Workflow Templates

Tags: Sustainability, CSRD, Reporting, ESG, Compliance, Automation Context Hey! I'm Samir, a Supply Chain Engineer and Data Scientist from Paris, founder of LogiGreen Consulting We help companies automate sustainability workflows using AI, Data Analytics, and No-Code tools like N8N. > Sustainability Reporting meets Automation with n8n! 📬 For business inquiries, you can add me on Here What is a CSRD XHTML Report? Under the Corporate Sustainability Reporting Directive (CSRD), companies must publish their ESG disclosures in a machine-readable XHTML format, embedding XBRL tags that make the report structured and standardized. These files must follow strict formatting and tagging rules to ensure compliance, traceability, and accessibility for both regulators and analysts. Who is this template for? This workflow is designed for sustainability teams, ESG consultants, or developers who want to automatically check the structure and format of CSRD reports submitted in XHTML. How does it work? This N8N workflow automates the audit process: 📤 Input Node → Uploads or fetches the XHTML file via URL or Webhook. 🧪 Validates Structure → Uses a custom code node to parse HTML and identify required tags (e.g., <ix:nonNumeric>, namespaces). 📋 Outputs a Report → Returns a summary report of errors, warnings, and key metadata (like entity name, reporting period). 📤 Export Option → Save the results in Google Sheets or send via email. Prerequisite A sample XHTML file that you can find in my GitHub Repository Google Sheets API* and *OpenAI API** credentials Next Steps Follow the sticky notes inside each node to adjust parsing rules or extend validation to specific XBRL tags relevant to your sector (e.g., GHG emissions, water usage). *📺 Check my complete tutorial to understand how to use it: * 🎥 Check My Tutorial 🚀 Interested in combining CSRD compliance with automation and analytics? Let’s connect on LinkedIn Notes This workflow includes an example XHTML file to test the validator. You can plug this into your internal systems or even extend it with AI to auto-summarize the sustainability report. This workflow has been created with N8N 1.82.1 Submitted: April 3rd, 2025

This n8n workflow verifies the deliverability of mailing addresses stored in Groundhogg CRM by integrating with Lob’s address verification service. Who is this for? This template is designed for Groundhogg CRM users who need to ensure the accuracy of mailing addresses stored in their CRM systems. What problem is this workflow solving? / Use Case This workflow addresses the challenge of maintaining accurate mailing addresses in CRM databases by verifying the deliverability of addresses. What this workflow does A new contact is created in Groundhogg CRM Webhook sent to n8n Verify if the address is deliverable via LOB Report back to Groundhogg CRM Set Up Steps Watch this setup video: https://www.youtube.com/watch?v=nrV0P0Yz8FI Takes 10-30 minutes to set up Accounts Needed: Groundhogg CRM LOB Account (https://www.lob.com $0.00/mo 300 US addresses Verifications) n8n Before using this template, ensure you have API keys for your Groundhogg CRM app and Lob. Set up authentication for both services within n8n. How to customize this workflow to your needs You can customize this workflow by adjusting the trigger settings to match Groundhogg CRM’s workflow configuration. Additionally, you can modify the actions taken based on the deliverability outcome, such as updating custom fields or sending notifications.

This workflow integrates LINE BOT, AI Agent (GPT), Google Sheets, and Google Drive to enable users to search for file URLs using natural language. The AI Agent extracts the filename from the message, searches for the file in Google Sheets, and returns the corresponding Google Drive URL via LINE BOT. Supports natural language queries (e.g., "Find file 1.pdf for me") AI-powered filename extraction Google Sheets Lookup for file URLs Auto-response via LINE BOT How to Use This Template 1. Download & Import Copy and save the Template Code as a .json file. Go to n8n Editor → Click Import → Upload the file. 2. Update Required Fields Replace YOUR_GOOGLE_SHEET_ID with your actual Google Sheet ID. Replace YOUR_LINE_ACCESS_TOKEN with your LINE BOT Channel Access Token. 3. Activate & Test Click Execute Workflow to test manually. Set Webhook URL in LINE Developer Console. Features of This Template Supports Natural Language Queries (e.g., “Find file 1.pdf for me”) AI-powered filename extraction using OpenAI (GPT-4/3.5) Real-time file lookup in Google Sheets Automatic LINE BOT Response Fully Automated Workflow

