedIn profile enrichment, making it faster and more effective for data-driven decision-making.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.filter","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote"]

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name: Microsoft Outlook AI Email Assistant with contact support from Monday and Airtable
author_name: Cognitive Creators
author_username: cognitivecreators
categories: []
created_at: 2025-01-28T05:04:49.156Z
updated_at: 2025-01-28T05:04:49.156Z
url: https://n8n.io/workflows/2809
scraped_at: 2025-12-24 13:02:45
description:
  Microsoft Outlook AI Email Assistant

  Prerequisites

  1. Microsoft 365 Login Credentials
  Provide your Office 365 credentials to connect Outlook.

  2. Monday.com
  Generate an API token and have a board with your contact details.

  3. Airtable
  Obtain an API key (or personal access token) and set up a base to store:
    Contacts (populated by the Monday.com sync).
    Rules & Categories (used by the AI Email Assistant).
  Use this Airtable base as the template: Airtable AI Email Assistant Template. Define your own rules, categories, and delete rules.

  4. OpenAI API Key
  Sign up for OpenAI if you don’t already have an account.
  Generate a new API key at OpenAI API Keys.

  What the System Does

  1. Daily Contact Sync (Monday.com → Airtable)
  Runs once a day to pull the latest contacts from Monday.com and store or update them in Airtable.

  2. AI Email Categorisation & Prioritisation
  Fetches Outlook emails with filters.
  Cleans and processes email content.
  Matches emails with known contacts from Airtable.
  Uses an AI agent to classify, categorise, and prioritise emails.
  Updates Outlook categories and importance based on AI results.
  Runs in parallel with Airtable rules & categories retrieval for real-time decision-making.

  Workflow 1: Daily Contact Sync (Monday.com → Airtable)

  Purpose
  Keep Airtable’s Contacts table up to date by pulling new or updated contact data from Monday.com daily.

  Steps
  Schedule Trigger  
     Runs at a set interval (daily) to initiate contact syncing.

  Monday.com: Get Contacts  
     Reads the specified board/columns from Monday.com where you store contact details.

  Airtable - Contacts  
     Upserts (adds or updates) the fetched Monday.com data into Airtable’s Contacts table.
     Ensures daily updates reflect changes from Monday.com.

  Result
  A consolidated contact list in Airtable, ready for AI email categorisation.

  Workflow 2: Categorise & Prioritise Outlook Emails

  Purpose
  Fetches Outlook emails, cleans and processes their content, matches senders with known contacts, and uses AI to categorise and prioritise them.

  Steps

  1. Get Outlook Emails with Filters
  Trigger**: Either scheduled (Check Mail Schedule Trigger) or manual (Test Workflow).
  Outlook Filters**:
    Not flagged (flag/flagStatus == 'notFlagged').
    Not categorised (not categories/any()).

  🔹 Result: A batch of fresh, unprocessed emails ready for processing.

  2. Sanitise Email
  Convert to Markdown: Strips **HTML tags and normalises formatting.
  Email Messages Processing: Allows manual removal of **signatures, disclaimers, or extra content.

  🔹 Result: A clean, AI-friendly email for categorisation.

  3. Match Contact
  Loop Over Emails**: Iterates over each email.
  Contact Lookup: Checks Airtable’s **Contacts table (updated daily).
  Merge Data: Enriches emails with known **client, supplier, or internal team info.

  🔹 Result: Enhanced email context for AI processing.

  4. AI Agent to Categorise & Prioritise
  Retrieve Rules & Categories**  
    Reads Rules, Categories, and Delete Rules from Airtable in parallel.
  AI: Analyse Email (Tools Agent)**  
    Uses email text, sender info, and rules to build a structured AI prompt.
  OpenAI Chat Model**  
    Processes the AI prompt and outputs:
      Category
      Subcategory (optional)
      Priority level
      Short rationale
  Structured Output Parser**  
    Ensures AI response is valid JSON format.

  🔹 Result:  
  Each email is labelled, categorised, and prioritised with AI-driven logic.

  5. Set Outlook Category & Importance
  Set Category: Updates Outlook with the assigned **category.
  Check Priority Conditions** (If Node):  
    If Action Required or from a VIP, mark as High Priority.
  Set Importance: Updates the email's **importance flag in Outlook.

  🔹 Result:  
  Outlook is updated with categories & importance based on AI recommendations.

  Parallel Processing: Retrieve Rules & Categories
  Runs alongside the email categorisation workflow.
  Ensures Airtable-based rules are available before AI processing.

  Steps
  Airtable: Get Rules & Categories  
     Fetches Rules, Categories, and Delete Rules from Airtable.
  Delete Rules (Optional)  
     If a delete rule matches, the email is removed.

  🔹 Result:  
  A dynamic, updatable rule system ensuring emails are handled properly.

  Final Outcome
  Daily Contact Sync** keeps contacts up to date.
  AI-driven email workflow** ensures smart categorisation.
  Outlook automatically updated** with AI-generated categories and importance.

  This automated system saves time, ensures efficient inbox management, and allows for customisable rules via Airtable.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.if","n8n-nodes-base.microsoftOutlook","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.mondayCom","n8n-nodes-base.airtable","n8n-nodes-base.markdown","n8n-nodes-base.set","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.airtable","n8n-nodes-base.splitInBatches","n8n-nodes-base.merge","n8n-nodes-base.stickyNote"]

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name: Scrape any web page into structured JSON data with ScrapeNinja and AI
author_name: Anthony
author_username: scrapeninja
categories: []
created_at: 2025-01-28T07:07:34.371Z
updated_at: 2025-01-28T07:07:34.371Z
url: https://n8n.io/workflows/2812
scraped_at: 2025-12-24 13:02:43
description:
  Disclaimer: This template only works on self-hosted for now, as it uses a community node.

  Use Case
  Web scrapers often break due to web page layout changes.
  This workflow attempts to mitigate this problem by auto-generating web scraping data extractor code via LLM.

  How It Works
  This workflow leverages ScrapeNinja n8n community node to: 
  scrape webpage HTML, 
  feed it into LLM (Google Gemini) and ask to write a JS extractor function code, then it 
  executes the written JS extractor against scraped HTML to extract useful data from webpage (the code is safely executed in a sandbox)

  Installation
  To install ScrapeNinja n8n node, in your self-hosted instance, go to Settings -&gt; Community nodes, enter "n8n-nodes-scrapeninja", and install. 

  Make sure you are using at least v0.3.0. 

  See this in action:
  https://www.linkedin.com/feed/update/urn:li:activity:7289659870935490560/

nodes_used:
  ["CUSTOM.scrapeNinja","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.manualTrigger","CUSTOM.scrapeNinja","@n8n/n8n-nodes-langchain.chainLlm","CUSTOM.scrapeNinja"]

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name: Automate Content Generator for WordPress with DeepSeek R1
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-28T11:15:09.521Z
updated_at: 2025-01-28T11:15:09.521Z
url: https://n8n.io/workflows/2813
scraped_at: 2025-12-24 13:02:41
description:
  This workflow is designed to generate SEO-friendly content with DeepSeek R1 (or V3), publish it on WordPress, and update a Google Sheets document with the details of the created post. Below is a detailed analysis of what each node in the workflow does:

  How It Works
  Triggering the Workflow:
     The workflow starts with a Manual Trigger node, which is activated when the user clicks "Test workflow" in the n8n interface.

  Fetching Data:
     The Get Ideas node retrieves data from a Google Sheets document. It reads a specific sheet and filters the data based on the "ID POST" column, returning the first matching row.

  Setting the Prompt:
     The Set your prompt node extracts the PROMPT field from the Google Sheets data and assigns it to a variable for use in subsequent steps.

  Generating Content:
     The Generate article node uses an AI model (DeepSeek) to create an SEO-friendly article based on the prompt. The article includes an introduction, 2-3 chapters, and a conclusion, formatted in HTML.
     The Generate title node uses the same AI model to generate a concise, SEO-optimized title for the article.

  Publishing on WordPress:
     The Create post on WordPress node creates a new draft post on WordPress using the generated title and article content.

  Generating and Uploading an Image:
     The Generate Image node creates a photorealistic image based on the article title using an AI model (OpenAI).
     The Upload image node uploads the generated image to WordPress as a media file.
     The Set Image node assigns the uploaded image as the featured image for the WordPress post.

  Updating Google Sheets:
     The Update Sheet node updates the original Google Sheets document with the post details, including the title, post ID, creation date, and row number.

  Set Up Steps
  Configure Google Sheets Integration:
     Set up the Google Sheets node to connect to your Google account and specify the document ID and sheet name to read from and update.

  Set Up AI Models:
     Configure the OpenAI nodes (for generating the article, title, and image) with the appropriate API credentials and model settings (e.g., deepseek-reasoner for text generation).

  Configure WordPress Integration:
     Set up the WordPress node with your WordPress site's API credentials to allow creating posts and uploading media.

  Define the Prompt and Content Structure:
     In the Set your prompt node, ensure the prompt variable is correctly mapped to the data from Google Sheets.
     In the Generate article and Generate title nodes, define the instructions for the AI model to generate the desired content.

  Set Up Image Generation:
     Configure the Generate Image node with the appropriate prompt and image settings (e.g., size, quality, style).

  Configure HTTP Requests for Media Upload:
     Set up the Upload image and Set Image nodes to use the WordPress REST API for uploading and assigning the featured image.

  Map Data for Google Sheets Update:
     In the Update Sheet node, map the relevant fields (e.g., title, post ID, date) to the appropriate columns in the Google Sheets document.

  Test and Activate the Workflow:
     Run the workflow manually to ensure all steps execute correctly.
     Once verified, activate the workflow for automated execution.

  Overall purpose of the workflow
  This workflow automates the creation of SEO-friendly content for a WordPress blog. Starting from a prompt extracted from a Google Sheets document, it generates an article, a title, and an image, publishes the post on WordPress, and updates the Google Sheets document with the details of the created post. This process is useful for blog managers who want to automate content creation and publishing.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.wordpress","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: 2-way Sync Google Contacts and Notion
author_name: Solomon
author_username: solomon
categories: []
created_at: 2025-01-28T14:00:09.385Z
updated_at: 2025-01-28T14:00:09.385Z
url: https://n8n.io/workflows/2814
scraped_at: 2025-12-24 13:02:47
description:
  Sync your Google Contacts with your Notion database.

  You can filter contacts by label or sync all contacts.

  All contact information is kept in sync between Google and Notion.

  What happens if I delete a contact?

  It gets deleted on the other end.

  Google Contact deleted → Notion page deleted
  Notion page deleted → Google contact deleted

  But you can easily change this behavior by disabling the respective deletion nodes.

  Who is this template for?

  Service providers looking to keep track of client information
  Organized individuals who like to centralize their lives in Notion
  Automation nerds who enjoy creating interesting projects

  How it works

  First, you perform an initial import to bring your contacts from Google to Notion.

  After that, the workflow listens for changes in both Notion and Google and keeps everything in sync.

  How to set up

  Instructions are included within the workflow itself.

  Check out my other templates

  👉 https://n8n.io/creators/solomon/

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name: Auto-Tag Blog Posts in WordPress with AI
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-01-28T16:50:58.256Z
updated_at: 2025-01-28T16:50:58.256Z
url: https://n8n.io/workflows/2816
scraped_at: 2025-12-24 13:02:45
description:
  How it works:
  This workflow automates tagging for WordPress posts using AI:

  Fetch blog post content and metadata.
  Generate contextually relevant tags using AI.
  Verify existing tags in WordPress and create new ones if necessary.
  Automatically update posts with accurate and optimized tags.

  Set up steps:

  Estimated time: ~15 minutes.

  Configure the workflow with your WordPress API credentials.
  Connect your content source (e.g., RSS feed or manual input).
  Adjust tag formatting preferences in the workflow settings.
  Run the workflow to ensure proper tag creation and assignment.

  This workflow is perfect for marketers and content managers looking to streamline their content categorization and improve SEO efficiency.

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.rssFeedReadTrigger","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.filter","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.wordpress","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: AI Social Media Caption Creator creates social media post captions in Airtable
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2025-01-29T08:07:40.999Z
updated_at: 2025-01-29T08:07:40.999Z
url: https://n8n.io/workflows/2817
scraped_at: 2025-12-24 13:02:46
description:
  Welcome to my AI Social Media Caption Creator Workflow!

  What this workflow does
  This workflow automatically creates a social media post caption in an editorial plan in Airtable. It also uses background information on the target group, tonality, etc. stored in Airtable.

  This workflow has the following sequence:

  Airtable trigger (scan for new records every minute)
  Wait 1 Minute so the Airtable record creator has time to write the Briefing field
  retrieval of Airtable record data
  AI Agent to write a caption for a social media post. The agent is instructed to use background information stored in Airtable (such as target group, tonality, etc.) to create the post.
  Format the output and assign it to the correct field in Airtable.
  Post the caption into Airtable record.

  Requirements
  Airtable Database: Documentation
  AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)

  Example of an editorial plan in Airtable:
  Editorial Plan example in Airtable

  For this workflow you need the Airtable fields "created_at", "Briefing" and "SoMe_Text_AI"

  Feel free to contact me via LinkedIn, if you have any questions!

nodes_used:
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name: Fetch Scriptures Dynamically from get Bible API
author_name: getBible
author_username: christian
categories: []
created_at: 2025-01-29T13:42:38.278Z
updated_at: 2025-01-29T13:42:38.278Z
url: https://n8n.io/workflows/2818
scraped_at: 2025-12-24 13:02:47
description:
  Overview
  The Get Bible Query Workflow is a modular and self-standing workflow designed to retrieve scriptures dynamically based on structured input. It serves as an intermediary layer that extracts references, queries the GetBible API, and returns scriptures in a standardized JSON format.

  This workflow is fully prepared for integration—simply call it from another workflow with the required JSON input, and it will return the requested scripture data.

  Who Is This For?
  This workflow is ideal for developers, Bible study apps, research tools, and dynamic scripture-based projects that need seamless access to scriptural content without direct API interaction.

