Workflow Templates

This cutting-edge n8n automation is a powerful market research tool designed to continuously monitor and capture User-Generated Content (UGC) opportunities on Fiverr. By intelligently scraping, parsing, and logging gig data, this workflow provides: Automated Market Scanning: Daily scrapes of Fiverr UGC gigs Real-time market intelligence Consistent, hands-off data collection Intelligent Data Extraction: Parses complex HTML structures Captures key gig details Transforms unstructured web data into actionable insights Seamless Data Logging: Automatic Google Sheets integration Comprehensive gig marketplace tracking Historical data preservation Key Benefits 🤖 Full Automation: Continuous market research 💡 Smart Filtering: Detailed UGC gig insights 📊 Instant Reporting: Real-time market trends ⏱️ Time-Saving: Eliminate manual research Workflow Architecture 🔍 Stage 1: Automated Triggering Scheduled Scraping**: Daily gig discovery Precise Timing**: Configurable run intervals Consistent Monitoring**: Always-on market intelligence 🌐 Stage 2: Web Scraping HTTP Request**: Fetch Fiverr search results Dynamic Headers**: Bypass potential scraping restrictions Targeted Search**: UGC-specific gig discovery 🧩 Stage 3: Data Extraction HTML Parsing**: Extract critical gig information Structured Data Collection**: Gig Prices Seller Names Gig Titles Direct Gig URLs 📋 Stage 4: Data Logging Google Sheets Integration**: Automatic data storage Historical Tracking**: Build comprehensive gig databases Easy Analysis**: Spreadsheet-ready format Potential Use Cases Content Creators**: Market rate research Freelance Platforms**: Competitive intelligence Marketing Agencies**: UGC trend analysis Recruitment Specialists**: Talent pool mapping Business Strategists**: Market opportunity identification Setup Requirements Fiverr Search Configuration Targeted search keywords Specific UGC categories Web Scraping Preparation User-agent rotation strategy Potential proxy configuration Robust error handling Google Sheets Setup Connected Google account Prepared spreadsheet Appropriate sharing permissions n8n Installation Cloud or self-hosted instance Import workflow configuration Configure API credentials Future Enhancement Suggestions 🤖 AI-powered gig trend analysis 📊 Advanced data visualization 🔔 Real-time price change alerts 🧠 Machine learning market predictions 🌐 Multi-platform gig tracking Ethical Considerations Respect Fiverr's Terms of Service Implement responsible scraping practices Avoid overwhelming target websites Use data for legitimate research purposes Technical Recommendations Implement exponential backoff for requests Use randomized delays between scrapes Maintain flexible CSS selector strategies Consider rate limiting and IP rotation Connect With Me Ready to unlock market insights? 📧 Email: Yaron@nofluff.online 🎥 YouTube: @YaronBeen 💼 LinkedIn: Yaron Been Transform your market research with intelligent, automated workflows!

How it works This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, the official n8n documentation—and then build a chatbot to ask it questions. Think of it like this: instead of a general-knowledge AI, you're building an expert librarian. The workflow is split into two main parts: Part 1: Indexing the Knowledge (Building the Library) This is a one-time process you run manually. The workflow automatically scrapes all pages of the n8n documentation, breaks them down into small, digestible chunks, and uses an AI model to create a special numerical representation (an "embedding") for each chunk. These embeddings are then stored in n8n's built-in Simple Vector Store. This is like a librarian reading every book and creating a hyper-detailed index card for every paragraph. Important: This in-memory knowledge base is temporary. It will be erased if you restart your n8n instance, and you will need to run the indexing process again. Part 2: The AI Agent (The Expert Librarian) This is the chat interface. When you ask a question, the AI agent doesn't guess the answer. Instead, it uses your question to find the most relevant "index cards" (chunks) from the knowledge base it just built. It then feeds these specific, relevant chunks to a powerful language model (Gemini) with a strict instruction: "Answer the user's question using ONLY this information." This ensures the answers are accurate, factual, and grounded in your provided documents. Set up steps Setup time: 2 minutes (plus 15-20 minutes for indexing) This template uses n8n's built-in tools, removing the need for an external database. Follow these simple steps to get started. Configure Google AI Credentials: You will need a Google AI API key for the Gemini models. In your n8n workflow, go to any of the three Gemini nodes (e.g., Gemini 2.5 Flash). Click the Credential dropdown and select + Create New Credential. Enter your Gemini API key and save. Apply Credentials to All Nodes: Your new Google AI credential is now saved. Go to the other two Gemini nodes (Gemini Chunk Embedding and Gemini Query Embedding) and select your newly created credential from the dropdown list. Build the Knowledge Base: Find the Start Indexing manual trigger node at the top-left of the workflow. Click its "Execute workflow" button to start the indexing process. ⚠️ Be Patient: This will take 15-20 minutes as it scrapes and processes the entire n8n documentation. You only need to do this once per n8n session. If you restart n8n, you must run this step again. Chat with Your Expert Agent: Once the indexing is complete, Activate the entire workflow using the toggle at the top of the screen. Open the RAG Chatbot chat trigger node (bottom-left) and copy its Public URL. Open the URL in a new tab and start asking questions about n8n! For example: "How does the IF node work?" or "What is a sub-workflow?".