Bulk Contact Deletion from HubSpot via Uploaded Excel / CSV File This workflow allows you to automate the deletion of HubSpot contacts based on email addresses provided in an uploaded Excel (.xlsx) file. It's ideal for bulk-cleaning outdated or invalid contact data. ✅ Prerequisites Before using this workflow, ensure you have the following: A valid HubSpot App Token with permissions to search and delete contacts. An Excel (.xlsx) file with a column labeled emails containing the contact emails to be deleted. n8n self-hosted or cloud environment with: Webhook node enabled and accessible. HubSpot node credentials configured. Basic familiarity with n8n node configuration for custom adjustments (optional). 📃Sample Document Download 🧠 n8n Workflow: Delete HubSpot Contacts from an Uploaded Excel File This n8n workflow allows you to upload an Excel file containing contact email addresses. It will check each one in HubSpot and delete the contact if it exists. 🔗 Workflow Overview 📥 1. Trigger via Webhook (POST) The workflow starts when a .xlsx file is uploaded via an HTTP POST request to the webhook. This Excel file should contain a column with contact email addresses. 📄 2. Extract Data from Excel The uploaded file is parsed, and its rows are converted into structured JSON items, making each email address available for further processing. 🧹 3. Normalize Data The data is cleaned and normalized — for example, mapping column headers (e.g., emails) into a standard email field, ensuring consistent downstream logic. 🔁 4. Loop Through Contacts Each row (contact) is processed individually using batch looping. This allows for fine-grained error handling and sequential processing. 🔎 5. Search for Contact in HubSpot For each contact, a search query is made in HubSpot based on the email address. The workflow only fetches the first result (if any). 🧪 6. Check if Contact Exists An IF condition checks whether the contact was found (i.e., if a HubSpot contact ID exists): ✅ Yes → proceed to delete the contact. ❌ No → skip deletion and continue to the next. 🗑️ 7. Delete Contact If a contact exists, it is deleted from HubSpot using its internal contact ID. 🛠️ 8. Optional Placeholder for Post-Processing A placeholder node named “Replace Me” is included for any custom logic you may want to add after the deletion step, such as: Logging Notifications Writing to external storage ✅ Use Cases Bulk delete old or bounced email addresses from HubSpot. Clean up contacts based on external suppression lists. Automate regular CRM hygiene processes. 💡 Suggested Enhancements ✍️ Log results to Google Sheets or a database 📬 Send completion report via email or Slack 🔁 Add retry logic for temporary API failures 🔍 Validate email format before making requests 📎 Requirements n8n (self-hosted or cloud) HubSpot App Token (set up in n8n credentials) Excel file (.xlsx) with a column for email 📦 Files No external files are required. All logic is contained within the n8n workflow. 🚀 Getting Started Deploy the workflow in n8n. Copy the webhook URL and use it in your app or API client (like Postman). Upload an Excel file containing contact emails via POST request. Watch as it searches and deletes matches in HubSpot.

This workflow allows you to add articles to a Notion reading list by accessing a Discord slash command. Prerequisites A Notion account and credentials, and a reading list similar to this template. A Discord account and credentials, and Discord Slash Command connected to n8n. Nodes Webhook node triggers the workflow whenever the Discord Slash command is issued. IF node checks the type returned by Discord. If the type is not equal to 1, it will return true, otherwise false. HTTP Request node makes an HTTP call to the link and gets the HTML of the webpage. HTML Extract node extracts the title from the HTML which we will use in the next node. Notion node adds the link to your Notion reading list. Set nodes set the reply values for Discord and register the Interaction Endpoint URL.