  ✅ Use Cases:
  Bible Study Apps** → Embed scripture retrieval functionality.
  Research & Theology Tools** → Fetch structured verse data.
  Dynamic Content Generation** → Integrate real-time scripture references.
  Sermon Preparation** → Automate scripture lookups.

  How It Works
  Trigger Workflow → This workflow is designed to be called from another workflow with a structured JSON input.
  Receive Input → Accepts a JSON object containing references, translation, and API version.
  Extract References → Parses single verses, comma-separated lists, and ranged passages.
  Query API → Sends structured requests to the GetBible API.
  Format Response → Returns structured JSON output, maintaining API response consistency.

  JSON Input Structure
  References** → Should include the book name, chapter, and verse(s).
  Multiple Verses** → Separated by commas (e.g., John 3:16,18).
  Verse Ranges** → Defined with a dash (e.g., John 3:16-18).
  Translation** → Choose from the supported translations.
  API Version** → Currently supports v2.

  Example JSON Input
  {
    "references": [
      "1 John 3:16",
      "Jn 3:16",
      "James 3:16",
      "Rom 3:16"
    ],
    "translation": "kjv",
    "version": "v2"
  }

  Example API Response
  {
    "result": {
      "kjv_62_3": {
        "translation": "King James Version",
        "abbreviation": "kjv",
        "book_name": "1 John",
        "chapter": 3,
        "ref": ["1 John 3:16"],
        "verses": [
          {
            "chapter": 3,
            "verse": 16,
            "name": "1 John 3:16",
            "text": "Hereby perceive we the love of God, because he laid down his life for us: and we ought to lay down our lives for the brethren."
          }
        ]
      }
    }
  }

  💡 Fully structured and formatted response – ready for seamless integration.

  Integration and Usage
  The GetBible Query Workflow is designed for immediate use. Simply call it from another workflow and pass the appropriate JSON object as input, and it will return the requested scripture passages.

  ✔️ No additional configuration is required.
  ✔️ Designed for fast, reliable, and structured scripture retrieval.
  ✔️ Fully compatible with GetBible API responses.

  Why Use This Workflow?
  ✔️ Fast & Reliable → Direct API integration for efficient queries.
  ✔️ Flexible Queries → Supports single, multi-verse, and ranged requests.
  ✔️ Agent-Compatible → Easily integrates into automated workflows.
  ✔️ No Code Needed → Just configure the JSON input and run the workflow.

  Next Steps
  🔗 API Support
  📖 API Documentation
  💬 Need help? Join the community for support! 🚀

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name: Simple Expense Tracker with n8n Chat, AI Agent and Google Sheets
author_name: Anthony
author_username: scrapeninja
categories: []
created_at: 2025-01-29T13:43:53.212Z
updated_at: 2025-01-29T13:43:53.212Z
url: https://n8n.io/workflows/2819
scraped_at: 2025-12-24 13:02:44
description:
  Use Case
  It is very convenient to add expenses via simple chat message. This workflow attempts to do exactly this using AI-powered n8n magic!


  Send message to a chat, something like "car wash; 59.3 usd; 25 jan 2024"

  And get a response:
  Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"}


  LLM will smartly parse your message to structured JSON and save the expense as a new row into Google Sheet!

  Installation
  1. Set up Google Sheets:
  Clone this Sheet:
  https://docs.google.com/spreadsheets/d/1D0r3tun7LF7Ypb21CmbTKEtn76WE-kaHvBCM5NdgiPU/edit?gid=0#gid=0

  (File -&gt; Make a copy)

  Choose this sheet into "Save expense into Google Sheets" node.

  2. Fix sub-workflow dropdown: 
  open "Parse msg and save to Sheets" node (which is an n8n sub-workflow executor tool) and make sure the SAME workflow is chosen in the dropdown. it will allow n8n to locate and call "Workflow Input Trigger" properly when needed.

  3. Activate the workflow to make chat work properly.
  Sent message to chat, something like "car wash; 59.3 usd; 25 jan 2024"

  you should get a response:
  Your expense saved, here is the output of save sub-workflow:{"cost":59.3,"descr":"car wash","date":"2024-01-25","msg":"car wash; 59.3 usd; 25 jan 2024"}

  and new row in Google sheets should be inserted!

nodes_used:
  ["@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.toolWorkflow"]

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name: Backup Workflows to Git Repository on Gitea
author_name: Octoleo
author_username: octoleo
categories: []
created_at: 2025-01-29T14:22:41.191Z
updated_at: 2025-01-29T14:22:41.191Z
url: https://n8n.io/workflows/2820
scraped_at: 2025-12-24 13:02:47
description:
  Overview
  This workflow automates the backup of all workflows from your system to a Git repository hosted on Gitea. It runs on a scheduled trigger, fetching, encoding, and committing workflow data, ensuring seamless version control and disaster recovery.

  📌 Quick Setup: Just update three global variables and configure authentication—no manual exports needed!

  How It Works (Quick Glance)
  1️⃣ Scheduled Execution → Runs automatically at defined intervals.
  2️⃣ Fetch Workflows → Uses the API to retrieve all workflows.
  3️⃣ Process Workflows → Converts workflow data into a Git-friendly format.
  4️⃣ Commit & Push to Git → Saves workflows in a Gitea repository.

  Setup Steps (⚡ Takes ~5 min)

  1️⃣ Set Global Variables
  Go to the Globals section in the workflow and update:
  repo.url* → https://your-gitea-instance.com *(Replace with your actual Gitea URL)
  repo.name* → workflows *(Repository name where backups will be stored)
  repo.owner* → octoleo *(Gitea account that owns the repository)

  📌 These three variables define where the workflows are stored.

  2️⃣ Configure Gitea Authentication
  Go to your Gitea account* → Generate a *Personal Access Token**
  In the credential manager, create a new Gitea Token with:
  Name:** Authorization
  Value:** Bearer YOUR_PERSONAL_ACCESS_TOKEN

  📌 Ensure there is a space after Bearer before the token!

  3️⃣ Link Credentials to Git Nodes
  Attach the Gitea credentials to these three Git nodes:
  GetGitea** → Retrieves existing repository data
  PutGitea** → Updates workflows
  PostGitea** → Adds new workflows

  4️⃣ Link Credentials for API Requests
  Add API authentication** in the node that fetches all workflows.

  5️⃣ Test & Activate
  Run the workflow manually** to confirm backups work.
  Enable the schedule trigger for automation.

  📌 The workflow automatically checks for changes before committing updates.

  Why Use This Workflow?
  ✅ Automated Backups → No manual exports needed.
  ✅ Version Control → Easily track workflow changes.
  ✅ Simple Setup → Just configure globals & credentials.
  ✅ Secure → Uses token-based authentication.

  Next Steps
  💬 Have questions? Reach out on the forum! 🚀

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.n8n","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote"]

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name: Domain Outbound : Automate Lead Extraction, and Targeted Outreach
author_name: Badr
author_username: b4dr
categories: []
created_at: 2025-01-30T13:57:48.318Z
updated_at: 2025-01-30T13:57:48.318Z
url: https://n8n.io/workflows/2821
scraped_at: 2025-12-24 13:02:46
description:
  Domain Outbound Machine: Automate Lead Generation, Email Extraction, and Targeted Outreach with n8n

  Description  
  Domain Outbound Machine is an n8n workflow designed to fully automate the domain sales process: lead generation, email extraction, personalized outreach, and automated email sending. It also stores extracted email addresses and sent emails in an Excel file for tracking and follow-up.

  How It Works?

  1. Domain Input  
  You provide the domain name you want to sell. The workflow takes this domain as the starting point.

  2. Google Maps Query Generation  
  The workflow automatically generates 100 Google Maps queries based on the domain's niche or industry. This ensures you target businesses or individuals who might be interested in acquiring the domain.

  3. Web Scraping for Emails  
  Using the generated queries, the workflow scrapes relevant websites to extract email addresses. This step ensures you have a targeted list of potential buyers.

  4. Email Enrichment  
  For each extracted email, the workflow gathers publicly available information (e.g., LinkedIn, company websites) to enhance personalization.

  5. Personalized Email Creation  
  Based on the enriched data, the workflow crafts a custom email for each lead, emphasizing the domain’s value, its relevance to their business, and a compelling call-to-action.

  6. Email Sending via Gmail  
  The workflow sends the personalized emails directly through your Gmail account, ensuring timely and professional outreach.

  7. Email & Lead Tracking in Excel  
  All extracted email addresses and sent messages are automatically stored in an Excel file, allowing you to track responses and manage follow-ups efficiently.

  Key Features
  ✅ Automated Lead Generation – Saves time by automatically finding and extracting potential leads.  
  ✅ Personalized Outreach – Boosts engagement by tailoring each email to the recipient.  
  ✅ Seamless Integration – Uses n8n’s automation to connect Google Maps, web scraping tools, and Gmail in one workflow.  
  ✅ Scalable & Trackable – Adjust the number of queries or emails as needed, while keeping track of everything in Excel.  

  Domain Outbound Machine is perfect for domain investors, digital marketers, and anyone looking to sell domains efficiently. By automating the tedious tasks, you can focus on closing deals and growing your business.

nodes_used:
  ["n8n-nodes-base.googleSheets","n8n-nodes-base.removeDuplicates","n8n-nodes-base.removeDuplicates","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.wait","n8n-nodes-base.manualTrigger","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.wait","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.filter","n8n-nodes-base.if","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

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name: AI-Generated Summary Block for WordPress Posts
author_name: Dataki
author_username: dataki
categories: []
created_at: 2025-01-30T15:04:16.233Z
updated_at: 2025-01-30T15:04:16.233Z
url: https://n8n.io/workflows/2822
scraped_at: 2025-12-24 13:02:46
description:
  What is this workflow?  
  This n8n template automates the process of adding an AI-generated summary at the top of your WordPress posts.  
  It retrieves, processes, and updates your posts dynamically, ensuring efficiency and flexibility without relying on a heavy WordPress plugin.  

  Example of AI Summary Section

  How It Works  
  Triggers → Runs on a scheduled interval or via a webhook when a new post is published.  
  Retrieves posts → Fetches content from WordPress and converts HTML to Markdown for AI processing.  
  AI Summary Generation → Uses OpenAI to create a concise summary.  
  Post Update → Inserts the summary at the top of the post while keeping the original excerpt intact.  
  Data Logging & Notifications → Saves processed posts to Google Sheets and notifies a Slack channel.  

  Why use this workflow?  
  ✅ No need for a WordPress plugin → Keeps your site lightweight.  
  ✅ Highly flexible → Easily connect with Google Sheets, Slack, or other services.  
  ✅ Customizable → Adapt AI prompts, formatting, and integrations to your needs.  
  ✅ Smart filtering → Ensures posts are not reprocessed unnecessarily.  

  💡 Check the detailed sticky notes for setup instructions and customization options!  

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.webhook","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.markdown","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.googleSheets","n8n-nodes-base.slack","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.dateTime","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.wordpress","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.wordpress","n8n-nodes-base.wordpress","n8n-nodes-base.wordpress"]

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name: Social Media Analysis and Automated Email Generation
author_name: PollupAI
author_username: Pollup
categories: []
created_at: 2025-01-30T16:19:16.536Z
updated_at: 2025-01-30T16:19:16.536Z
url: https://n8n.io/workflows/2823
scraped_at: 2025-12-24 13:02:44
description:
  Social Media Analysis and Automated Email Generation

  &gt; by Thomas Vie Thomas@pollup.net

  Who is this for?
  This template is ideal for marketers, lead generation specialists, and business professionals seeking to analyze social media profiles of potential leads and automate personalized email outreach efficiently.

  What problem is this workflow solving?
  Manually analyzing social media profiles and crafting personalized emails can be time-consuming and prone to errors. This workflow streamlines the process by integrating social media APIs with AI to generate tailored communication, saving time and increasing outreach effectiveness.

  What this workflow does:
  Google Sheets Integration: Start with a Google Sheet containing lead information such as LinkedIn URL, Twitter handle, name, and email.
  Social Media Data Extraction: Automatically fetch profile and activity data from Twitter and LinkedIn using RapidAPI integrations.
  AI-Powered Content Generation: Use OpenAI's Chat Model to analyze the extracted data and generate personalized email subject lines and cover letters.
  Automated Email Dispatch: Send the generated email directly to the lead, with a copy sent to yourself for tracking purposes.
  Progress Tracking: Update the Google Sheet to indicate completed actions.

  Setup:
  Google Sheets:
     Create a sheet with the columns: LinkedIn URL, name, Twitter handle, email, and a "done" column for tracking.
     Populate the sheet with your leads.

  RapidAPI Accounts:
     Sign up for RapidAPI and subscribe to the Twitter and LinkedIn API plans.
     Configure API authentication keys in the workflow.

  AI Configuration:
     Connect OpenAI Chat Model with your API key for text generation.

  Email Integration:
     Add your email credentials or service (SMTP or third-party service like Gmail) for sending automated emails.

  How to customize this workflow to your needs:
  Modify the AI Prompt:** Adapt the prompt in the AI node to better align with your tone, style, or specific messaging framework.
  Expand Data Fields:** Add additional data fields in Google Sheets if you require further personalization.
  API Limits:** Adjust API configurations to fit your usage limits or upgrade to higher tiers for increased data scraping capabilities.
  Personalize Email Templates:** Tweak email formats to suit different audiences or use cases.
  Extend Functionality:** Integrate additional social media platforms or CRM tools as needed.

  By implementing this workflow, you’ll save time on repetitive tasks and create more effective lead generation strategies.

nodes_used:
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name: Query Perplexity AI from your n8n workflows
author_name: Emmanuel Bernard
author_username: n8ninja
categories: []
created_at: 2025-01-30T18:22:09.713Z
updated_at: 2025-01-30T18:22:09.713Z
url: https://n8n.io/workflows/2824
scraped_at: 2025-12-24 13:02:42
description:
  This workflow illustrates how to use Perplexity AI in your n8n workflow. 

  Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.

  Credentials Setup 

  1/ Go to the perplexity dashboard, purchase some credits and create an API Key

  https://www.perplexity.ai/settings/api

  2/ In the perplexity Request node, use Generic Credentials, Header Auth. 

  For the name, use the value "Authorization"
  And for the value "Bearer pplx-e4...59ea" (Your Perplexity Api Key)

  AI Model 
  Sonar Pro is the current top model used by perplexity. 
  If you want to use a different one, check this page: 

  https://docs.perplexity.ai/guides/model-cards

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Spot Workplace Discrimination Patterns with AI
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-01-30T19:31:39.617Z
updated_at: 2025-01-30T19:31:39.617Z
url: https://n8n.io/workflows/2825
scraped_at: 2025-12-24 13:02:45
description:
  How It Works:
  • Scrapes company review data from Glassdoor using ScrapingBee.
  • Extracts demographic-based ratings using AI-powered text analysis.
  • Calculates workplace disparities with statistical measures like z-scores, effect sizes, and p-values.
  • Generates visualizations (scatter plots, bar charts) to highlight patterns of discrimination or bias.

  Example Visualizations:

  Set Up Steps:
  Estimated time: ~20 minutes.
  • Replace ScrapingBee and OpenAI credentials with your own.
  • Input the company name you want to analyze (best results with large U.S.-based organizations).
  • Run the workflow and review the AI-generated insights and visual reports.

  This workflow empowers users to identify potential workplace discrimination trends, helping advocate for greater equity and accountability.

  Additional Credit: Wes Medford
  For algorithms and inspiration

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name: Automatically Send a Direct Message (DM) to New Followers on Bluesky using Baserow
author_name: Gareth B. Davies
author_username: garethbdavies
categories: []
created_at: 2025-01-30T21:22:55.627Z
updated_at: 2025-01-30T21:22:55.627Z
url: https://n8n.io/workflows/2827
scraped_at: 2025-12-24 13:02:47
description:
  Send personalized welcome messages to your new Bluesky followers automatically, helping you maintain engagement while saving time.

  This workflow monitors your follower list and sends customized direct messages, creating a warm welcome for new connections without manual intervention.

  How it works
  Checks your Bluesky followers list daily at 9 AM
  Identifies new followers by comparing against a database
  Extracts the follower's first name** when available
  Sends a personalized welcome message with optional link
  Prevents duplicate messages** through double-verification
  Maintains a record of sent messages to avoid repetition

  Set up steps (10-15 minutes)
  Create a Bluesky account if you haven't already
  Generate an app password in your Bluesky settings
  Enter your Bluesky handle and app password in the "Set Bluesky Credentials" node
  Set up your database (Baserow, or adapt for Airtable/Google Sheets)
  Customize your welcome message in the "Create Welcome Message" node
  Optional: Adjust the regular check time (default: 9 AM)

  Features
  Personalized messaging** using follower's first name when available
  Database tracking** to prevent duplicate messages
  Basic rate limiting protection** to stay within API limits
  Customizable welcome message with clickable links
  Ability to handle up to 100 new followers per check

  Perfect for creators who want to
  Welcome new followers consistently
  Save time** on manual messaging
  Build early engagement with followers
  Share important links or resources
  Maintain a professional presence
  Scale** their community management

  Suggested enhancements
  Add message templates for different follower types
  Include email/Slack notifications for errors
  Add analytics tracking for message success rates
  Implement dynamic timing based on follower activity
  Create A/B testing for different welcome messages
  Add follower segmentation based on profile data

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name: Batch Airtable requests to send data 9x faster
author_name: Brahim HAMICHAN
author_username: brahimh
categories: []
created_at: 2025-02-01T06:21:25.311Z
updated_at: 2025-02-01T06:21:25.311Z
url: https://n8n.io/workflows/2831
scraped_at: 2025-12-24 13:02:47
description:
  Watch Demo YouTube Video

  Optimized Airtable Bulk Data Workflow

  This workflow is specifically designed to address the challenges of upserting or inserting large volumes of data into Airtable. By leveraging the Airtable Batch API, it delivers up to 9X faster performance compared to standard data insertion methods, making it an indispensable tool for high-demand data operations.

  Key Features
  •	Accelerated Data Processing:
  Utilize the Airtable Batch API to perform bulk operations swiftly and efficiently.
  •	Seamless Workflow Integration:
  Easily integrate this sub-processor into any n8n workflow that requires Airtable updates, ensuring smooth data synchronization across multiple processes.
  •	Enhanced Reliability and Scalability:
  Designed to handle extensive datasets, this solution is perfect for real-time updates, database migrations, and continuous data syncing without performance degradation.

  Setup Instructions
  Add the Sub-Workflow:
  Import this workflow to your n8n workflows, then add it as a sub-workflow call in other workflows requiring a lot of Airtable updates.
  **Configure Sub-Worflow variables:
  "set_Batching_vars" SET Node**
  •	Obtain the correct Base ID and Table ID, and insert in the "set_Batching_vars" SET Node.

  •	Add or select the correct Airtable credentials in both Airtable Upsert & Insert HTTP nodes in the sub-workflow.

  •	Ensure the API permissions are set correctly to allow data insertion/upsertion.
  **Adjust Batch Settings:
  "set_Batching_vars" SET Node**
  •	In the same "set_Batching_vars" SET Node, put the field name in the "merge_on" field if you wish to upsert record, otherwise, keep it empty for insertion.

  •	Correctly setup the fields you want to insert/upsert in the 'record' field.

  Test the Integration:
  Run a small-scale test to ensure that data is correctly processed and inserted/upserted into Airtable.

  Use Case Scenarios

  •	Bulk Data Insertion:
  Efficiently insert large datasets into Airtable, perfect for initial data migrations or periodic data updates.
  •	Real-Time Data Upsertion:
  Keep your Airtable records current by integrating this workflow with your live data pipelines.
  •	Database Migrations & Synchronization:
  Seamlessly transfer data between databases and Airtable, ensuring minimal downtime and data integrity.

  Specific Requirements for Airtable Integration
  •	Airtable Account:
  You must have an active Airtable account with appropriate permissions to modify the target base.
  •	API Credentials:
  Secure a valid Airtable API connection and ensure you have the correct Base ID and Table ID for the target data store.

  By integrating this workflow into your system, you can significantly improve the efficiency of your Airtable operations, reducing processing time and enabling smoother data management at scale.

nodes_used:
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name: Scrape Latest 20 TechCrunch Articles
author_name: Teddy
author_username: teddysbeach
categories: []
created_at: 2025-02-01T07:06:30.056Z
updated_at: 2025-02-01T07:06:30.056Z
url: https://n8n.io/workflows/2832
scraped_at: 2025-12-24 13:02:45
description:
  Retrieve 20 Latest TechCrunch Articles

  Who is this for?
  This workflow is designed for developers, content creators, and data analysts who need to scrape recent articles from TechCrunch. It’s perfect for anyone looking to aggregate news articles or create custom feeds for analysis, reporting, or integration into other systems.

  What problem is this workflow solving?
  This workflow automates the process of scraping recent articles from TechCrunch. Manually collecting article data can be time-consuming and inefficient, but with this workflow, you can quickly gather up-to-date news articles with relevant metadata, saving time and effort.

  What this workflow does
  This workflow retrieves the latest 20 news articles from TechCrunch’s “Recent” page. It extracts the article URLs, metadata (such as titles and publication dates), and main content for each article, allowing you to access the information you need without any manual effort.

  Setup
  Clone or download the workflow template.
  Ensure you have a working n8n environment.
  Configure the HTTP Request nodes with your desired parameters to connect to the TechCrunch API.
  (Optional) Customize the workflow to target specific sections or topics of interest.
  Run the workflow to retrieve the latest 20 articles.

  How to customize this workflow to your needs
  Modify the HTTP request to pull articles from different pages or sections of TechCrunch.
  Adjust the number of articles to retrieve by changing the selection criteria.
  Add additional processing steps to further filter or analyze the article data.

  Workflow Steps
  Send an HTTP request to the TechCrunch "Recent" page.
  Parse a posts box that holds the list of articles.
  Parse all posts to extract all articles.
  spilt out posts for each article.
  Extract the URL and metadata from each article.
  Send an HTTP request for each article using its URL.
  Locate and parse the main content of each article.

  Note: Be sure to update the HTTP Request nodes with any necessary headers or authentication to work with TechCrunch’s website.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.html","n8n-nodes-base.splitOut","n8n-nodes-base.html","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.set"]

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name: Smart Email Outreach Sequence – AI-Powered & Customizable
author_name: Badr
author_username: b4dr
categories: []
created_at: 2025-02-01T10:42:30.224Z
updated_at: 2025-02-01T10:42:30.224Z
url: https://n8n.io/workflows/2833
scraped_at: 2025-12-24 13:02:46
description:
  Automated Email Outreach Workflow

  This intelligent email automation workflow helps you maximize engagement through domain-based outreach. It utilizes AI-powered personalization and strategic follow-ups to increase response rates. The workflow includes a customizable three-step email sequence (initial email + two follow-ups) sent at optimized 3-day intervals.

  🔥 Key Features
  ✅ Smart Domain Extraction – Automatically extracts domains from email addresses for precise targeting.  
  ✅ Website Intelligence – Uses Jina.ai to analyze recipient websites and craft ultra-relevant messaging.  
  ✅ AI-Powered Personalization – Leverages GPT-3.5 to generate compelling and highly customized email content.  
  ✅ Adjustable AI Prompts – Easily customize AI-generated messages to align with your product, service, or campaign.  
  ✅ Automated Follow-Up Sequence – Sends a structured series of three emails over time to increase engagement.  
  ✅ Human-Like Sending Patterns – Randomized delays help bypass spam filters and improve deliverability.  
  ✅ Error-Resilient Execution – Built-in error handling ensures a smooth, uninterrupted workflow.  

  🎯 Ideal Use Cases
  ✔️ Domain Sales & Lead Generation – Engage potential buyers with hyper-personalized emails.  
  ✔️ B2B Partnerships & Outreach – Automate connections with potential business partners.  
  ✔️ Cold Emailing at Scale – Improve engagement with structured, AI-optimized emails.  

  ⚙️ How It Works

  1️⃣ Setup & Configuration
  Integrate Gmail via OAuth for secure email sending.  
  Connect your Google Sheets with leads for automated processing.  
  Customize AI-generated messages by adjusting prompt inputs to fit your strategy.  

  2️⃣ Workflow Execution
  Domain Extraction – Automatically extracts recipient domains from email addresses.  
  Website Analysis – Uses Jina.ai to gather insights for ultra-personalized messaging.  
  AI-Powered Email Generation – GPT-3.5 crafts highly engaging outreach emails.  
  Automated Email Sending – Initial email is sent, followed by two optimized follow-ups if no response is received.  
  Engagement Tracking & Adaptation – Ensures delivery success and prevents emails from being flagged as spam.  

  ⚡ Setup Instructions

  ✅ Preconditions
  You need a Gmail account with OAuth authentication enabled.  
  Your Google Sheets must contain lead emails formatted correctly.  
  Ensure your n8n instance is running and connected to your workflow.  

  🛠 Implementation Steps
  Install and open n8n.  
  Import the workflow JSON file into your n8n instance.  
  Configure your Gmail credentials in the workflow settings.  
  Link your Google Sheets containing the leads' email addresses.  
  Customize AI prompts in the GPT-3.5 node to match your campaign strategy.  
  Test the workflow manually to ensure proper execution.  
  Activate the workflow and let automation take over!  

  📈 Expected Results
  🚀 Higher Response Rates – Personalization improves recipient engagement.  
  📊 Increased Conversions – Optimized email sequences boost lead-to-client conversions.  
  💡 Time-Saving Automation – Hands-free execution streamlines your outreach process.  

  🔧 Ready to supercharge your outreach? Set up the workflow and start automating today! 🚀🔥  

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name: Automated Upwork Job Alerts with MongoDB & Slack
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-02-01T15:08:11.227Z
updated_at: 2025-02-01T15:08:11.227Z
url: https://n8n.io/workflows/2834
scraped_at: 2025-12-24 13:02:45
description:
  Overview  
  This automated workflow fetches Upwork job postings using Apify, removes duplicate job listings via MongoDB, and sends new job opportunities to Slack.  

  Key Features:  
  Automated job retrieval** from Upwork via Apify API  
  Duplicate filtering** using MongoDB to store only unique jobs  
  Slack notifications** for new job postings  
  Runs every 20 minutes** during working hours (9 AM - 5 PM)  

  This workflow requires an active Apify subscription to function, as it uses the Apify Upwork API to fetch job listings. 

  Who is This For?  
  This workflow is ideal for:  
  Freelancers looking to track Upwork jobs in real time  
  Recruiters automating job collection for analytics  
  Developers who want to integrate Upwork job data into their applications  

  What Problem Does This Solve?  
  Manually checking Upwork for jobs is time-consuming and inefficient. This workflow:  
  Automates job discovery based on your keywords  
  Filters out duplicate listings, ensuring only new jobs are stored  
  Notifies you on Slack when new jobs appear  

  How the Workflow Works  

  1. Schedule Trigger (Every 20 Minutes)  
  Triggers the workflow at 20-minute intervals  
  Ensures job searches are only executed during working hours (9 AM - 5 PM)  

  2. Query Upwork for Jobs  
  Uses Apify API to scrape Upwork job posts for specific keywords (e.g., "n8n", "Python")  

  3. Find Existing Jobs in MongoDB  
  Searches MongoDB to check if a job (based on title and budget) already exists  

  4. Filter Out Duplicate Jobs  
  The Merge Node compares Upwork jobs with MongoDB data  
  The IF Node filters out jobs that are already stored in the database  

  5. Save Only New Jobs in MongoDB  
  The Insert Node adds only new job listings to the MongoDB collection  

  6. Send a Slack Notification  
  If a new job is found, a Slack message is sent with job details  

  Setup Guide  

  Required API Keys  
  Upwork Scraper (Apify Token) – Get your token from Apify
  MongoDB Credentials – Set up MongoDB in n8n using your connection string  
  Slack API Token – Connect Slack to n8n and set the channel ID (default: #general)  

  Configuration Steps  
  Modify search keywords in the 'Assign Parameters' node (startUrls)  
  Adjust the Working Hours in the 'If Working Hours' node  
  Set your Slack channel in the Slack node  
  Ensure MongoDB is connected properly  
  Adjust the 'If Working Hours' node to match your timezone and hours, or remove it altogether to receive notifications and updates constantly.

