Workflow Templates

Video Guide I prepared a detailed guide that showed the whole process of building a resume analyzer. Who is this for? This workflow is ideal for recruitment agencies, HR professionals, and hiring managers looking to automate the initial screening of CVs. It is especially useful for organizations handling large volumes of applications and seeking to streamline their recruitment process. What problem does this workflow solve? Manually screening resumes is time-consuming and prone to human error. This workflow automates the process, providing consistent and objective analysis of CVs against job descriptions. It helps filter out unsuitable candidates early, reducing workload and improving the overall efficiency of the recruitment process. What this workflow does This workflow automates the resume screening process using OpenAI for analysis. It provides a matching score, a summary of candidate suitability, and key insights into why the candidate fits (or doesn’t fit) the job. Retrieve Resume: The workflow downloads CVs from a direct link (e.g., Supabase storage or Dropbox). Extract Data: Extracts text data from PDF or DOC files for analysis. Analyze with OpenAI: Sends the extracted data and job description to OpenAI to: Generate a matching score. Summarize candidate strengths and weaknesses. Provide actionable insights into their suitability for the job. Setup Preparation Create Accounts: N8N: For workflow automation. OpenAI: For AI-powered CV analysis. Get CV Link: Upload CV files to Supabase storage or Dropbox to generate a direct link for processing. Prepare Artifacts for OpenAI: Define Metrics: Identify the metrics you want from the analysis (e.g., matching percentage, strengths, weaknesses). Generate JSON Schema: Use OpenAI to structure responses, ensuring compatibility with your database. Write a Prompt: Provide OpenAI with a clear and detailed prompt to ensure accurate analysis. N8N Scenario Download File: Fetch the CV using its direct URL. Extract Data: Use N8N’s PDF or text extraction nodes to retrieve text from the CV. Send to OpenAI: URL: POST to OpenAI’s API for analysis. Parameters: Include the extracted CV data and job description. Use JSON Schema to structure the response. Summary This workflow provides a seamless, automated solution for CV screening, helping recruitment agencies and HR teams save time while maintaining consistency in candidate evaluation. It enables organizations to focus on the most suitable candidates, improving the overall hiring process.

Note: This template only works for self-hosted n8n. This n8n template demonstrates how to use the Langchain code node to track token usage and cost for every LLM call. This is useful if your templates handle multiple clients or customers and you need a cheap and easy way to capture how much of your AI credits they are using. How it works In our mock AI service, we're offering a data conversion API to convert Resume PDFs into JSON documents. A form trigger is used to allow for PDF upload and the file is parsed using the Extract from File node. An Edit Fields node is used to capture additional variables to send to our log. Next, we use the Information Extractor node to organise the Resume data into the given JSON schema. The LLM subnode attached to the Information Extractor is a custom one we've built using the Langchain Code node. With our custom LLM subnode, we're able to capture the usage metadata using lifecycle hooks. We've also attached a Google Sheet tool to our LLM subnode, allowing us to send our usage metadata to a google sheet. Finally, we demonstrate how you can aggregate from the google sheet to understand how much AI tokens/costs your clients are liable for. Check out the example Client Usage Log - https://docs.google.com/spreadsheets/d/1AR5mrxz2S6PjAKVM0edNG-YVEc6zKL7aUxHxVcffnlw/edit?usp=sharing How to use SELF-HOSTED N8N ONLY** - the Langchain Code node is only available in the self-hosted version of n8n. It is not available in n8n cloud. The LLM subnode can only be attached to non-"AI agent" nodes; Basic LLM node, Information Extractor, Question & Answer Chain, Sentiment Analysis, Summarization Chain and Text Classifier. Requirements Self-hosted version of n8n OpenAI for LLM Google Sheets to store usage metadata Customising this template Bring the custom LLM subnode into your own templates! In many cases, it can be a drop-in replacement for the regular OpenAI subnode. Not using Google Sheets? Try other databases or a HTTP call to pipe into your CRM.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Fetch SEO and traffic information from ahref for a list of domains in a Google Sheet. This is great for marketing research and SEO workflow optimizations and saves tons of time. How it works We'll import domains from the Google sheet We use an SEO MCP server to fetch data from ahref free tooling The fetched data is stored in the Google sheet Set up steps Copy Google Sheet template and add it in all Google Sheet nodes Make sure that n8n has read & write permissions for your Google sheet. Add your list of domains in the first column in the Google sheet Add MCP credentials for seo-mcp