About The Product Hunt Automation Staying up-to-date with specific topics and launches on Product Hunt can be time-consuming. Manually checking the site multiple times a day interrupts your workflow and risks missing important launches. What if you could automatically get relevant launches delivered to your Slack workspace? How to Monitor Product Hunt In this guide, you'll learn how to create a Product Hunt monitoring system using Airtop's built-in node in n8n. This automation will scan Product Hunt for your chosen topics and deliver the most relevant launches directly to Slack. What You'll Need A free Airtop API key A Slack workspace with permissions to add incoming webhooks Estimated setup time: 5 minutes Understanding the Process The Monitor Product Hunt automation uses Airtop's cloud browser capabilities to access Product Hunt and extract launch information. Here's what happens: Airtop visits Product Hunt and navigates the page It searches for and extracts up to 5 launches related to your chosen topic The information is formatted and sent to your specified Slack channel This process can run on your preferred schedule, ensuring you never miss relevant launches. Setting Up Your Automation We've created a ready-to-use template that handles all the complex parts. Here's how to get started: Connect your Airtop account by adding the API key you created Connect your Slack account Set your prompt in the Airtop node. For this example, we’ve set it to be “Extract up to 5 launches related to AI products” Choose your preferred monitoring schedule. Customization Options While our template works immediately, you might want to customize it for your specific needs: Adjust the prompt and the maximum number of launches to monitor Customize the Slack message format Change the monitoring frequency Add filters for particular keywords or companies Real-World Applications Here's how teams can use this automation: A startup's engineering team could track trends in other product’s tech stack, helping them stay informed about potential issues and improvements. A product manager can track launches of competitor products, enabling them to gather valuable market insights and user feedback directly from the tech community on that launch. Best Practices To get the most out of this automation: Choose Specific Search Terms**: For more relevant results, instead of broad terms like "AI," use specific phrases like "machine learning for healthcare" Optimize Scheduling**: When setting the monitoring frequency, consider your team's workflow. Running the scenario every 4 hours during working hours often provides a good balance between staying updated and avoiding notification fatigue. Set Up Error Handling**: Enable n8n's error output to alert you if the automation encounters any issues with accessing Product Hunt or sending messages to Slack. Regular Topic Review**: Schedule a monthly review of your monitored topics to ensure they're still relevant to your needs and adjust as necessary. What's Next? Now that you've set up your Product Hunt monitor automation, you might be interested in: Creating a similar monitor for other tech websites Setting up automated content curation for your team's newsletter Building a competitive intelligence dashboard using web monitoring Happy Automating!

This workflow will take an alert from Syncro, determine if it's an agent_offline_trigger type, then determine if it's a new alert or a close to an existing alert, and then submit it to OpsGenie. New alerts will create a new alert in OpsGenie and resolved alerts will close the alert in OpsGenie. It doesn't require any kind of Google Sheets because OpsGenie allows you to submit a unique ID (known as an alias) along with the alert, which can be referenced later when closing the alert. The trigger type can be changed to suit your needs. You will need to create an API integration in OpsGenie. In Syncro, in addition to setting up the appropriate notification to webhook, you will also need a script that closes the agent_offline_trigger alert and an automated remediation to trigger that script when the asset goes offline (the script is queued and run when the asset comes back online). > This workflow is part of an MSP collection, The original can be found here: https://github.com/bionemesis/n8nsyncro

This workflow is aimed at generating keywords for SEO and articles To get started, you need to use the workflow as it is. You just call the webhook URL with a query parameter as q={{ $keywords}} For example, you can call it using ?q=keyword research This will give you a list of keywords back as an array. This system can be used by SEO pros, content marketers and also social media marketers to generate relevant keywords for their user needs

Receive Webhook Notification The workflow starts when a webhook receives a POST request from Whapi, notifying that a new participant has joined a WhatsApp group. Filter the Event The workflow checks two conditions: The event is for the correct WhatsApp group (matching the specific group ID). The action type is "add" (meaning a user was added to the group). Send Welcome Message If both conditions are met, the workflow sends a personalized welcome message to the new participant via Whapi. The message explains the group rules and how the user can earn points and participate in weekly raffles. Create Airtable Record After sending the welcome message, the workflow creates a new record in the Airtable database for the new participant. The record includes: The participant’s WhatsApp ID An initial engagement count of 100 points The date of the last interaction (set to today) Result Every new group member is automatically welcomed and registered in your engagement database with starter points, ready to participate in your group’s activities and rewards. This workflow ensures new users are greeted, informed, and instantly included in your engagement tracking system.