  How to Customize the Workflow  
  Change keywords: update the startUrls in the 'Assign Parameters' node to track different job categories  
  Change 'If Working Hours': Modify conditions in the IF Node to filter times based on your needs  
  Modify Slack Notifications: Adjust the Slack message format to include additional job details  

  Why Use This Workflow?  
  Automated job tracking without manual searches  
  Prevents duplicate entries in MongoDB  
  Instant Slack notifications for new job opportunities  
  Customizable – adapt the workflow to different job categories  

  Next Steps  
  Run the workflow and test with a small set of keywords  
  Expand job categories for better coverage  
  Enhance notifications by integrating Telegram, Email, or a dashboard  

  This workflow ensures real-time job tracking, prevents duplicates, and keeps you updated effortlessly.

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.mongoDb","n8n-nodes-base.merge","n8n-nodes-base.mongoDb","n8n-nodes-base.stickyNote","n8n-nodes-base.slack"]

workflow_json_compressed:
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name: Docker Registry Cleanup Workflow
author_name: Muzaffer AKYIL
author_username: victorious
categories: []
created_at: 2025-02-01T23:26:03.445Z
updated_at: 2025-02-01T23:26:03.445Z
url: https://n8n.io/workflows/2835
scraped_at: 2025-12-24 13:02:46
description:
  Docker Registry Cleanup Template

  This template is designed to automatically clean up old image tags in the Docker registry and perform garbage collection.

  Features
  List all images in the registry
  Preserve the last 10 tags for each image (latest tag is always preserved)
  Delete old tags
  Email notification for Successful/Excused cancellation
  Registry garbage collection automation
  Failure notification in error conditions

  Prerequisites
  Docker Registry v2 API access
  Basic Authentication credentials
  SMTP email settings (for notifications)
  SSH node installed on n8n (for garbage collection)

  Installation

  1. Identity Information
  Add the following credentials in n8n:
  HTTP Basic Auth**: For Registry access
  SSH Private Key**: For Garbage collection command
  Email SMTP**: For notifications

  2. Set Variables
  Replace your-registry-url with your actual registry URL on all nodes:
  ‘url": ‘https://your.registry.com/v2/_catalog’.

  Customisation
  Retention Policy: 
  Set the number of tags to be retained by changing the slice(0, 10) value in the  Identify Tags to Remove node
  Schedule:
  Change the frequency of operation at the Trigger node
  Notification Content: 
  Customise email templates according to your needs

  Notes
  Check DELETE operations before running in a test environment

  Make sure that the registry is not in read-only mode

  The registry may need to be put into maintenance mode for garbage collection

  Step Details:
  Retrieving image information:** The workflow starts by fetching a list of images and their associated tags from the Docker registry.
  Filtering and sorting:** The retrieved tags are then filtered and sorted based on specific criteria, such as creation date and tag name.
  Deleting old tags:** The workflow identifies old or unused tags and attempts to delete them from the registry.
  Sending notifications:** The workflow sends email notifications to inform the user about the status of the cleanup process, including any errors or successes.
  Executing additional cleanup tasks:** Finally, the workflow executes an SSH command on the Docker registry server to perform additional cleanup tasks, such as garbage collection.

  TL;DR
  In summary, this n8n template provides a robust and automated solution for managing and cleaning up Docker registries. By regularly running this workflow, users can ensure that their registry remains organized and efficient, and avoid running out of storage space.- 

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.emailSend","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.sort","n8n-nodes-base.emailSend","n8n-nodes-base.ssh"]

workflow_json_compressed:
  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


name: Automate Rank Math SEO Field Updates for Wordpress or Woocommerce
author_name: phil
author_username: phil
nodes_used: ["n8n-nodes-base.set","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest"]
categories: []
created_at: 2025-02-02T10:00:10.263Z
updated_at: 2025-02-02T10:00:10.263Z
url: https://n8n.io/workflows/2836
scraped_at: 2025-12-24 13:02:46
description:
  This workflow automates the process of updating important Rank Math SEO fields (SEO Title, Description, and Canonical URL) directly via n8n. 

  By leveraging a custom WordPress plugin that extends the WordPress REST API, this workflow ensures that you can programmatically manage SEO metadata for your posts and WooCommerce products efficiently.

  Bulk version available here.

  How it works:
  Sends a POST request to a custom API endpoint exposed by the Rank Math plugin.
  Updates SEO Title, Description, and Canonical URL fields for a specified post or product.

  Setup steps:
  Install and activate the Rank Math API Manager Extended plugin on WordPress.
  Provide the post or product ID you want to update in the workflow.
  Run the workflow to update the metadata automatically.

  Benefits:
  Full automation of SEO optimizations.
  Works for both standard posts and WooCommerce products.
  Simplifies large-scale SEO management tasks.

  To understand exactly how to use it in detail, check out my comprehensive documentation here.

  Rank Math API Manager Extended plugin on WordPress

  // ATTENTION: Replace the line below with &lt;?php - This is necessary due to display constraints in web interfaces.

  &lt;?php
  /**
   Plugin Name: Rank Math API Manager Extended v1.4
   Description: Manages the update of Rank Math metadata (SEO Title, SEO Description, Canonical URL) via a dedicated REST API endpoint for WordPress posts and WooCommerce products.
   Version: 1.4
   Author: Phil - https://inforeole.fr
   */

  if ( ! defined( 'ABSPATH' ) ) {
      exit; // Exit if accessed directly.
  }

  class Rank_Math_API_Manager_Extended {

      public function __construct() {
          add_action('rest_api_init', [$this, 'register_api_routes']);
      }

      /**
       Registers the REST API route to update Rank Math meta fields.
       */
      public function register_api_routes() {
          register_rest_route( 'rank-math-api/v1', '/update-meta', [
              'methods'             =&gt; 'POST',
              'callback'            =&gt; [$this, 'update_rank_math_meta'],
              'permission_callback' =&gt; [$this, 'check_route_permission'],
              'args'                =&gt; [
                  'post_id' =&gt; [
                      'required'          =&gt; true,
                      'validate_callback' =&gt; function( $param ) {
                          $post = get_post( (int) $param );
                          if ( ! $post ) {
                              return false;
                          }
                          $allowed_post_types = class_exists('WooCommerce') ? ['post', 'product'] : ['post'];
                          return in_array($post-&gt;post_type, $allowed_post_types, true);
                      },
                      'sanitize_callback' =&gt; 'absint',
                  ],
                  'rank_math_title' =&gt; [
                      'type'              =&gt; 'string',
                      'sanitize_callback' =&gt; 'sanitize_text_field',
                  ],
                  'rank_math_description' =&gt; [
                      'type'              =&gt; 'string',
                      'sanitize_callback' =&gt; 'sanitize_text_field',
                  ],
                  'rank_math_canonical_url' =&gt; [
                      'type'              =&gt; 'string',
                      'sanitize_callback' =&gt; 'esc_url_raw',
                  ],
              ],
          ] );
      }

      /**
       Updates the Rank Math meta fields for a specific post.
       * @param WP_REST_Request $request The REST API request instance.
       @return WP_REST_Response|WP_Error Response object on success, or WP_Error on failure.
       */
      public function update_rank_math_meta( WP_REST_Request $request ) {
          $post_id = $request-&gt;get_param('post_id');
          
          // Secondary, more specific permission check.
          if ( ! current_user_can('edit_post', $post_id) ) {
              return new WP_Error(
                  'rest_forbidden',
                  'You do not have permission to edit this post.',
                  ['status' =&gt; 403]
              );
          }

          $fields  = ['rank_math_title', 'rank_math_description', 'rank_math_canonical_url'];
          $results = [];
          $updated = false;

          foreach ( $fields as $field ) {
              if ( $request-&gt;has_param( $field ) ) {
                  $value = $request-&gt;get_param( $field );
                  $current_value = get_post_meta($post_id, $field, true);

                  if ($current_value === $value) {
                       $results[$field] = 'unchanged';
                  } else {
                      $update_status = update_post_meta( $post_id, $field, $value );
                      if ($update_status) {
                          $results[$field] = 'updated';
                          $updated = true;
                      } else {
                          // This case is rare but could indicate a DB error or other failure.
                          $results[$field] = 'failed'; 
                      }
                  }
              }
          }

          if ( ! $updated && empty($results) ) {
              return new WP_Error(
                  'no_fields_provided',
                  'No Rank Math fields were provided for update.',
                  ['status' =&gt; 400]
              );
          }

          return new WP_REST_Response( $results, 200 );
      }

      /**
       Checks if the current user has permission to access the REST API route.
       * @return bool
       */
      public function check_route_permission() {
          return current_user_can( 'edit_posts' );
      }
  }

  new Rank_Math_API_Manager_Extended();


  Bulk version available here. : this bulk version, provided with a dedicated WordPress plugin, allows you to generate and bulk-update meta titles and descriptions for multiple articles simultaneously using artificial intelligence. It automates the entire process, from article selection to the final update in Rank Math, offering considerable time savings.
  .

  Phil | Inforeole | Linkedin

  🇫🇷 Contactez nous pour automatiser vos processus

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name: Calculate the Centroid of a Set of Vectors
author_name: Mauricio Perera
author_username: rckflr
categories: []
created_at: 2025-02-03T02:28:31.005Z
updated_at: 2025-02-03T02:28:31.005Z
url: https://n8n.io/workflows/2839
scraped_at: 2025-12-24 13:02:47
description:
  n8n Workflow: Calculate the Centroid of a Set of Vectors

  Overview
  This workflow receives an array of vectors in JSON format, validates that all vectors have the same dimensions, and computes the centroid. It is designed to be reusable across different projects.

  Workflow Structure
  Nodes and Their Functions:
  Receive Vectors (Webhook): Accepts a GET request containing an array of vectors in the vectors parameter.
     Expected Input: vectors parameter in JSON format.
     Example Request: /webhook/centroid?vectors=[[2,3,4],[4,5,6],[6,7,8]]
     Output: Passes the received data to the next node.

  Extract & Parse Vectors (Set Node): Converts the input string into a proper JSON array for processing.
     Ensures vectors is a valid array.
     If the parameter is missing, it may generate an error.
     Expected Output Example:
        {
       "vectors": [[2,3,4],[4,5,6],[6,7,8]]
     }
     
  Validate & Compute Centroid (Code Node): Validates vector dimensions and calculates the centroid.
     Validation: Ensures all vectors have the same number of dimensions.
     Computation: Averages each dimension to determine the centroid.
     If validation fails: Returns an error message indicating inconsistent dimensions.
     Successful Output Example:
        {
       "centroid": [4,5,6]
     }
        Error Output Example:
        {
       "error": "Vectors have inconsistent dimensions."
     }
     
  Return Centroid Response (Respond to Webhook Node): Sends the final response back to the client.
     If the computation is successful, it returns the centroid.
     If an error occurs, it returns a descriptive error message.
     Example Response:
        {
       "centroid": [4, 5, 6]
     }
  Inputs
  JSON array of vectors, where each vector is an array of numerical values.

  Example Input
  {
    "vectors": [
      [1, 2, 3],
      [4, 5, 6],
      [7, 8, 9]
    ]
  }

  Setup Guide
  Create a new workflow in n8n.
  Add a Webhook node (Receive Vectors) to receive JSON input.
  Add a Set node (Extract & Parse Vectors) to extract and convert the data.
  Add a Code node (Validate & Compute Centroid) to:
     Validate dimensions.
     Compute the centroid.
  Add a Respond to Webhook node (Return Centroid Response) to return the result.

  Function Node Script Example
  const input = items[0].json;
  const vectors = input.vectors;

  if (!Array.isArray(vectors) || vectors.length === 0) {
    return [{ json: { error: "Invalid input: Expected an array of vectors." } }];
  }

  const dimension = vectors[0].length;
  if (!vectors.every(v =&gt; v.length === dimension)) {
    return [{ json: { error: "Vectors have inconsistent dimensions." } }];
  }

  const centroid = new Array(dimension).fill(0);
  vectors.forEach(vector =&gt; {
    vector.forEach((val, index) =&gt; {
      centroid[index] += val;
    });
  });

  for (let i = 0; i &lt; dimension; i++) {
    centroid[i] /= vectors.length;
  }

  return [{ json: { centroid } }];

  Testing
  Use a tool like Postman or the n8n UI to send sample inputs and verify the responses.
  Modify the input vectors to test different scenarios.

  This workflow provides a simple yet flexible solution for vector centroid computation, ensuring validation and reliability.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Automate SIEM Alert Enrichment with MITRE ATT&CK, Qdrant & Zendesk in n8n
author_name: Angel Menendez
author_username: djangelic
categories: []
created_at: 2025-02-03T09:56:24.838Z
updated_at: 2025-02-03T09:56:24.838Z
url: https://n8n.io/workflows/2840
scraped_at: 2025-12-24 13:02:43
description:
  n8n Workflow: Automate SIEM Alert Enrichment with MITRE ATT&CK & Qdrant  

  Who is this for?  
  This workflow is ideal for:  
  Cybersecurity teams & SOC analysts* who want to automate *SIEM alert enrichment**.  
  IT security professionals* looking to integrate *MITRE ATT&CK intelligence** into their ticketing system.  
  Organizations using Zendesk for security incidents* who need enhanced *contextual threat data**.  
  Anyone using n8n and Qdrant* to build *AI-powered security workflows**.  