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Introduction Tired of spending time crafting the perfect AI prompt? This workflow takes your simple ideas like "write a blog post" and automatically transforms them into detailed, structured prompts that actually work. 🎯 What This Does Automatically converts simple user prompts like "write a blog post" into structured, professional AI prompts with metadata, variables, and clear instructions. Perfect for everybody, all industries and organizations who are wanting to eliminate prompt engineering works. 🔄 How It Works Google Sheets Trigger monitors for new prompts AI Enhancement Pipeline uses Gemini + Groq to add structure & context Field Completion auto-generates missing metadata (topic, categories) Quality Assurance validates & stores complete results 🚀 Setup Requirements AI APIs**: Gemini, Telegram, Groq API keys Google Sheets**: 2 sheets (Main, ModifiedPrompt) 5 minutes setup time** - detailed instructions in blue sticky notes Set up steps Setup time: < 5 minutes Create a Google Spreadsheet with two tabs (sheets): OriginalPrompts and ModifiedPrompts. OriginalPrompts columns: Original Prompt ID | Model | Original Prompt | Created Time ModifiedPrompts columns (example): Modified Prompt ID | Original Prompt ID | Topic | Topic Categories | Modified Prompt | Prompt Title | Prompt Type | Model Used | Improvement Notes | Updated Time | Created Time | isProcessed Add and attach credentials in n8n: Google Sheets OAuth2 (required for getting new prompt) Gemini and Groq API credentials (required for AI Agent) Telegram credential (required for notifications) Save & Activate the workflow. Add a test row to OriginalPrompts, for example: Original Prompt ID: 1 — Original Prompt: "Write a short blog post about AI ethics". Wait ~30–60s and check ModifiedPrompts for the enhanced output. That’s it ! Once it configured, drop short ideas into your sheet and get professional prompts back automatically. Your prompts get better, your AI outputs improve, and you save hours of manual prompt crafting.

1. How it Works This n8n workflow automates fine-tuning OpenAI models through these key steps: Manual Trigger**: Starts with the "When clicking ‘Test workflow’" event to initiate the process. Downloads a .jsonl file from Google Drive Upload to OpenAI**: Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune"). Create Fine-tuning Job**: Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with: { "training_file": "{{ $json.id }}", "model": "gpt-4o-mini-2024-07-18" } OpenAI automatically starts training the model based on the provided file. Interaction with the Trained Model**: An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages. 2. Set up Steps To configure the workflow: Prepare the Training File: Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples). Upload it to Google Drive and update the ID in the "Google Drive" node. Configure Credentials: Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api). OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes. Customize the Model: In the "OpenAI Chat Model" node, specify the name of your fine-tuned model (e.g., ft:gpt-4o-mini-...). Update the HTTP request body (Create Fine-tuning Job) if needed (e.g., a different base model). Start the Workflow: Use the manual trigger ("Test workflow") to begin the upload and training process. Test the model via the "Chat Trigger" (chat messages). Integrated Documentation: Follow the instructions in the Sticky Notes to: Properly format the .jsonl (Step 1). Monitor progress on OpenAI (Step 2, link: https://platform.openai.com/finetune/). Note: Ensure the .jsonl file adheres to OpenAI’s required structure and that credentials are valid.