The Agent Decisioner is a dynamic, AI-powered routing system that automatically selects the most appropriate large language model (LLM) to respond to a user's query based on the query’s content and purpose. This workflow ensures dynamic, optimized AI responses by intelligently routing queries to the best-suited model. Advantages 🔁 Automatic Model Routing:** Automatically selects the best model for the job, improving efficiency and relevance of responses. 🎯 Optimized Use of Resources:** Avoids overuse of expensive models like GPT-4 by routing simpler queries to lightweight models. 📚 Model-Aware Reasoning:** Uses detailed metadata about model capabilities (e.g., reasoning, coding, web search) for intelligent selection. 📥 Modular and Extendable:** Easy to integrate with other tools or expand by adding more models or custom decision logic. 👨‍💻 Ideal for RAG and Multi-Agent Systems:** Can serve as the brain behind more complex agent frameworks or Retrieval-Augmented Generation pipelines. How It Works Chat Trigger: The workflow starts when a user sends a message, triggering the Routing Agent. Model Selection: The AI Agent analyzes the query and selects the best-suited model from the available options (e.g., Claude 3.7 Sonnet for coding, Perplexity/Sonar for web searches, GPT-4o Mini for reasoning). Structured Output: The agent returns a JSON response with the user’s prompt and the chosen model. Execution: The selected model processes the query and generates a response, ensuring optimal performance for the task. Set Up Steps Configure Nodes: Chat Trigger: Set up the webhook to receive user messages. Routing Agent (AI Agent): Define the system message with model strengths and JSON output rules. OpenRouter Chat Model: Connect to OpenRouter for model access. Structured Output Parser: Ensure it validates the JSON response format (prompt + model). Execution Agent (AI Agent1): Configure it to forward the prompt to the selected model. Connect Nodes: Link the Chat Trigger to the Routing Agent. Connect the OpenRouter Chat Model and Output Parser to the Routing Agent. Route the parsed JSON to the Execution Agent, which uses the chosen model via OpenRouter Chat Model1. Credentials: Ensure OpenRouter API credentials are correctly set for both chat model nodes. Test & Deploy: Activate the workflow and test with sample queries to verify model selection logic. Adjust the routing rules if needed for better accuracy. Need help customizing? Contact me for consulting and support or add me on Linkedin.

🎯 Workflow Goal Still manually checking form responses in your inbox? What if every submission landed neatly in Airtable — and you got a clean Slack message instantly? That’s exactly what this workflow does. No code, no delay — just a smooth automation to keep your team in the loop: Tally → Airtable → Slack Build an automated flow that: receives Tally form submissions, cleans up the data into usable fields, stores the results in Airtable, and automatically notifies a Slack channel. Step 1 – Connect Tally to n8n What we’re setting up A Webhook node in POST mode. Technical Add a Webhook node. Set it to POST. Copy the generated URL. In Tally → Integrations → Webhooks → paste this URL. Submit a test response on your form to capture a sample structure. Step 2 – Clean the data After connecting Tally, you now receive raw data inside a fields[] array. Let’s convert that into something clean and structured. Goal Extract key info like Full Name, Email, Phone, etc. into simple keys. What we’re doing Add a Set node to remap and clean the fields. Technical Add a Set node right after the Webhook. Add new values (String type) manually: Name: Full Name → Value: {{$json"fields"["value"]}} Name: Email → Value: {{$json"fields"["value"]}} Name: Phone → Value: {{$json"fields"["value"]}} (Adapt the indexes based on your form structure.) Use the data preview in the Webhook node to check the correct order. Output You now get clean data like: { "Full Name": "Jane Doe", "Email": "jane@example.com", "Phone": "+123456789" } Step 3 – Send to Airtable ✅ Once the data is cleaned, let’s store it in Airtable automatically. Goal Create one new Airtable row for each form submission. What we’re setting up An Airtable – Create Record node. Technical Add an Airtable node. Authenticate or connect your API token. Choose the base and table. Map the fields: Name: {{$json["Full Name"]}} Email: {{$json["Email"]}} Phone: {{$json["Phone"]}} Output Each submission creates a clean new row in your Airtable table. Step 4 – Add a delay ⌛ After saving to Airtable, it’s a good idea to insert a short pause — this prevents actions like Slack messages from stacking too fast. Goal Wait a few seconds before sending a Slack notification. What we’re setting up A Wait node for X seconds. ✅ Technical Add a Wait node. Choose Wait for X minutes. Step 5 – Send a message to Slack 💬 Now that the record is stored, let’s send a Slack message to notify your team. Goal Automatically alert your team in Slack when someone fills the form. What we’re setting up A Slack – Send Message node. Technical Add a Slack node. Connect your account. Choose the target channel, like #leads. Use this message format: New lead received! Name: {{$json["Full Name"]}} Email: {{$json["Email"]}} Phone: {{$json["Phone"]}} Output Your Slack team is notified instantly, with all lead info in one clean message. Workflow Complete Your automation now looks like this: Tally → Clean → Airtable → Wait → Slack Every submission turns into clean data, gets saved in Airtable, and alerts your team on Slack — fully automated, no extra work.