  What problem does this workflow solve?  
  Security teams receive large volumes of raw SIEM alerts that lack actionable context. Investigating every alert manually is time-consuming and can lead to delayed response times. This workflow solves this problem by:  
  ✔ Automatically enriching SIEM alerts with MITRE ATT&CK TTPs.  
  ✔ Tagging & classifying alerts based on known attack techniques.  
  ✔ Providing remediation steps to guide the response team.  
  ✔ Enhancing security tickets in Zendesk with relevant threat intelligence.  

  What this workflow does  

  1️⃣ Ingests SIEM alerts (via chatbot or ticketing system like Zendesk).  
  2️⃣ Queries a Qdrant vector store containing MITRE ATT&CK techniques.  
  3️⃣ Extracts relevant TTPs (Tactics, Techniques, & Procedures) from the alert.  
  4️⃣ Generates remediation steps using AI-powered enrichment.  
  5️⃣ Updates Zendesk tickets with threat intelligence & recommended actions.  
  6️⃣ Provides structured alert data for further automation or reporting.  

  Setup Guide  

  Prerequisites  
  n8n instance** (Cloud or Self-hosted).  
  Qdrant vector store** with MITRE ATT&CK data embedded.  
  OpenAI API key** (for AI-based threat processing).  
  Zendesk account** (for ticket enrichment, if applicable).  
  Clean Mitre Data Python Script
  Cleaned Mitre Data
  Full Mitre Data

  Steps to Set Up  

  1️⃣ Embed MITRE ATT&CK data into Qdrant  
     This workflow pulls MITRE ATT&CK data from Google Drive and loads it into Qdrant.  
     The data is vectorized using OpenAI embeddings for fast retrieval.  

  2️⃣ Deploy the n8n Chatbot  
     The chatbot listens for SIEM alerts and sends them to the AI processing pipeline.  
     Alerts are analyzed using an AI agent trained on MITRE ATT&CK.  

  3️⃣ Enrich Zendesk Tickets  
     The workflow extracts MITRE ATT&CK techniques from alerts.  
     It updates Zendesk tickets with contextual threat intelligence.  
     The remediation steps are included as internal notes for SOC teams.  

  How to Customize This Workflow  

  🔧 Modify the chatbot trigger: Adapt the chatbot node to receive alerts from Slack, Microsoft Teams, or any other tool.  

  🔧 Change the SIEM input source: Connect your workflow to Splunk, Elastic SIEM, or Chronicle Security.  

  🔧 Customize remediation steps: Use a custom AI model to tailor remediation responses based on organization-specific security policies.  

  🔧 Extend ticketing integration: Modify the Zendesk node to also work with Jira, ServiceNow, or another ITSM platform.  

  Why This Workflow is Powerful  
  ✅ Saves time: Automates alert triage & classification.  
  ✅ Improves security posture: Helps SOC teams act faster on threats.  
  ✅ Leverages AI & vector search: Uses LLM-powered enrichment for real-time context.  
  ✅ Works across platforms: Supports n8n Cloud, Self-hosted, and Qdrant.  

  🚀 Get Started Now!  
  📖 Watch the Setup Video  
  💬 Have Questions? Join the Discussion in the YouTube Comments! 

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.extractFromFile","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.zendesk","n8n-nodes-base.zendesk","n8n-nodes-base.noOp"]

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name: Connect AI to any chats in Kommo
author_name: yatolstoy
author_username: yatolstoy
categories: []
created_at: 2025-02-03T10:12:55.563Z
updated_at: 2025-02-03T10:12:55.563Z
url: https://n8n.io/workflows/2841
scraped_at: 2025-12-24 13:02:46
description:
  Entrust customer service to AI using n8n and Kommo!
  Using this workflow, you can make the AI agent answer customer questions for your managers.
  See how it works in the video.

  Advantages of integration
  Works with any message channel that is connected to Kommo (telegram, whatsapp, facebook)
  Understands voice and text messages
  You can stop for a specific transaction or contact if you need a person's help.
  It is possible to supplement the AI agent with additional tools to suit your needs

  Where it can be useful
  In customer support
  In the qualification of clients
  When invoicing

  How it works
  1) Any incoming message to the Kommo chats is sent by the webhook to n8n
  2) n8n processes the webhook according to the specified logic
  3) n8n sends a reply message to the Kommo chat

  Installation Steps
  1) Install workflow
  2) Follow the instructions to connect the kommo to the n8n
  3) Set up Credentials for OpenAI
  4) Fill in the Credentials as shown in the workflow notes
  5) Activate Workflow
  6) Write your first message as client

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Automate Sports Betting Data with the Odds API
author_name: Marketing Canopy
author_username: johnmarketingcanopy
categories: []
created_at: 2025-02-04T01:22:43.903Z
updated_at: 2025-02-04T01:22:43.903Z
url: https://n8n.io/workflows/2843
scraped_at: 2025-12-24 13:02:46
description:
  Automate Sports Betting Data with TheOddsAPI  

  This workflow enables you to create and update a table using TheOddsAPI for sports betting data. It automatically pulls upcoming Ice Hockey games at the start of the day and updates the table with results at the end of the day. You can modify it to retrieve odds and game data for any sport.  

  This setup is particularly useful for sports betting applications, such as tracking the results of a predictive model. It leverages scheduled triggers to activate HTTP requests, which then create or update fields in Airtable by matching on the game ID.  

  Prerequisites  

  Before implementing this workflow, ensure you have the following:  

  TheOddsAPI Account & API Key  
     Sign up at TheOddsAPI and obtain an API key.  
     Ensure you have the correct API permissions to access sports odds and results.  

  Airtable Account & API Key  
     Create an account at Airtable and set up a database.  
     Obtain an API key from the Account Settings page.  

  API Access & Rate Limits  
     Review TheOddsAPI’s rate limits and ensure your account tier allows for scheduled API calls.  
     Confirm that Airtable API limits align with your expected data retrieval frequency.  

  Step-by-Step Guide to Integrating TheOddsAPI  

  1. Schedule API Requests  
  Set up a trigger to automatically pull upcoming Ice Hockey games at the start of each day.  

  2. Fetch Data from TheOddsAPI  
  Retrieve the latest sports betting data, including game details and odds, using TheOddsAPI.  

  3. Store Data in Airtable  
  Insert or update records in Airtable by matching game IDs, ensuring data accuracy.  

  Sample Airtable Template Column Setup for Ice Hockey  
  (Table can adjust depending on sport and data needs. Reference TheOddsAPI for more documentation.)  

  Game ID**  
  Sport**  
  League**  
  Game Date (UTC)**  
  Home Team**  
  Away Team**  
  Completed** (Boolean: TRUE/FALSE for game completion status)  
  Scores** (JSON or String for final scores)  
  Last Update** (Timestamp of the latest update)  

  4. Schedule an End-of-Day Update  
  Configure another trigger to fetch final game results at the end of the day.  

  5. Update Records in Airtable  
  Modify existing Airtable records with final scores and game outcomes for complete tracking.  

  6. Customize for Other Sports  
  Adjust API parameters to retrieve data for different sports and betting odds, making the system flexible for multiple use cases.  

  This structured workflow automates sports betting data collection and updates, ensuring accurate and real-time tracking of odds and game results. By integrating TheOddsAPI with Airtable, you can build scalable applications for predictive sports analytics and betting insights.  

nodes_used:
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name: Complete business WhatsApp AI-Powered RAG Chatbot using OpenAI
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-04T10:29:23.985Z
updated_at: 2025-02-04T10:29:23.985Z
url: https://n8n.io/workflows/2845
scraped_at: 2025-12-24 13:02:42
description:
  The provided workflow in n8n is designed to create a Business WhatsApp AI RAG (Retrieval-Augmented Generation) Chatbot.

  How it works:
  Webhook Setup: The workflow begins by setting up webhooks for verification and response. The Verify webhook receives GET requests and sends back a verification code, while the Respond webhook handles incoming POST requests from Meta regarding WhatsApp messages.
  Message Handling: Once a message is received, the workflow checks if the incoming JSON contains a user message. If it does, the message is processed further; otherwise, a generic response is sent.
  AI Agent Interaction: The user's message is passed to the AI Agent node, which uses a conversational agent with a predefined system message tailored for an electronics store. This ensures that the AI provides accurate and professional responses based on the knowledge base.
  Knowledge Base Utilization: The AI Agent references a knowledge base stored in Qdrant, a vector database. Documents from Google Drive are downloaded, vectorized using OpenAI embeddings, and stored in Qdrant for retrieval during conversations.
  Response Generation: The AI Agent generates a response using the OpenAI chat model (gpt-4o-mini) and sends it back to the user via WhatsApp.

  Set up steps:
  Create Qdrant Collection:
     Update the QDRANTURL and COLLECTION variables in the workflow.
     Use the Create collection HTTP request node to initialize the collection in Qdrant.

  Vectorize Documents:
     Configure the Get folder and Download Files nodes to fetch documents from a specified Google Drive folder.
     Use the Embeddings OpenAI node to generate embeddings for the downloaded files.
     Store the vectorized documents in Qdrant using the Qdrant Vector Store node.

  Configure Webhooks:
     Ensure both Verify and Respond webhooks have the same URL.
     Set the Verify webhook to use the GET HTTP method and the Respond webhook to use the POST HTTP method.

  Set Up AI Agent:
     Define the system prompt for the AI Agent, specifying guidelines for product information, technical support, customer service, and knowledge base usage.
     Link the AI Agent to the OpenAI chat model and configure any additional tools as needed.

  Test Workflow:
     Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all components are functioning correctly.
     Monitor the flow of data through the nodes and verify that responses are being generated and sent accurately.

  By following these steps, the workflow will be fully operational, enabling a robust AI-powered chatbot capable of handling customer inquiries via WhatsApp.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

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name: AI Voice Chatbot with ElevenLabs & OpenAI for Customer Service and Restaurants
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-04T13:13:55.888Z
updated_at: 2025-02-04T13:13:55.888Z
url: https://n8n.io/workflows/2846
scraped_at: 2025-12-24 13:02:42
description:
  The "Voice RAG Chatbot with ElevenLabs and OpenAI" workflow in n8n is designed to create an interactive voice-based chatbot system that leverages both text and voice inputs for providing information. Ideal for shops, commercial activities and restaurants

  How it works:

  Here's how it operates:

  Webhook Activation: The process begins when a user interacts with the voice agent set up on ElevenLabs, triggering a webhook in n8n. This webhook sends a question from the user to the AI Agent node.
  AI Agent Processing: Upon receiving the query, the AI Agent node processes the input using predefined prompts and tools. It extracts relevant information from the knowledge base stored within the Qdrant vector database.
  Knowledge Base Retrieval: The Vector Store Tool node interfaces with the Qdrant Vector Store to retrieve pertinent documents or data segments matching the user’s query.
  Text Generation: Using the retrieved information, the OpenAI Chat Model generates a coherent response tailored to the user’s question.
  Response Delivery: The generated response is sent back through another webhook to ElevenLabs, where it is converted into speech and delivered audibly to the user.
  Continuous Interaction: For ongoing conversations, the Window Buffer Memory ensures context retention by maintaining a history of interactions, enhancing the conversational flow.

  Set up steps:

  To configure this workflow effectively, follow these detailed setup instructions:

  ElevenLabs Agent Creation:
  	Create a FREE account on ElevenLabs
     Begin by creating an agent on ElevenLabs (e.g., named 'test_n8n').
     Customize the first message and define the system prompt specific to your use case, such as portraying a character like a waiter at "Pizzeria da Michele".
     Add a Webhook tool labeled 'test_chatbot_elevenlabs' configured to receive questions via POST requests.

  Qdrant Collection Initialization:
     Utilize the HTTP Request nodes ('Create collection' and 'Refresh collection') to initialize and clear existing collections in Qdrant. Ensure you update placeholders QDRANTURL and COLLECTION accordingly.

  Document Vectorization:
     Use Google Drive integration to fetch documents from a designated folder. These documents are then downloaded and processed for embedding.
     Employ the Embeddings OpenAI node to generate embeddings for the downloaded files before storing them into Qdrant via the Qdrant Vector Store node.

  AI Agent Configuration:
     Define the system prompt for the AI Agent node which guides its behavior and responses based on the nature of queries expected (e.g., product details, troubleshooting tips).
     Link necessary models and tools including OpenAI language models and memory buffers to enhance interaction quality.

  Testing Workflow:
     Execute test runs of the entire workflow by clicking 'Test workflow' in n8n alongside initiating tests on the ElevenLabs side to confirm all components interact seamlessly.
     Monitor logs and outputs closely during testing phases to ensure accurate data flow between systems.

  Integration with Website:
     Finally, integrate the chatbot widget onto your business website replacing placeholder AGENT_ID with the actual identifier created earlier on ElevenLabs.

  By adhering to these comprehensive guidelines, users can successfully deploy a sophisticated voice-driven chatbot capable of delivering precise answers utilizing advanced retrieval-augmented generation techniques powered by OpenAI and ElevenLabs technologies.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

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name: Content Generator for WordPress v3
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2025-02-05T04:03:29.558Z
updated_at: 2025-02-05T04:03:29.558Z
url: https://n8n.io/workflows/2849
scraped_at: 2025-12-24 13:02:46
description:
  UPDATE
  This is an updated version of the previous Ultimate Content Generator for WordPress v2.
  https://n8n.io/workflows/2737-ultimate-content-generator-for-wordpress/

  It includes an additional workflow to generate content using Anthropic Claude in addition to OpenAI 4o.

  I also mofidied it to use the AI Agent Tool node with structured output for more consistent outputs.

  Lastly, the workflow updates a new table in AirTable with the published blog post url and featured image url.

  Overview- 
  This workflow automates the end-to-end process of creating, optimizing, and publishing content on WordPress.

  It integrates AI-powered tools, Airtable, and WordPress plugins to generate high-quality, on-brand posts effortlessly.

  Perfect for content creators, marketers, and business owners looking to save time and scale their content strategy.