🤖 Smart Interview Assistant: Tailored Questions Based on CV, JD, and Round Watch the demo video below: 📌 Who’s it for This workflow is designed for: Recruiters* and *Talent Acquisition Specialists** who want to automate candidate interview prep. Hiring Managers** conducting multiple interviews and needing personalized question sets. Technical Interviewers** who want to save time and be well-prepared with relevant questions. ⚙️ How it works / What it does The Smart Interview Assistant automates the interview preparation process in a few clicks: Accepts: Multiple resumes (PDFs) Selected job role Chosen interview round Extracts structured data from: The candidate’s CV The corresponding Job Description (JD) Uses GPT-4 to analyze: Candidate profile Role requirements Interview round context Generates: Tailored interview questions Expected answers A summarized interview prep report Sends the report directly to the hiring team via email (SMTP) 📁 Google Drive Structure 📂 Root Folder ├── 📁 jd/ # Stores all job descriptions in PDF format │ ├── Backend_Engineer.pdf │ ├── Azure_DevOps_Lead.pdf │ └── ... └── 📄 Positions (Google Sheet) # Maps Job Role ↔ JD File Link 📝 Sample Mapping Sheet: Positions Sheet Columns: Job Role Job Description File URL (pointing to PDF in jd/ folder) 🛠️ How to Set Up Step 1: Configure API Integrations ✅ Connect your OpenAI GPT-4 API Key ✅ Enable Google Cloud APIs: Google Sheets API (to read job roles) Google Drive API (to access CV and JD files) ✅ Set up SMTP credentials (for email delivery) Step 2: Prepare Google Drive & Mapping Sheet Create a root folder on Google Drive Inside the root folder: Create a folder named /jd/ and upload all job descriptions (PDFs) Create a Google Sheet named Positions with the following format: | Job Role | Job Description File URL | |-----------------------------|--------------------------------------------| | Azure DevOps Engineer | https://drive.google.com/xxx/jd1.pdf | | Full-Stack Developer (.NET) | https://drive.google.com/xxx/jd2.pdf | Step 3: Build the Application Form Use any form tool (e.g., Typeform, Tally, or custom HTML) that collects: 📎 Resume file (PDF) 🧾 Job Role (dropdown) 🔄 Interview Round (dropdown) Step 4: Resume & JD Extraction 🔍 Use Extract from PDF to parse the resume content 📄 Retrieve the JD link from the Positions sheet based on the selected Job Role 🔗 Use Download file to pull the PDF for processing Step 5: Analyze with GPT-4 Run both Resume and JD through a Profile Analyzer Agent (GPT-4 with JSON output) Merge results Add manual input or mapping for the Interview Round metadata Step 6: Generate Interview Report Use a second GPT-4 agent (e.g., HR Expert Agent) to: Generate 6–8 tailored interview questions Include expected answers and rationale Step 7: Deliver Final Report Format the content as: 📄 PDF (optional) 📨 Email body Send the report to the recruiter, hiring manager, or interviewer via SMTP ✅ Requirements 🔑 OpenAI GPT-4 API Key 📁 Google Drive (for resume and JD storage) 📊 Google Sheet (job role mapping) 📬 SMTP credentials (host, username, password) 🧰 n8n self-hosted or cloud instance with: PDF Parser Google Sheets node HTTP Download node Email node ✏️ How to Customize the Workflow | Part | Customization Options | |----------------------------|-------------------------------------------------------------| | Form UI | Modify the design, dropdown options, or input validations | | Job Description Source | Replace Google Sheet with Notion, Airtable, or database | | Interview Metadata | Add job level, region, or language preference | | AI Prompt Tuning | Adjust prompt phrasing or temperature in GPT nodes | | Report Format | Generate PDF instead of email body using PDF node | | Delivery Method | Add internal HR portal webhook or generate downloadable link |

Why If you need to use n8n to connect to service providers of yours, some of which happen to rely on firewall white-listing as part of their access control, you'll need to determine or verify the public IP addresses of your n8n instance(s). How does it work The webhook, upon invocation, will use Http Request node to request public IP address information from ++api.ipify.org++ in json format, for 10 times, then aggregate results to an array. The reason to repeat, is to get all the potential public IP addresses of your n8n instance. Often than not, enterprises or network providers deploy at least a pair of gateway devices at the border for redundancy. built-in array functions in a javascript expression are used to pluck all the values under 'ip' key, and to dedup to an array as response body. How to set it up import the workflow set up your own header-auth credential update the workflow to use the new credential test or activate workflow as usual. example invocation $ curl -H "api-key: super-long-api-token" http://localhost:5678/webhook-test/4879bc79-d6f8-48df-bfe4-613366c7f399 ["88.88.88.66", "88.88.88.88"]

Automated Binary Data Extraction from Gmail to Google Drive Folder This workflow is designed to automate the process of handling emails with binary attachments. It triggers when a new email arrives in a specified Gmail account (or can be configured with a similar email trigger) and is set to download any binary attachments. The workflow then filters the email to confirm it contains binary data (attachments). If attachments are present, it proceeds to retrieve the full email details, including all binary data. A crucial step is the creation of a new Google Drive folder. This folder is dynamically named using the email's subject and the current timestamp, for example, "[Email Subject] - [Current Timestamp]". Following this, the workflow separates each individual attachment from the email. Finally, these attachments are uploaded into the newly created Google Drive folder, with their original filenames preserved. The overall purpose of this workflow is to automatically organize and store email attachments into a structured Google Drive folder system. This workflow is compatible with any type of binary data found in an email, as the filter is designed to detect any binary data, not just PDFs. How It Works Trigger: The workflow initiates when a new email arrives in a specified Gmail account. Alternatively, it can be configured with a similar email trigger. Download Attachments: The workflow is set to automatically download any binary attachments from the incoming email. Filter Attachments: The workflow then filters the email to confirm it contains binary data (attachments). Retrieve Full Email Details: If attachments are present, the workflow proceeds to retrieve the complete details of the email, including all binary data. Create Google Drive Folder: A new folder is created in Google Drive. This folder is dynamically named using the email's subject and the current timestamp (e.g., "[Email Subject] - [Current Timestamp]"). Split Out Attachments: Each individual binary attachment from the email is separated into its own item within the workflow. Upload to Google Drive: Finally, these separated attachments are uploaded into the newly created Google Drive folder, retaining their original filenames. Need Help? Have Questions? For consulting and support, or if you have questions, please feel free to connect with me on LinkedIn or email michael.gullo@outlook.com.