This n8n template shows you how to connect Github's Free Models to your existing n8n AI workflows. Whilst it is possible to use HTTP nodes to access Github Models, The aim of this template is to use it with existing n8n LLM nodes - saves the trouble of refactoring! Please note, Github states their model APIs are not intended for production usage! If you need higher rate limits, you'll need to use a paid service. How it works The approach builds a custom OpenAI compatible API around the Github Models API - all done in n8n! First, we attach an OpenAI subnode to our LLM node and configure a new OpenAI credential. Within this new OpenAI credential, we change the "Base URL" to point at a n8n webhook we've prepared as part of this template. Next, we create 2 webhooks which the LLM node will now attempt to connect with: "models" and "chat completion". The "models" webhook simply calls the Github Model's "list all models" endpoint and remaps the response to be compatible with our LLM node. The "Chat Completion" webhook does a similar task with Github's Chat Completion endpoint. How to use Once connected, just open chat and ask away! Any LLM or AI agent node connected with this custom LLM subnode will send requests to the Github Models API. Allowing your to try out a range of SOTA models for free. Requirements Github account and credentials for access to Models. If you've used the Github node previously, you can reuse this credential for this template. Customising this workflow This template is just an example. Use the custom OpenAI credential for your other workflows to test Github models. References https://docs.github.com/en/github-models/prototyping-with-ai-models https://docs.github.com/en/github-models

Reddit Poster to Discord This workflow checks Reddit every 15 minutes for new posts and sends selected posts to a Discord channel via webhook. Flow Overview: Schedule Trigger Runs every 15 minutes. Fetch Latest Posts Retrieves up to 3 new posts from any subreddit. Filter Posts Skips moderator or announcement posts based on author ID. Fetch Full Post Data Gets full details for the remaining post. Extract Image URL Parses the post to extract a direct image link. Send to Discord Sends the post title, image, and link to a Discord webhook. Setup Notes: Create a Reddit app and connect credentials in n8n. Add your subreddit name to both Reddit nodes. Connect a Discord webhook for posting.

🛠️Setup Guide 'Get OVH Invoices to Google Sheets' Author: Oliver Bardenheier Who is this for? This Workflow is for all users who have services (Domains, BareMetal, VPS, Cloud, etc.) with Provider OVH.com (European API) It automatically retrieves invoice data, -files and puts the Data in a Google Spreadsheet for further processing. What problem is this workflow solving? / use case Currently the invoices from OVH do not come as an attachment via mail, it is just a link. So, the receiver has to be logged in to the ovh account to download the file. Even more effort if one is using 2FA. This workflow retrieves all information through the oauth2 token. What this workflow does This Workflow automatically retrieves invoice data, -files from Your OVH.com account and puts the Data in a Google Spreadsheet for further processing. It also saves the invoice PDF to a certain (yearly) folder in Your Google Drive. Setup Make a copy of this Google Sheet Template Set the timeframe for the query to Your likings in "Query Latest OVH Invoices" You could set an email trigger before and make the frame only one day. Log into Your OVH Account and get Your Credentials here Authentication using oAuth2 Authorization Code "Login with OVHcloud SSO" You need to Authorize OVHcloud API console If this worked fine You'll see a green text: "Access Token Received" Head over to the OVH API Console to get Your Token. Set Up Header Auth in the HTTP nodes: Authentication = Generic Credential Type Generic Auth Type = Header Auth Header Auth = Your OVH Header Credentials: -- a.) In every API Call in the console You'll find a curl example, just take the data from the line including: -H "authorization: Bearer eyJhxxxxxxxxxxxxxxxxxxxxxxxxxxxxx......" -- b.) Create a new Credential in n8n for the header auth. Put in the 'name' Field: authorization Copy Your Token including Bearer in the value field: 'Bearer eyJhxxxxxxxxxxxxxxxxxxxxxxxxxxxxx......' How to customize this workflow to your needs You can put in a mail trigger that activates on every incoming invoice mail from OVH. Adjusting the timeframe to get invoices from a certain time period, or remove the time variables completely to get ALL invoices.

Use Case Track all Linear tickets in Google sheets. Useful if you want to do some custom analysis but don't want to pay for Linear's Plus features (Linear Insights) or that it does not cover. Setup Add Linear API header key Add Google sheets creds Update which teams to get tickets from in Graphql Nodes Update which Google Sheets page to write all the tickets to You only need to add one column, id, in the sheet. Google Sheets node in automatic mapping mode will handle adding the rest of the columns. Set any custom data on each ticket Activate workflow 🚀 How to adjust this template Set any custom fields you want to get out of this, that you can quickly do in n8n.

🤖 Telegram Bot Webhook Configuration Tool This workflow creates a simple web form that helps you configure Telegram bot webhooks quickly. Instead of manually constructing the Telegram API URL, this tool does it for you automatically. How It Works The workflow consists of three main steps: Form Input: A web form collects your bot token and webhook URL URL Construction: Automatically builds the correct Telegram API URL Redirect: Takes you directly to the Telegram API to complete the configuration What You Need Bot Token**: Get this from @BotFather on Telegram (format: 123456789:ABCdefGHIjklMNOpqrsTUVwxyz) Webhook URL**: Your n8n webhook endpoint (must be HTTPS) Setup Instructions Import this workflow into your n8n instance Activate the workflow Access the generated form URL Fill in your bot details and submit Form Fields | Field | Description | Example | |-------|-------------|---------| | Bot API Token | Token from BotFather | 123456789:ABCdefGHIjklMNOpqrsTUVwxyz | | Webhook URL | Your n8n webhook endpoint | https://your-instance.app.n8n.cloud/webhook/telegram | What Happens You enter your bot token and webhook URL in the form The workflow constructs this URL: https://api.telegram.org/bot{TOKEN}/setWebhook?url={WEBHOOK_URL} You're redirected to that URL where Telegram configures your webhook Telegram shows you a success or error message Benefits No Manual URL Building**: Eliminates copy-paste errors Quick Setup**: Configure webhooks in seconds Privacy Focused**: No data is stored anywhere Team Friendly**: Share the form URL with team members Common Webhook URLs n8n Cloud: https://your-instance.app.n8n.cloud/webhook/telegram-bot Self-hosted: https://your-domain.com/webhook/telegram-bot Requirements n8n with form trigger support Valid Telegram bot token Publicly accessible webhook URL (HTTPS required) Troubleshooting Invalid Token Error: Make sure you copied the complete token from BotFather Webhook Error: Ensure your URL is publicly accessible and uses HTTPS SSL Error: Verify your webhook URL has a valid SSL certificate This tool simply automates the manual process of visiting the Telegram API URL to configure your bot's webhook. Perfect for developers who frequently set up or change Telegram bot configurations.

Recursive Web Scraping Use Case Automating web scraping with recursive depth is ideal for collecting content across multiple linked pages—perfect for content aggregation, lead generation, or research projects. What This Automation Does This automation reads a list of URLs from a Google Sheet, scrapes each page, stores the content in a document, and adds newly discovered links back to the sheet. It continues this process for a specified number of iterations based on the defined scraping depth. Input Parameters: Seed URL: The starting URL to begin the scraping process. Example: https://example.com/ Links must contain: Restricts the links to those that contain this specified string. Example: https://example.com/ Depth: The number of iterations (layers of links) to scrape beyond the initial set. Example: 3 How It Works Starts by reading the Seed URL from the Google Sheet. Scrapes each page and saves its content to the specified document. Extracts new links from each page that match the Links must contain string, appends them to the Google Sheet. Repeats steps 2–3 for the number of times specified by Depth - 1. Setup Requirements Airtop API Key — free to generate. Credentials set up for Google Docs (requires creating a project on Google Console). Read how to. Credentials set up for Google Spreadsheet. Next Steps Add Filtering Rules**: Filter which links to follow based on domain, path, or content type. Combine with Scheduler**: Run this automation on a schedule to continuously explore newly discovered pages. Export Structured Data**: Extend the process to store extracted data in a CSV or database for analysis. Read more about website scraping for LLMS