  Features
  Content Creation:
  AI-Powered Content: Generates SEO-friendly blog posts with structured headings, relevant keywords, and meta descriptions.
  Custom Prompts: Tailor the AI-generated content to match your brand’s tone and voice.
  SEO Optimization: RankMath Plugin Integration: Updates RankMath SEO with focus keywords and meta descriptions, ensuring your content is search-engine optimized.
  Content Management: 
  Airtable Integration: Organizes content ideas, drafts, and publishing schedules in one place. Easily scalable for teams or solo creators.
  Visuals: 
  Branded Featured Images: Automatically generates on-brand images for every post.
  Publishing:
  Effortless Formatting: Adapts content to fit your WordPress theme and schedules it for publication.
  Workflow Steps
  Trigger: Initiated manually or on a schedule.
  Content Management: Retrieves and organizes ideas from Airtable.
  Content Generation: Generates AI-driven blog content tailored to your audience.
  SEO Optimization: Automatically updates RankMath with SEO details.
  Featured Image Creation: Produces on-brand images for the post.
  Publishing: Formats and schedules the post on WordPress.
  Prerequisites
  API Keys:
  OpenAI
  Claude
  Airtable
  WordPress REST API

  Custom Code:
  Add a small update to your WordPress theme’s functions.php file to enable seamless automation.

  Customization
  Replace Airtable with another content management system if preferred.
  Adjust AI prompts to reflect different tones, styles, or industries.
  Add integrations for additional plugins, analytics, or storage services.
  Usage
  Import the workflow into your n8n instance.
  Configure API credentials for WordPress, Airtable, OpenAI, Claude and Slack.
  Update your functions.php file with the provided code snippet.
  Customize prompts and Airtable structure for your content needs.
  Trigger the workflow manually or set it on a schedule.
  Notes
  Experiment with Airtable views or add filters for more granular control over your content pipeline.
  Extend the workflow to include social media posting or analytics tracking.

  Feel free to adapt and extend this workflow to meet your specific needs! 🎉

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name: BambooHR AI-Powered Company Policies and Benefits Chatbot
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-02-05T07:19:48.368Z
updated_at: 2025-02-05T07:19:48.368Z
url: https://n8n.io/workflows/2850
scraped_at: 2025-12-24 13:02:46
description:
  How it works
  This workflow enables companies to provide instant HR support by automating responses to employee queries about policies and benefits:  
  Retrieves company policies, benefits, and HR documents from BambooHR.  
  Uses AI to analyze and answer employee questions based on company records.  
  Identifies the most relevant contact person for escalations.  
  Seamlessly integrates with company systems to provide real-time HR assistance.
  Set up steps:  
  Estimated time: ~20 minutes
  Connect your BambooHR account to allow policy retrieval.  
  Configure AI parameters and access control settings.  
  (Optional) Set up the employee lookup tool for personalized responses.  
  Test the chatbot to ensure accurate responses and seamless integration.  

  Benefits
  This workflow is perfect for HR teams looking to enhance employee support while reducing manual inquiries.

  Outperform BambooHR's "Ask BambooHR" Chatbot

  #1. Superior specificity of replies to general inquiries




  #2.  More appropriate escalations when responding to sensitive employee concerns

nodes_used:
  ["n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.bambooHr","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.splitOut","n8n-nodes-base.filter","n8n-nodes-base.bambooHr","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.vectorStoreSupabase","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.bambooHr","n8n-nodes-base.filter","n8n-nodes-base.set","n8n-nodes-base.bambooHr","n8n-nodes-base.aggregate","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.bambooHr","n8n-nodes-base.filter","n8n-nodes-base.aggregate","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Modular & Customizable AI-Powered Email Routing: Text Classifier for eCommerce
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-05T10:23:35.794Z
updated_at: 2025-02-05T10:23:35.794Z
url: https://n8n.io/workflows/2851
scraped_at: 2025-12-24 13:02:45
description:
  How It Works
  Form Submission:
     The workflow starts with the On form submission node, which triggers when a user submits a contact form. The form collects the user's name, email, and message.

  Text Classification:
     The Text Classifier node uses an AI model (GPT-4) to classify the submitted message into one of the predefined categories:
       Request Quote: For quote requests.
       Product info: For general product inquiries.
       General problem: For issues or problems related to products.
       Order: For questions about placed orders.
       Other: For any messages that don’t fit the above categories.

  Email Routing:
     Based on the classification, the workflow routes the message to the appropriate department via email:
       Prod. Dep.: For product-related inquiries.
       Quote Dep.: For quote requests.
       Gen. Dep.: For general problems.
       Order Dep.: For order-related questions.
       Other Dep.: For all other inquiries.
     Each email includes the user's name, email, message, and the classified category.

  Data Logging:
     The workflow logs the form submission and classification results into a Google Sheets document. Each department has its own sheet where the data is appended, including:
       User’s name, email, and message.
       Submission date and time.
       Assigned category.
       Email recipient details.

  AI Model Integration:
     The OpenAI node provides the AI model (GPT-4) used by the Text Classifier to classify the messages. The model is instructed to classify the text into one of the predefined categories without additional explanations.

  Set Up Steps
  Configure the Form Trigger:
     Set up the On form submission node to collect user inputs (name, email, and message) and trigger the workflow.

  Set Up the Text Classifier:
     Configure the Text Classifier node to use the OpenAI model (GPT-4) for text classification.
     Define the categories and their descriptions (e.g., "Request Quote", "Product info", etc.).
     Set the fallback category to "Other" for unclassifiable messages.

  Configure Email Sending:
     Set up the Email Send nodes for each department (Prod. Dep., Quote Dep., Gen. Dep., Order Dep., Other Dep.).
     Configure the email subject, body, and reply-to address using the form data and classification results.
     Ensure SMTP credentials are correctly configured for sending emails.

  Set Up Google Sheets Integration:
     Configure the Google Sheets nodes to append data to the appropriate sheets for each department.
     Map the form data (name, email, message, date, category, and recipient) to the corresponding columns in the Google Sheets document.

  Test the Workflow:
     Submit a test form to ensure the workflow correctly classifies the message, sends the email to the right department, and logs the data in Google Sheets.
     Verify that the OpenAI model is classifying messages accurately.

  Activate the Workflow:
     Once tested, activate the workflow to automate the process of handling contact form submissions.

  Key Features
  Automated Classification**: Uses AI to classify messages into relevant categories, reducing manual effort.
  Email Routing**: Sends emails to the appropriate department based on the classification.
  Data Logging**: Logs all form submissions and classification results in Google Sheets for tracking and analysis.
  Scalability**: Easily adaptable to additional categories or departments by modifying the workflow.

  This workflow is ideal for eCommerce businesses or customer support teams looking to automate and streamline the handling of contact form submissions.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.formTrigger","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.emailSend","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote"]

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name: AI-Powered Email Automation for Business: Summarize & Respond with RAG
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-05T13:22:24.743Z
updated_at: 2025-02-05T13:22:24.743Z
url: https://n8n.io/workflows/2852
scraped_at: 2025-12-24 13:02:43
description:
  This workflow is ideal for businesses looking to automate their email responses, especially for handling inquiries about company information. It leverages AI to ensure accurate and professional communication.

  How It Works
  Email Trigger:
     The workflow starts with the Email Trigger (IMAP) node, which monitors an email inbox for new messages. When a new email arrives, it triggers the workflow.

  Email Preprocessing:
     The Markdown node converts the email's HTML content into plain text for easier processing by the AI models.

  Email Summarization:
     The Email Summarization Chain node uses an AI model (DeepSeek R1) to generate a concise summary of the email. The summary is limited to 100 words and is written in Italian.

  Email Classification:
     The Email Classifier node categorizes the email into predefined categories (e.g., "Company info request"). If the email does not fit any category, it is classified as "other".

  Email Response Generation:
     The Write email node uses an AI model (OpenAI) to draft a professional response to the email. The response is based on the email content and is limited to 100 words.
     The Review email node uses another AI model (DeepSeek) to review and format the drafted response. It ensures the response is professional and formatted in HTML (e.g., using `, , , ` tags where necessary).

  Email Sending:
     The Send Email node sends the reviewed and formatted response back to the original sender.

  Vector Database Integration:
     The Qdrant Vector Store node retrieves relevant information from a vector database (Qdrant) to assist in generating accurate responses. This is particularly useful for emails classified as "Company info request".
     The Embeddings OpenAI node generates embeddings for the email content, which are used to query the vector database.

  Document Vectorization:
     The workflow includes steps to create and refresh a Qdrant collection (Create collection and Refresh collection nodes).
     Documents from Google Drive are downloaded (Get folder and Download Files nodes), processed into embeddings (Embeddings OpenAI1 node), and stored in the Qdrant vector store (Qdrant Vector Store1 node).

  Set Up Steps
  Configure Email Trigger:
     Set up the Email Trigger (IMAP) node with the appropriate IMAP credentials to monitor the email inbox.

  Set Up AI Models:
     Configure the DeepSeek R1, OpenAI, and DeepSeek nodes with the appropriate API credentials for text summarization, response generation, and review.

  Set Up Email Classification:
     Define the categories in the Email Classifier node (e.g., "Company info request", "Other").
     Ensure the OpenAI 4-o-mini node is configured to assist in classification.

  Set Up Vector Database:
     Configure the Qdrant Vector Store and Qdrant Vector Store1 nodes with the appropriate Qdrant API credentials and collection details.
     Set up the Embeddings OpenAI and Embeddings OpenAI1 nodes to generate embeddings for the email content and documents.

  Set Up Document Processing:
     Configure the Get folder and Download Files nodes to access and download documents from Google Drive.
     Use the Token Splitter and Default Data Loader nodes to process and split the documents into manageable chunks for vectorization.

  Set Up Email Sending:
     Configure the Send Email node with the appropriate SMTP credentials to send responses.

  Test the Workflow:
     Trigger the workflow manually using the When clicking ‘Test workflow’ node to ensure all steps execute correctly.
     Verify that emails are summarized, classified, and responded to accurately.

  Activate the Workflow:
     Once tested, activate the workflow to automate the process of handling incoming emails.

  Key Features
  Automated Email Handling**: Automatically processes incoming emails, summarizes them, and generates professional responses.
  AI-Powered Classification**: Uses AI to classify emails into relevant categories for targeted responses.
  Vector Database Integration**: Retrieves relevant information from a vector database to enhance response accuracy.
  Document Vectorization**: Processes and stores documents from Google Drive in a vector database for quick retrieval.
  Professional Email Formatting**: Ensures responses are professionally formatted and concise.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.markdown","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.emailSend","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

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name: Spotify to YouTube Playlist Synchronization
author_name: Lugnicca
author_username: lugnicca
categories: []
created_at: 2025-02-05T17:50:00.354Z
updated_at: 2025-02-05T17:50:00.354Z
url: https://n8n.io/workflows/2853
scraped_at: 2025-12-24 13:02:46
description:
  Spotify to YouTube Playlist Synchronization
  A workflow that maintains a YouTube playlist in sync with a Spotify playlist, featuring smart video matching and persistent synchronization.

  Key Features
  One-way Sync**: Spotify playlist → YouTube playlist (additions and deletions)
  Continuous Monitoring**: Automatic synchronization (every hour by default, but you can put any time you want)
  Smart Video Matching**: Considers video length and content relevance
  Auto-Recovery**: Automatically handles deleted YouTube videos
  Database Backup**: Persistent storage using Supabase

  Prerequisites

  Supabase project with the following table structure:
  CREATE TABLE IF NOT EXISTS musics (
      id TEXT PRIMARY KEY,
      title TEXT NOT NULL,
      artist TEXT NOT NULL,
      duration INT8 NOT NULL,
      youtube_video_id TEXT,
      to_delete BOOLEAN DEFAULT FALSE
  );
  Empty YouTube playlist (recommended as duplicates are not handled)
  Configured credentials for YouTube, Spotify, and Supabase APIs
  Properly set variables in all "variables" nodes (variables, variables1, variables2, variables3, variables4 (all the same))
  Activate the workflow !

nodes_used:
  ["n8n-nodes-base.spotify","n8n-nodes-base.spotify","n8n-nodes-base.if","n8n-nodes-base.supabase","n8n-nodes-base.supabase","n8n-nodes-base.supabase","n8n-nodes-base.compareDatasets","n8n-nodes-base.spotify","n8n-nodes-base.noOp","n8n-nodes-base.supabase","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.wait","n8n-nodes-base.set","n8n-nodes-base.youTube","n8n-nodes-base.youTube","n8n-nodes-base.splitInBatches","n8n-nodes-base.if","n8n-nodes-base.youTube","n8n-nodes-base.supabase","n8n-nodes-base.discord","n8n-nodes-base.discord","n8n-nodes-base.supabase","n8n-nodes-base.aggregate","n8n-nodes-base.if","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.set","n8n-nodes-base.youTube","n8n-nodes-base.compareDatasets","n8n-nodes-base.supabase","n8n-nodes-base.removeDuplicates","n8n-nodes-base.youTube","n8n-nodes-base.compareDatasets","n8n-nodes-base.supabase","n8n-nodes-base.supabase","n8n-nodes-base.supabase","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.set","n8n-nodes-base.supabase","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Automatic Youtube Shorts Generator
author_name: Samautomation.work
author_username: contactexpertly
categories: []
created_at: 2025-02-07T00:47:46.429Z
updated_at: 2025-02-07T00:47:46.429Z
url: https://n8n.io/workflows/2856
scraped_at: 2025-12-24 13:02:44
description:
  Automated YouTube Shorts Creator from Google News Trends
  Transform trending Google News articles into engaging YouTube Shorts with this fully automated workflow. Save time and effort while creating dynamic, eye-catching videos that are perfect for content creators, journalists, and digital marketers.