> ⚠️ Disclaimer: This workflow uses Scrapeless and Claude AI via community nodes, which require n8n self-hosted to work properly. 🔁 How It Works This intelligent B2B lead generation workflow combines search automation, website crawling, AI analysis, and multi-channel output: It starts by using Scrapeless’s Deep Serp API to find company websites from targeted Google Search queries. Each result is then individually crawled using Scrapeless's Crawler module, retrieving key business information from pages like /about, /contact, /services. The raw web content is processed via a Code node to clean, extract, and prepare structured data. The cleaned data is passed to Claude Sonnet (Anthropic) which analyzes and qualifies the lead based on content richness, contact data, and relevance. A filter step ensures only high-quality leads (e.g. lead score ≥ 6) are kept. Sent via Discord webhook for real-time notification (can be replaced with Slack, email, or CRM tools). > 📌 The result is a fully automated system that finds, qualifies, and organizes B2B leads with high efficiency and minimal manual input. ✅ Pre-Conditions Before using this workflow, make sure you have: An n8n self-hosted instance A Scrapeless account and API key (get it here) An Anthropic Claude API key A configured Discord webhook URL (or alternative notification service) ⚙️ Workflow Overview Manual Trigger → Scrapeless Google Search → Item Lists → Scrapeless Crawler → Code (Data Cleaning) → Claude Sonnet → Code (Response Parser) → Filter → Discord Notification 🔨 Step-by-Step Breakdown Manual Trigger – For testing purposes (can be replaced with Cron or Webhook) Scrapeless Google Search – Queries target B2B topics via Scrapeless’s Deep SERP API Item Lists – Splits search results into individual items Scrapeless Crawler – Visits each company domain and scrapes structured content Code Node (Data Cleaner) – Extracts and formats content for LLM input Claude Sonnet (via HTTP Request) – Evaluates lead quality, relevance, and contact info Code Node (Parser) – Parses Claude’s JSON response IF Filter – Filters leads based on score threshold Discord Webhook – Sends formatted message with company info 🧩 Customization Guidance You can easily adjust the workflow to match your needs: Lead Criteria**: Modify the Claude prompt and scoring logic in the Code node Output Channels**: Replace the Discord webhook with Slack, Email, Airtable, or any CRM node Search Topics**: Change your query in the Scrapeless SERP node to find leads in different niches or countries Scoring Threshold**: Adjust the filter logic (lead_score >= 6) to match your quality tolerance 🧪 How to Use Insert your Scrapeless and Claude API credentials in the designated nodes Replace or configure the Discord webhook (or alternative outputs) Run the workflow manually (or schedule it) View qualified leads directly in your chosen notification channel 📦 Output Example Each qualified lead includes: 🏢 Company Name 🌐 Website ✉️ Email(s) 📞 Phone(s) 📍 Location 📈 Lead Score 📝 Summary of relevant content 👥 Ideal Users This workflow is perfect for: AI SaaS companies** targeting mid-market & enterprise leads Marketing agencies** looking for B2B-qualified leads Automation consultants** building scraping solutions No-code developers** working with n8n, Make, Pipedream Sales teams** needing enriched prospecting data