by EoCi - Mr.Eo
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.
by Yulia
This is an end-to-end workflow for creating a simple OpenAI Assistant. The whole process is done with n8n nodes and do not require any programming experience. The workflow is divided into three main steps: Step 1: Get a Google Drive File and Upload to OpenAI The workflow starts by retrieving a file from Google Drive using the "Get File" node. The example file used is a Music Festival document. The retrieved file is then uploaded to OpenAI using the "Upload File to OpenAI" node. Run this section only once. The file is stored persistently on the OpenAI side. Step 2: Set Up a New Assistant In this step, a new assistant is created using the "Create new Assistant" node. The assistant is given a name, description, and system prompt. The uploaded file from Step 1 is attached as a knowledge source for the assistant. Same as for Step 1, run this section only once. Step 3: Chat with the Assistant The "Chat Trigger" node initiates the conversation with the assistant. The "OpenAI Assistant" node handles the conversation, using the assistant created in Step 2. Step 4: Expand the Assistant This step provides resources for ideas on how to expand the Assistant's capabilities: Create a WhatsApp bot Create a simple Telegram bot Create a Telegram AI bot (YouTube video) By following this workflow, users can create their own AI-powered assistants using OpenAI's API and integrate them with various platforms like WhatsApp and Telegram.
by Yaron Been
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
by Yaron Been
Spuuntries Ilearnmate Icts AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the spuuntries/ilearnmate-icts 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 Optional Parameters seed** (integer, default: None): Seed for reproducibility of example generation and vector training. Set to 0 for random behavior. num_examples_per_side** (integer, default: 3): Number of descriptive examples to generate for each side of the contrast. More examples might lead to better vectors but will increase generation time. attributes_to_generate** (string, default: girly,modestly,verbose,happy): Comma-separated list of attributes for which to generate control vectors (e.g., 'girly,modestly,verbose,happy') 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: spuuntries/ilearnmate-icts API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
by Yaron Been
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
by Harshil Agrawal
This workflow demonstrates the use of the Split In Batches node and the Wait node to avoid API rate limits. Customer Datastore node: The workflow fetches data from the Customer Datastore node. Based on your use case, replace it with a relevant node. Split In Batches node: This node splits the items into a single item. Based on the API limit, you can configure the Batch Size. HTTP Request node: This node makes API calls to a placeholder URL. If the Split In Batches node returns 5 items, the HTTP Request node will make 5 different API calls. Wait node: This node will pause the workflow for the time you specify. On resume, the Split In Batches node gets executed node, and the next batch is processed. Replace Me (NoOp node): This node is optional. If you want to continue your workflow and process the items, replace this node with the corresponding node(s).
by Krzysztof Kuzara
Who is this for? This workflow is perfect for anyone looking to automate the process of replacing variables in Google Docs with data from form. What problem does this workflow solve? This workflow automates the process of filling Google Docs templates with data coming from n8n forms or other variables. It’s especially useful for generating documents like contracts, invoices, or reports quickly and efficiently without manual intervention. What does this workflow do? The workflow receives data from a form in n8n. It uses the form data to replace the corresponding variables (e.g., {{example_variable}}) in a Google Docs template. The document is then generated with the new values, ready for further use, such as sending or archiving. How to set up this workflow? Prepare the template: Create a Google Docs template with variables in the {{variable}} format that you want to replace with form data. Modify the variables in the n8n form: Make sure the form fields correspond to the variables you want to replace in the Google Docs template. Connect to Google Docs: Set up the connection to Google Docs in n8n using the appropriate authentication credentials. Test the workflow: Run the workflow to ensure that the form data correctly replaces the variables in the Google Docs template. How to customize this workflow to your needs? Change the data source: You can modify the form or other data sources (e.g., API) from which the replacement values will be fetched. Customize the Google Docs template: Adapt the template to include additional fields for replacement as needed. Integrate with other applications: You can expand the workflow to include actions like sending the generated document via email, saving it to Google Drive, or passing it to other systems.
by Yaron Been
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
by Friedemann Schuetz
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!
by Yang
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).
by Zacharia Kimotho
Create new Clickup Tasks from Slack commands This workflow aims to make it easy to create new tasks on Clickup from normal Slack messages using simple slack command. For example We can have a slack command as /newTask Set task to update new contacts on CRM and assign them to the sales team This will have an new task on Clickup with the same title and description on Clickup For most teams, getting tasks from Slack to Clickup involves manually entering the new tasks into Clickup. What if we could do this with a simple slash command? Step 1 The first step is to Create an endpoint URL for your slack command by creating an events API from the link [below] https://api.slack.com/apps/) STEP 2 Next step is defining the endpoint for your URL Create a new webhook endpoint from your n8n with a POST and paste the endpoint URL to your event API. This will send all slash commands associated with the Slash to the desired endpoint Step 3 Log on to slack API (https://api.slack.com/) and create an application. This is the one we use to run all automation and commands from Slack. Once your app is ready, navigate to the Slash Commands and create a new command This will include the command, the webhook URL and a description of what the slash command is all about Now that this is saved you can do a test by sending a demo task to your endpoint Once you have tested the webhook slash command is working with the webhook, create a new Clickup API that can be used to create new tasks in ClickUp This workflow creates a new task with the start dates on Clikup that can be assigned to the respective team members More details about the document setup can be found on this document below Happy Productivity
by CustomJS
This n8n template demonstrates how to convert HTML into a PDF, compress the generated PDF, and return it as a binary response using the PDF Toolkit from www.customjs.space. Notice Community nodes can only be installed on self-hosted instances of n8n. @custom-js/n8n-nodes-pdf-toolkit What this workflow does Convert** the requested HTML to PDF. Compress** the PDF file. Use** a Code node to handle URLs pointing to PDF files if they exceed 6MB. Compress** the PDF pages. Requirements Self-hosted** n8n instance CustomJS API key** for compressing PDF files. HTML** Data to convert PDF files Code node** for handling URL that indicates PDF file. Workflow Steps: Manual Trigger: Runs with user interaction. HTML to PDF: Request HTML Data Convert HTML to PDF Request PDF from URL. Compress Pages from PDF: Compress PDF as a binary file. Usage Get API key from customJS Sign up to customJS platform. Navigate to your profile page Press "Show" button to get API key Set Credentials for CustomJS API on n8n Copy and paste your API key generated from CustomJS here. Design workflow A Manual Trigger for starting workflow. HTTP Request Nodes for downloading PDF files. Code node for handling URL that indicates PDF file. Compress PDF files. You can replace logic for triggering and returning results. For example, you can trigger this workflow by calling a webhook and get a result as a response from webhook. Simply replace Manual Trigger and Write to Disk nodes.