  Key Features
   Trend Analysis: Automatically fetch Google News articles and entertainment trends every 24 hours.
  AI-Powered Video Creation:                    
   Generate accurate transcripts from articles using Deepseek AI.
   Create visually appealing backgrounds with AI image generation tools.
  Add voiceovers with ElevenLabs TTS or similar services.
                                        
  Dynamic Video Compilation:                    
  Combine images, audio, and background music to craft short, dynamic videos.
  Fully customizable video length and music selection.
                                        
  YouTube Integration: Upload videos directly to YouTube, complete with autogenerated titles and descriptions.
  Organized Management: Titles and descriptions are saved to a Google Sheet for easy tracking and editing.

  How It Works
  Fetch trending topics from Google News using Deepseek.
  Convert the article's content into a readable transcript.
  Automatically create a voiceover, generate visuals, and combine them into a complete video.
  Upload the video to YouTube with a dynamic caption and description.

  Who Is It For?
  Content creators looking to expand their reach on YouTube Shorts.
  News agencies aiming to deliver content faster.
  Digital marketers needing quick, engaging videos.

  Setup Requirements
  API keys for Google News, ElevenLabs, OpenAI, and Deepseek.
  Basic knowledge of API integration and video content creation.

  Start creating trending, eye-catching YouTube Shorts in minutes. Automate your workflow and stay ahead in the content game!

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.convertToFile","n8n-nodes-base.wait","n8n-nodes-base.googleCloudStorage","n8n-nodes-base.limit","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.googleCloudStorage","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.youTube","n8n-nodes-base.googleSheets"]

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name: Pattern for Multiple Triggers Combined to Continue Workflow
author_name: Hubschrauber
author_username: hubschrauber
categories: []
created_at: 2025-02-07T01:14:59.594Z
updated_at: 2025-02-07T01:14:59.594Z
url: https://n8n.io/workflows/2857
scraped_at: 2025-12-24 13:02:47
description:
  Overview

  This template describes a possible approach to handle a pseudo-callback/trigger from an independent, external process (initiated from a workflow) and combine the received input with the workflow execution that is already in progress.  This requires the external system to pass through some context information (resumeUrl), but allows the "primary" workflow execution to continue with BOTH its own (previous-node) context, AND the input received in the "secondary" trigger/process.

  Primary Workflow Trigger/Execution
  The workflow path from the primary trigger initiates some external, independent process and provides "context" which includes the value of $execution.resumeUrl. This execution then reaches a Wait node configured with Resume - On Webhook Call  and stops until a call to resumeUrl is received.

  External, Independent Process
  The external, independent process could be anything like a Telegram conversation, or a web-service as long as:
  it results in a single execution of the Secondary Workflow Trigger, and
  it can pass through the value of resumeUrl associated with the Primary Workflow Execution

  Secondary Workflow Trigger/Execution
  The secondary workflow execution can start with any kind of trigger as long as part of the input can include the resumeUrl.  To combine / rejoin the primary workflow execution, this execution passes along whatever it receives from its trigger input to the resume-webhook endpoint on the Wait node.

  Notes
  IMPORTANT: The workflow ids in the Set nodes marked **Update Me have embedded references to the workflow IDs in the original system.  They will need to be CHANGED to make this demo work.
  Note: The Resume Other Workflow Execution node in the template uses the $env.WEBHOOK_URL configuration to convert to an internal "localhost" call in a Docker environment.  This can be done differently.
  ALERT:** This pattern is NOT suitable for a workflow that handles multiple items because the first workflow execution will only be waiting for one callback.
  The second workflow (not the second trigger in the first workflow) is just to demonstrate how the Independent, External Process needs to work.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.respondToWebhook","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Automate GitLab Merge Requests Using APIs with n8n
author_name: Aditya Gaur
author_username: autom8r
categories: []
created_at: 2025-02-07T07:44:07.959Z
updated_at: 2025-02-07T07:44:07.959Z
url: https://n8n.io/workflows/2858
scraped_at: 2025-12-24 13:02:45
description:
  Who is this template for?

  This template is designed for developers, DevOps engineers, and automation enthusiasts who want to streamline their GitLab merge request process using n8n, a low-code workflow automation tool. It eliminates manual intervention by automating the merging of GitLab branches through API calls.

  How it works ?

  Trigger the workflow: The workflow can be triggered by a webhook, a scheduled event, or a GitLab event (e.g., a new merge request is created or approved).

  Fetch Merge Request Details: n8n makes an API call to GitLab to retrieve merge request details.

  Check Merge Conditions: The workflow validates whether the merge request meets predefined conditions (e.g., approvals met, CI/CD pipelines passed).

  Perform the Merge: If all conditions are met, n8n sends a request to the GitLab API to merge the branch automatically.

  Setup Steps

  1. Prerequisites

  An n8n instance (Self-hosted or Cloud)

  A GitLab personal access token with API access

  A GitLab repository with merge requests enabled

  2. Create the n8n Workflow

  Set up a trigger: Choose a trigger node (Webhook, Cron, or GitLab Trigger).

  Fetch merge request details: Add an HTTP Request node to call GET /merge_requests/:id from GitLab API.

  Validate conditions:

  Check if the merge request has necessary approvals.

  Ensure CI/CD pipelines have passed.

  Merge the request:

  Use an HTTP Request node to call PUT /merge_requests/:id/merge API.

  3. Test the Workflow

  Create a test merge request.

  Check if the workflow triggers and merges automatically.

  Debug using n8n logs if needed.

  4. Deploy and Monitor

  Deploy the workflow in production.

  Use n8n’s monitoring features to track execution.

  This template enables seamless GitLab merge automation, improving efficiency and reducing manual work!

  Note: 
  Never hard code API token or secret in your https request.

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.scheduleTrigger"]

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name: Chat with Postgresql Database
author_name: KumoHQ
author_username: kumohq
categories: []
created_at: 2025-02-07T10:20:16.255Z
updated_at: 2025-02-07T10:20:16.255Z
url: https://n8n.io/workflows/2859
scraped_at: 2025-12-24 13:02:41
description:
  Who is this template for?

  This workflow template is designed for any professionals seeking relevent data from database using natural language.

  How it works

  Each time user ask's question using the n8n chat interface, the workflow runs.
  Then the message is processed by AI Agent using relevent tools - Execute SQL Query, Get DB Schema and Tables List and Get Table Definition, if required. Agent uses these tool to form and run sql query which are necessary to answer the questions.
  Once AI Agent has the data, it uses it to form answer and returns it to the user.

  Set up instructions

  Complete the Set up credentials step when you first open the workflow. You'll need a Postgresql Credentials, and OpenAI api key.

  Template was created in n8n v1.77.0

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.postgresTool","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.postgresTool","n8n-nodes-base.postgresTool"]

workflow_json_compressed:
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name: AI Automated HR Workflow for CV Analysis and Candidate Evaluation
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-07T12:30:06.464Z
updated_at: 2025-02-07T12:30:06.464Z
url: https://n8n.io/workflows/2860
scraped_at: 2025-12-24 13:02:41
description:
  How it Works

  This workflow automates the process of handling job applications by extracting relevant information from submitted CVs, analyzing the candidate's qualifications against a predefined profile, and storing the results in a Google Sheet. Here’s how it operates:

  Data Collection and Extraction: 
     The workflow begins with a form submission (On form submission node), which triggers the extraction of data from the uploaded CV file using the Extract from File node.
     Two informationExtractor nodes (Qualifications and Personal Data) are used to parse specific details such as educational background, work history, skills, city, birthdate, and telephone number from the text content of the CV.

  Processing and Evaluation:
     A Merge node combines the extracted personal and qualification data into a single output.
     This merged data is then passed through a Summarization Chain that generates a concise summary of the candidate’s profile.
     An HR Expert chain evaluates the candidate against a desired profile (Profile Wanted), assigning a score and providing considerations for hiring.
     Finally, all collected and processed data including the evaluation results are appended to a Google Sheets document via the Google Sheets node for further review or reporting purposes [[9]].

  Set Up Steps

  To replicate this workflow within your own n8n environment, follow these steps:

  Configuration:
     Begin by setting up an n8n instance if you haven't already; you can sign up directly on their website or self-host the application.
     Import the provided JSON configuration into your n8n workspace. Ensure that all necessary credentials (e.g., Google Drive, Google Sheets, OpenAI API keys) are correctly configured under the Credentials section since some nodes require external service integrations like Google APIs and OpenAI for language processing tasks.

  Customization:
     Adjust the parameters of each node according to your specific requirements. For example, modify the fields in the formTrigger node to match what kind of information you wish to collect from applicants.
     Customize the prompts given to AI models in nodes like Qualifications, Summarization Chain, and HR Expert so they align with the type of analyses you want performed on the candidates' profiles.
     Update the destination settings in the Google Sheets node to point towards your own spreadsheet where you would like the final outputs recorded.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["n8n-nodes-base.formTrigger","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.chainSummarization","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: AI-powered email processing autoresponder and response approval (Yes/No)
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-07T15:16:27.194Z
updated_at: 2025-02-07T15:16:27.194Z
url: https://n8n.io/workflows/2861
scraped_at: 2025-12-24 13:02:44
description:
  How it Works

  This workflow is designed to automate the process of handling incoming emails, summarizing their content, generating appropriate responses, and obtaining approval before sending replies. Below are the key operational steps:

  Email Reception and Summarization:
     The workflow starts with an Email Trigger (IMAP) node that listens for new emails in a specified inbox.
     Once an email is received, its HTML content is processed by a Markdown node to convert it into plain text if necessary, followed by an Email Summarization Chain node which uses AI to create a concise summary of the email's content using prompts tailored for this purpose.

  Response Generation and Approval:
     A Write email node generates a professional response based on the summarized content, utilizing predefined templates and guidelines such as keeping responses under 100 words and ensuring they're formatted correctly in HTML.
     Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through a Gmail node configured with double-approval settings. If approved (Approve?), the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention.

  Set Up Steps

  To replicate this workflow within your own n8n environment, follow these essential configuration steps:

  Configuration:
     Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site.
     Import the provided JSON configuration file into your workspace, making sure all required credentials like IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials section since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals.

  Customization:
     Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?.
     Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications. Ensure correct mappings between fields when appending data to external systems like Google Sheets or similar platforms where records might need tracking post-interaction completion. 

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.markdown","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.emailSend","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 4248 characters]


name: Effortless Email Management with AI-Powered Summarization & Review
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-02-07T15:34:24.029Z
updated_at: 2025-02-07T15:34:24.029Z
url: https://n8n.io/workflows/2862
scraped_at: 2025-12-24 13:02:42
description:
  How it Works

  This workflow automates the handling of incoming emails, summarizes their content, generates appropriate responses using a retrieval-augmented generation (RAG) approach, and obtains approval or suggestions before sending replies. Below is an explanation of its functionality divided into two main sections:

  Email Handling and Summarization:
     The process begins with the Email Trigger (IMAP) node which listens for new emails in a specified inbox.
     Once an email is received, the Markdown node converts its HTML content into plain text if necessary, followed by the Email Summarization Chain that uses AI to create a concise summary of up to 100 words.

  Response Generation and Approval:
     A Write email node generates a professional response based on the summarized content, ensuring brevity and professionalism while keeping within the word limit.
     Before sending out any automated replies, the system sends these drafts via Gmail for human review and approval through the Gmail node configured with free-text response options. If approved, the finalized email is sent back to the original sender using the Send Email node; otherwise, it loops back for further edits or manual intervention.
     Additionally, there's a Text Classifier node designed to categorize feedback from humans as either "Approved" or "Declined", guiding whether the email should proceed directly to being sent or require additional editing.

  Set Up Steps

  To replicate this workflow within your own n8n environment, follow these essential configuration steps:

  Configuration:
     Begin by setting up an n8n instance either locally or via cloud services offered directly from their official site.
     Import the provided JSON configuration file into your workspace, making sure all required credentials such as IMAP, SMTP, OpenAI API keys, etc., are properly set up under Credentials since multiple nodes rely heavily on external integrations for functionalities like reading emails, generating summaries, crafting replies, and managing approvals.

  Customization:
     Adjust parameters according to specific business needs, including but not limited to adjusting the conditions used during conditional checks performed by nodes like Approve?.
     Modify the template messages given to AI models so they align closely with organizational tone & style preferences while maintaining professionalism expected in business communications.
     Ensure correct mappings between fields when appending data to external systems where records might need tracking post-interaction completion, such as Google Sheets or similar platforms.

nodes_used:
  ["n8n-nodes-base.emailReadImap","n8n-nodes-base.markdown","n8n-nodes-base.emailSend","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.chainSummarization","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.set","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatDeepSeek","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6296 characters]


name: 🐋🤖 DeepSeek AI Agent + Telegram + LONG TERM Memory 🧠
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2025-02-08T00:49:32.604Z
updated_at: 2025-02-08T00:49:32.604Z
url: https://n8n.io/workflows/2864
scraped_at: 2025-12-24 13:02:42
description:
  This n8n workflow template is designed to integrate a DeepSeek AI agent with Telegram, incorporating long-term memory capabilities for personalized and context-aware responses. Here's a detailed breakdown:

  Core Features
  Telegram Integration  
  Uses a webhook to receive messages from Telegram users.
  Validates user identity and message content before processing.

  AI-Powered Responses  
  Employs DeepSeek's AI models for conversational interactions.
  Includes memory capabilities to personalize responses based on past interactions.

  Error Handling  
  Sends an error message if the input cannot be processed.

  Model Options 🧠
  DeepSeek-V3 Chat**: Handles general conversational tasks.
  DeepSeek-R1 Reasoning**: Provides advanced reasoning capabilities for complex queries.
  Memory Buffer Window**: Maintains session context for ongoing conversations.

  Quick Setup 🛠️
  Telegram Webhook Configuration
  Set up a webhook using the Telegram Bot API:
        https://api.telegram.org/bot{my_bot_token}/setWebhook?url={url_to_send_updates_to}
        Replace {my_bot_token} with your bot's token and {url_to_send_updates_to} with your n8n webhook URL.
  Verify the webhook setup using:
        https://api.telegram.org/bot{my_bot_token}/getWebhookInfo
  DeepSeek API Configuration
  Base URL: https://api.deepseek.com
  Obtain your API key from the DeepSeek platform.