Preventing Google Sheets Quota Errors during Batch Processing This template provides a robust solution for dealing with Google Sheets API rate limits. It is designed for workflows that update a large number of rows in a Google Sheet and frequently fail with "too many requests" errors. The template uses a Wait node connected to the error output of the Google Sheets node, creating a retry loop that delays execution for a set period before attempting the update again. To use this template, simply replace the placeholder Google Sheets nodes with your own credentials and sheet. You can find an example Google Sheet for this template here. For a full explanation of this approach, check out the blog post here.

Who is this for? This workflow is perfect for lead generation experts, digital marketers, SEO professionals, and virtual assistants who need to quickly collect local business information based on specific search terms without manually navigating Google Places. What problem is this workflow solving? Manually searching Google Places for business leads is time-consuming and inconsistent. This workflow automates the entire process using Dumpling AI’s Google Places search endpoint, helping users collect accurate and structured business data and log it into a Google Sheet automatically. What this workflow does This workflow runs daily at 1 PM. It starts by reading a list of business-related search terms from a Google Sheet (for example, “dentists in Dallas”). Each term is sent to Dumpling AI’s search-places endpoint, which returns local business listings from Google Places. The data is split, structured, and logged row-by-row in a connected Google Sheet. Nodes Overview Run Every Day at 1 PM A scheduled trigger that executes the workflow daily. Google Sheets (Input) – Fetch Search Terms from Sheet Pulls a list of search terms from a Google Sheet. Each term should describe a business category and location (e.g., “coffee shops in Atlanta”). HTTP Request – Scrape Google Places via Dumpling AI Sends each search term to Dumpling AI’s /search-places endpoint, returning data like business names, phone numbers, websites, ratings, and categories. Split In Batches – Split Places Result Breaks the list of businesses returned for each search term into individual items for processing. Google Sheets (Output) – Save Each Business to Sheet Saves the scraped data into a second Google Sheet. Each row contains: title address rating category phoneNumber website 📝 Notes You must set up Dumpling AI and generate your API key from: Dumpling AI You can change the run schedule in the schedule node to fit your needs (e.g., weekly or hourly).

Creativeathive Lemaar Door Mockedup AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the creativeathive/lemaar-door-mockedup model. This powerful AI model can generate high-quality other content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate other content Access the generated output from the final node API Reference Model: creativeathive/lemaar-door-mockedup API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Justingirard Draft Ui Designer Image Generator Description An experiment: a fine-tuned FLUX model for UI design generation Overview This n8n workflow integrates with the Replicate API to use the justingirard/draft-ui-designer model. This powerful AI model can generate high-quality image content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate image content Access the generated output from the final node API Reference Model: justingirard/draft-ui-designer API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Vcollos Trefilio AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the vcollos/trefilio model. This powerful AI model can generate high-quality other content based on your inputs. Features Easy integration with Replicate API Automated status checking and result retrieval Support for all model parameters Error handling and retry logic Clean output formatting Parameters Required Parameters prompt** (string): Prompt for generated image. If you include the trigger_word used in the training process you are more likely to activate the trained object, style, or concept in the resulting image. Optional Parameters mask** (string, default: None): Image mask for image inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. seed** (integer, default: None): Random seed. Set for reproducible generation image** (string, default: None): Input image for image to image or inpainting mode. If provided, aspect_ratio, width, and height inputs are ignored. model** (string, default: dev): Which model to run inference with. The dev model performs best with around 28 inference steps but the schnell model only needs 4 steps. width** (integer, default: None): Width of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation height** (integer, default: None): Height of generated image. Only works if aspect_ratio is set to custom. Will be rounded to nearest multiple of 16. Incompatible with fast generation go_fast** (boolean, default: False): Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16 extra_lora** (string, default: None): Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars' lora_scale** (number, default: 1): Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora. megapixels** (string, default: 1): Approximate number of megapixels for generated image How to Use Set up your Replicate API key in the workflow Configure the required parameters for your use case Run the workflow to generate other content Access the generated output from the final node API Reference Model: vcollos/trefilio API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters