🤖 Automate CV screening with AI candidate analysis
How it works This workflow automates your initial hiring pipeline by creating an AI-powered CV scanner. It collects job applications through a web form, uses AI to analyze the candidate's CV against your job description, and neatly organizes the results in a Google Sheet. Here’s the step-by-step process: The Application Form: A Form Trigger provides a public web form for candidates to submit their name, email, and CV (as a PDF). Initial Logging: As soon as an application is submitted, the candidate's name and email are added to a Google Sheet. This ensures every applicant is logged, even if a later step fails. CV Text Extraction: The workflow uses Mistral's OCR model to accurately extract all the text from the uploaded CV PDF. AI Analysis: The extracted text is sent to Google Gemini. A detailed prompt instructs the AI to act as a hiring assistant, scoring the CV against the specific requirements of your job role and providing a detailed explanation for its score. Structured Output: A JSON Output Parser ensures the AI's analysis is returned in a clean, structured format, making the data reliable. Final Record: The AI-generated qualification score and explanation are added to the candidate's row in the Google Sheet, giving you a complete, analyzed list of applicants. Set up steps Setup time: ~15 minutes You'll need API keys for Mistral and Google AI, and to connect your Google account. Get Your Mistral API Key: Visit the Mistral Platform at console.mistral.ai/api-keys. Create and copy your API key. In the workflow, go to the Extract CV Text node, click the Credential dropdown, and select + Create New Credential. Paste your key into the API Key field and Save. Get Your Google AI API Key: Visit Google AI Studio at aistudio.google.com/app/apikey. Click "Create API key in new project" and copy the key. In the workflow, go to the Gemini 2.5 Flash Lite node, click the Credential dropdown, and select + Create New Credential. Paste your key into the API Key field and Save. Connect Your Google Account: Select the Create 'CVs' Spreadsheet node. Click the Credential dropdown and select + Create New Credential to connect your Google account. Repeat this for the Log Candidate Submission and Add CV Analysis nodes, selecting the credential you just created. Create Your Spreadsheet: Click the "play" icon on the Start Here node to run it. This will create a new Google Sheet in your Google Drive named "CVs" with the correct columns. Customize the Job Role: Go to the AI Qualification node. In the Text parameter, find the job_requirements section and replace the example job description with your own. Be as detailed as possible for the best results. Start Screening! Activate the workflow using the toggle at the top right. Go to the Application Form node and click the "Open Form URL" button. Fill out the form with a test application and upload a sample CV. Check your Google Sheet to see the AI's analysis appear within moments
By Lucas PeyrinCreate Atlassian Confluence page from template
How it works creates a new page in Confluence based on a page template also defined in Confluence replaces any number of placeholders with data from your workflow generic implementation for maximum flexibility Set up steps All parameters you need to change are defined in the Set node Set your Atlassian-domain Set the template id you want to use as the basis for new pages Set the target space and parent page for new pages added based on that template. 🎥 Explainer video has all the details. =) Feedback Any feedback is welcome. If you have ideas for improvements, let me know.
By Jon BungartzGenerate text images from the Free DummyJSON API using the HTTP request node
Who is this for? This workflow template is ideal for marketers, designers, content creators, and developers who need to generate custom text-based images dynamically. Whether you want to create social media graphics, placeholder images, or text-based LinkedIn carousels, this workflow provides a simple, no-code solution using an API that requires no authentication. What problem does this workflow solve? Creating text-based images often requires design software or complex integrations with graphic tools. This workflow eliminates that hassle by allowing users to generate images with custom text, font styles, colors, and background colors using a simple HTTP request. It’s perfect for automating image generation without relying on external tools or manual effort. What this workflow does This workflow leverages an HTTP request to a free API that generates text-based images dynamically. Here's what it enables you to do: Define custom image text Set image dimensions (width x height) Choose a background color and text color using hex codes Select a font family and font size Specify the image format (PNG, JPG, or WebP) The generated image can be used immediately, making it ideal for automating content creation workflows. Setup Open the workflow in n8n. Modify the Set node to define your preferred image properties: text: The message displayed on the image. size: Image dimensions (e.g., 500x300 pixels). backgroundColor: Hex color code for the background. textColor: Hex color code for the text. fontFamily: Select from available font options (e.g., Pacifico, Ubuntu). fontSize: Define the text size. type: Choose the image format (PNG, JPG, or WebP). Execute the workflow to generate an image. The HTTP request returns the generated image, ready for use. How to customize this workflow Adjust the Set node values to match your desired design. Use dynamic data for text, allowing personalized images based on user input. Automate image delivery by adding email or social media posting nodes. Integrate this workflow into larger automation sequences, such as content marketing pipelines.
By Akram KadriMulti-format document processing for RAG chatbot with Google Drive & Supabase
This n8n workflow is the data ingestion pipeline for the "RAG System V2" chatbot. It automatically monitors a specific Google Drive folder for new files, processes them based on their type, and inserts their content into a Supabase vector database to make it searchable for the RAG agent. Key Features & Workflow: Google Drive Trigger: The workflow starts automatically when a new file is created in a designated folder (named "DOCUMENTS" in this template). Smart File Handling: A Switch node routes the file based on its MIME type (e.g., PDF, Excel, Google Doc, Word Doc) for correct processing. Multi-Format Extraction: PDF: Text is extracted directly using the Extract PDF Text node. Google Docs: Files are downloaded and converted to plain text (text/plain) and processed by the Extract from Text File node. Excel: Data is extracted, aggregated, and concatenated into a single text block for embedding. Word (.doc/.docx): Word files are automatically converted into Google Docs format using an HTTP Request. This newly created Google Doc will then trigger the entire workflow again, ensuring it's processed correctly. Chunking & Metadata Enrichment: The extracted text is split into manageable chunks using the Recursive Character Text Splitter (set to 2000-character chunks). The Enhanced Default Data Loader then enriches these chunks with crucial metadata from the original file, such as filename, creator, and createdat. Vectorization & Storage: Finally, the workflow uses OpenAI Embeddings to create vector representations of the text chunks and inserts them into the Supabase Vector Store.
By edisantosa