  Implementation Details 🔧
  User Validation
  The workflow validates the user's first name, last name, and ID using data from incoming Telegram messages.
  Only authorized users proceed to the next steps.

  Message Routing
  Routes messages based on their type (text, audio, or image) using a switch node.
  Ensures appropriate handling for each message format.

  AI Agent Interaction
  Processes text input using DeepSeek-V3 or DeepSeek-R1 models.
  Customizable system prompts define the AI's behavior and rules, ensuring user-centric and context-aware responses.

  Memory Management
  Retrieves long-term memories stored in Google Docs to enhance personalization.
  Saves new memories based on user interactions, ensuring continuity across sessions.

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.googleDocsTool","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDocs","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

workflow_json_compressed:
  [encoded data, 7176 characters]


name: Automate Pinterest Analysis & AI-Powered Content Suggestions With Pinterest API
author_name: Marketing Canopy
author_username: johnmarketingcanopy
categories: []
created_at: 2025-02-08T01:27:33.308Z
updated_at: 2025-02-08T01:27:33.308Z
url: https://n8n.io/workflows/2865
scraped_at: 2025-12-24 13:02:45
description:
  Automate Pinterest Analysis & AI-Powered Content Suggestions With Pinterest API

  This workflow automates the collection, analysis, and summarization of Pinterest Pin data to help marketers optimize content strategy. It gathers Pinterest Pin performance data, analyzes trends using an AI agent, and delivers actionable insights to the Marketing Manager via email.  

  This setup is ideal for content creators and marketing teams who need weekly insights on Pinterest trends to refine their content calendar and audience engagement strategy.  

  Prerequisites  

  Before setting up this workflow, ensure you have the following:  

  Pinterest API Access & Developer Account  
  Sign up at Pinterest Developers and obtain API credentials.  
  Ensure you have access to both Organic and Paid Pin data.  

  Airtable Account & API Key  
  Create an account at Airtable and set up a database.  
  Obtain an API key from Account Settings.  

  AI Agent for Trend Analysis  
  An AI-powered agent (such as OpenAI's GPT or a custom ML model) is required to analyze Pinterest trends.  
  Ensure integration with your workflow automation tool (e.g., Zapier, Make, or a custom Python script).  

  Email Automation Setup  
  Configure an SMTP email service (e.g., Gmail, Outlook, SendGrid) to send the summarized results to the Marketing Manager.  

  Step-by-Step Guide to Automating Pinterest Pin Analysis  

  1. Scheduled Trigger for Data Collection  
  At 8:00 AM (or your preferred time), an automated trigger starts the workflow.  
  Adjust the timing based on your marketing schedule to optimize trend tracking.  

  2. Fetch Data from Pinterest API  
  Retrieve recent Pinterest Pin performance data, including impressions, clicks, saves, and engagement rate.  
  Ensure both Organic and Paid Ads data are labeled correctly for clarity.  

  3. Store Data in Airtable  
  Pins are logged and categorized in an Airtable database for further analysis.  

  Sample Airtable Template for Pinterest Pins  
  | Column Name    | Description                           |
  |---------------|---------------------------------------|
  | pin_id     | Unique identifier for each Pin      |
  | created_at | Timestamp of when the Pin was created |
  | title      | Title of the Pin                     |
  | description| Short description of the Pin        |
  | link       | URL linking to the Pin              |
  | type       | Type of Pin (e.g., organic, ad)     |

  4. AI Agent Analyzes Pinterest Trends  
  The AI model reviews the latest Pinterest data and identifies:  
  Trending Topics & Keywords**  
  Engagement Patterns**  
  Audience Interests & Behavior Changes**  
  Optimal Posting Times & Formats**  

  5. Generate Content Suggestions with AI  
  The AI Agent recommends new Pin ideas and content calendar updates to maximize engagement.  
  Suggestions include creative formats, hashtags, and timing adjustments for better performance.  

  6. Summary & Insights Generated by AI  
  A concise report is created, summarizing Pinterest trends and actionable insights for content strategy.  

  7. Email Report Sent to the Marketing Manager  
  The summary is emailed to the Marketing Manager to assist with content planning and execution.  
  The report includes:  
    Performance Overview of Recent Pins  
    Trending Content Ideas  
    Best Performing Pin Formats  
    AI-Generated Recommendations  

  This workflow enables marketing teams to automate Pinterest analysis and optimize their content strategy through AI-driven insights. 🚀  

nodes_used:
  ["@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.airtableTool","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.airtable","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chainSummarization","n8n-nodes-base.gmail","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Scrape Latest Github Trending Repositories
author_name: Teddy
author_username: teddysbeach
categories: []
created_at: 2025-02-08T03:25:14.572Z
updated_at: 2025-02-08T03:25:14.572Z
url: https://n8n.io/workflows/2866
scraped_at: 2025-12-24 13:02:44
description:
  Scrape Latest 20 TechCrunch Articles

  Who is this for?
  This workflow is designed for developers, researchers, and data analysts who need to track the latest trending repositories on GitHub. It is useful for anyone who wants to stay updated on popular open-source projects without manually browsing GitHub’s trending page.

  What problem is this workflow solving?
  Manually checking GitHub’s trending repositories daily can be time-consuming and inefficient. This workflow automates the extraction of trending repositories, providing structured data including repository name, author, description, programming language, and direct repository links. 

  What this workflow does
  This workflow scrapes the trending repositories from GitHub’s trending page and extracts essential metadata such as repository names, languages, descriptions, and URLs. It processes the extracted data and structures it into an easy-to-use format.

  Setup
  Ensure you have n8n installed and configured.
  Import this workflow into your n8n instance.
  Run the workflow manually or schedule it to execute at regular intervals.
  (Optional) Customize the extracted data or integrate it with other systems.

  How to customize this workflow to your needs
  Modify the HTTP request node to target different GitHub trending categories (e.g., specific programming languages).
  Add further processing steps such as filtering repositories by stars, forks, or specific keywords.
  Integrate this workflow with Slack, email, or a database to store or notify about trending repositories.

  Workflow Steps
  Trigger execution manually using the "When clicking ‘Test workflow’" node.
  Send an HTTP request to fetch GitHub’s trending page using "Request to Github Trend".
  Extract the trending repositories box from the HTML response using "Extract Box".
  Extract all repository data including names, authors, descriptions, and languages using "Extract all repositories".
  Convert extracted data into a structured list for easier processing using "Turn to a list".
  Extract detailed repository information using "Extract repository data".
  Format and set variables to ensure clean and structured data output using "Set Result Variables".

  Note: Since GitHub’s trending page updates dynamically, ensure you run this workflow periodically to capture the latest trends.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.html","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.set","n8n-nodes-base.html","n8n-nodes-base.html"]

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name: Credit Card Payment Reminder & Tracking-For Taiwan Banks
author_name: darrell_tw
author_username: darrelltw
categories: []
created_at: 2025-02-08T08:38:52.038Z
updated_at: 2025-02-08T08:38:52.038Z
url: https://n8n.io/workflows/2867
scraped_at: 2025-12-24 13:02:47
description:
  Workflow Description
  This workflow automates the processing of credit card statement emails from multiple banks. It extracts important payment details, stores them in Google Sheets, and creates calendar reminders in Google Calendar. Additionally, it allows users to update the payment status once the bill has been paid.

  Key Features
  Email Processing: Retrieves credit card statement emails from eight Taiwanese banks.
  PDF Parsing: Extracts payment due date and amount from email content or attached PDF files.
  Google Sheets Integration: Logs extracted data into a Google Sheets document for record-keeping.
  Google Calendar Integration: Creates Google Calendar events as reminders for due payments.
  Webhook for Payment Updates: Allows users to update the payment status via a webhook.

  Node Configurations

  1. Email Retrieval
  Purpose**: Fetches credit card statement emails from Gmail.
  Configuration**:
    Email Filters:
      SinoPac Bank: from:(newebill.banksinopac.com.tw) SinoPac Bank Credit Card E-Statement Notification
      Cathay United Bank: from:(service@pxbillrc01.cathaybk.com.tw) Cathay United Bank Monthly E-Statement
      CTBC Bank: from:(ebill@estats.ctbcbank.com) CTBC Bank Credit Card E-Statement
      Taipei Fubon Bank: from:(rs@cf.taipeifubon.com.tw) Taipei Fubon Bank Credit Card Statement
      E.SUN Commercial Bank: from:(estatement@esunbank.com) E.SUN Commercial Bank Credit Card E-Statement
      DBS Bank: from:(eservicetw@dbs.com) DBS Bank Credit Card E-Statement
      Union Bank of Taiwan: from:(聯邦銀行信用卡) Union Bank of Taiwan Credit Card E-Statement (Year Month)
      Taishin International Bank: from:(webmaster@bhurecv.taishinbank.com.tw) Taishin International Bank Credit Card E-Statement

  2. Extract Payment Information
  Purpose**: Extracts payment due date, total amount, and minimum payment amount.
  Methods**:
    Text-based Extraction: Uses regex to parse email body.
    PDF Parsing: Extracts text from PDF attachments.

  3. Data Processing and Storage
  3.1. Consolidate Extracted Data
  Purpose**: Standardizes extracted payment details.
  Data Fields**:
    payment_due_date
    payment_amount
    minimum_payment
    email_id
    bank
    email_subject

  3.2. Google Sheets Integration
  Purpose**: Stores the extracted data in a structured format.
  Configuration**:
    Sheet Name: n8n-Credit Card Payment Reminder
    Columns:
      calendar_id
      Paid
      Billing Period
      Amount
      Minimum Payment
      Bank
      email_id

  4. Google Calendar Integration
  4.1. Create Calendar Reminders
  Purpose**: Generates reminders for upcoming payments.
  Configuration**:
    Event Title: Credit Card Payment - {{ bank }}
    Due Date: payment_due_date
    Reminders:
      30 minutes before
      60 minutes before
      1 day before

  4.2. Update Payment Status
  Purpose**: Updates the calendar event once payment is made.
  Configuration**:
    Webhook URL: Automatically updates the Google Calendar event title and description.

  5. Webhook for Payment Updates
  Purpose: Users can mark a payment as **paid via a webhook.
  Configuration**:
    Webhook Path: darrell_demo_creditcard_pay_update_path
    Updates:
      Marks the payment as Paid
      Updates Google Calendar and Google Sheets

nodes_used:
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name: Backup your workflows to GitHub -- in (subfolders)
author_name: Nazmy
author_username: islamnazmi
categories: []
created_at: 2025-02-08T21:34:44.233Z
updated_at: 2025-02-08T21:34:44.233Z
url: https://n8n.io/workflows/2868
scraped_at: 2025-12-24 13:02:45
description:
  Based on Jonathan & Solomon work.

  &gt; The only addition I've made is a Set node. This node organizes workflows into subfolders within the GitHub repository based on their respective tags.

  How it works
  This workflow will backup your workflows to GitHub. It uses the n8n API node to export all workflows.

  It then loops over the data, checks in GitHub to see if a file exists that uses the credential's ID. Once checked it will:
  update the file on GitHub if it exists;
  create a new file if it doesn't exist;
  ignore if it's the same.

  Who is this for?
  People wanting to backup their workflows outside the server for safety purposes or to migrate to another server.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.n8n","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.merge","n8n-nodes-base.code","n8n-nodes-base.switch","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.github","n8n-nodes-base.github","n8n-nodes-base.splitInBatches","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.github","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.executeWorkflow","n8n-nodes-base.set","n8n-nodes-base.switch"]

workflow_json_compressed:
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name: Test Webhooks in n8n Without Changing WEBHOOK_URL (PostBin & BambooHR Example)
author_name: Ludwig
author_username: ludwig
categories: []
created_at: 2025-02-08T22:05:00.438Z
updated_at: 2025-02-08T22:05:00.438Z
url: https://n8n.io/workflows/2869
scraped_at: 2025-12-24 13:02:46
description:
  Using PostBin to Test Webhooks Without Changing WEBHOOK_URL  

  How it Works  
  Many new n8n users struggle with testing webhooks when running n8n on localhost, as external services cannot reach localhost. This workflow introduces a technique using PostBin, which provides a temporary, publicly accessible URL to receive webhook requests.  

  Generates a temporary webhook endpoint via PostBin.  
  Uses this endpoint in place of localhost to test webhooks.  
  Captures and displays the incoming webhook request data.  
  Enables debugging and iterating without modifying the WEBHOOK_URL environment variable.  

  Set Up Steps  
  Estimated time:** ~5–10 minutes  
  Create a PostBin instance to generate a publicly accessible webhook URL.  
  Copy the PostBin URL and use it as the webhook destination in n8n.  
  Trigger the webhook from an external service or manually.  
  Inspect the request payload in PostBin to verify data reception.  

  (EXAMPLE) Using PostBin for Webhook Testing in a BambooHR Integration  

  How it Works  
  In this example, we apply the PostBin technique to a BambooHR integration. Instead of manually configuring a webhook in BambooHR, this workflow automates webhook registration using the BambooHR API. The workflow:  

  Uses the BambooHR API to programmatically register the PostBin URL as a webhook.  
  Retrieves the most recent webhook calls made by BambooHR to the PostBin URL.  
  (Optional) Sends a personalized Slack message for new employees using OpenAI.  

  Set Up Steps  
  Estimated time:** ~15–20 minutes  
  Set up PostBin using the steps from the first section.  
  Log into BambooHR to generate an API key for authentication.  
  Run the workflow to register the PostBin URL as a webhook in BambooHR via the API.
  Retrieve recent webhook calls from PostBin to validate data reception.  
  (Optional) Send a Slack notification using the processed data.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.merge","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.postBin","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.postBin","n8n-nodes-base.postBin","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.postBin","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.aggregate","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.debugHelper","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.bambooHr","n8n-nodes-base.splitOut","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.aggregate","n8n-nodes-base.renameKeys","n8n-nodes-base.slack","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.postBin","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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