Supabase Storage File Upload Workflow works with selfhosted Supabase ℹ️ How it works • Accepts file data (MIME type, filename, base64 content) from other workflows • Automatically routes files to appropriate storage buckets based on file type (images, audio, video, documents) • Uploads files to Supabase Storage using the REST API • Generates secure signed URLs for file access with 30-day expiration • Returns structured success/error responses for downstream processing 🏗️ Set up steps • Configure Supabase API credentials in n8n • Create storage buckets in your Supabase project (image-files, audio-files, video-files, document-files) (or choose your own structuring system) • Replace url paths with your own • Test the workflow using the included form trigger • Remove test form and integrate with your main workflows 📚 Reference: Supabase Storage Documentation

Who this is for? Extract & Summarize Yelp Business Review is an automated workflow that extracts the Yelp business reviews using Bright Data Web Unlocker, process and formats the raw data, summarizes using the Google Gemini's LLM, and forward the concise summary with the review respose to a specified webhook endpoint. This workflow is tailored for: Local SEO Specialists who need structured insights from Yelp reviews to optimize listings. Business Owners wanting quick summaries of what customers love or complain about. Reputation Managers who monitor brand sentiment and identify customer pain points. Data Analysts & Researchers extracting Yelp review patterns at scale. AI Product Builders needing clean Yelp review data as input for their LLMs or recommender systems. What problem is this workflow solving? Yelp reviews are rich in customer sentiment but messy to work with manually. This workflow solves: The pain of scraping Yelp review content manually. The challenge of building the structured data with the summary. The need for structured outputs suitable for analysis, reports, or AI input. What this workflow does This automated pipeline does the following: Bright Data Integration**: Queries Yelp and scrapes business listing data using Bright Data's Web Unlocker. Structured Data Formatting**: Formats the Yelp review data to a structured response in JSON format. Google Gemini Summarization**: Sends the cleaned reviews to Google Gemini to: Output Delivery**: Returns the structured response with the concise summary over the webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Yelp Business Review URL with the Bright Data zone by navigating to the Set Yelp URL with the Bright Data Zone node. Update the Webhook Notifier for the merged response node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you’re a market researcher, entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Target Specific Business Categories** Update the Yelp Business Review input to scrape different businesses like gyms, salons etc. Limit Reviews** Add filters by description, location, page range to get the top reviews. Tweak the Data Extraction Node** Update the Structured Data Extractor node Output Parser for building the JSON response with the appropriate fields or attributes. Tweak the Summarization Prompt** Modify the Gemini prompt to generate a comprehensive summary. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints

Use Case When trying to maximize your outreach, website visitors are often an overlooked source of qualified new leads. This workflow allows your to track and enrich new website visitors and saves them to a Google Sheet once they meet a pre-defined criteria. What this workflow does This workflow fires once a day and gets all your leads saved in Leadfeeder. It then takes the leads that meet a pre-defined engagement criteria, e.g. that they visited your site 3 times, and enriches them additionally with Clearbit. From there it filters the leads again by a criteria on the company, e.g. a minimum employee count, and saves matching leads into a Google Sheet document. Setup Add your Leedfeeder credentials. The name should be Authorization and the value Token token=yourapitoken. You can find your token via Settings -> Personal -> API-Token Add your Google Sheet credentials Save the Leedfeeder account names you want to use in the Setup node Copy the Google Sheets Template and add its URL to the Setup node How to adjust this to your needs Adjust and/or remove the engagement and company criteria Add more ways to enrich a company Potential ideas to enhance the use of this workflow Automatically reach out to users that meet the criteria / that get added to the sheet Create a workflow that finds the right employee in companies that are identified by this workflow