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.
by Yaron Been
Adamantiamable Lumi AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the adamantiamable/lumi 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: adamantiamable/lumi API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
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 Tom
This workflow shows a low code approach to parsing an XML file and storing its contents in a Google Sheets spreadsheet. To run the workflow: Make sure you are running n8n 0.197 or newer Have n8n authenticated with Google Sheets How it's done: This workflow first downloads an example file using the HTTP Request node and reads this file using the XML node. It then runs the Item Lists node to split out the individual food items from the example file. It then splits up the workflow into a separate branch creating a new spreadsheet file using the Google Sheets node. To read the column names we're using the Object.keys() method inside a Set node. Once the spreadsheet is created (the workflow waits for this using the Merge node), the data is appended to the newly created sheet (again using the Google Sheets node).
by Trung Tran
CV Extractor: Google Drive to Sheet + Slack Update for Recruiters Watch the demo video below: > This workflow automatically processes resumes (PDFs) uploaded or updated in a Google Drive folder. It extracts and structures the candidate’s information using AI, then updates or inserts the data into a Google Sheet, acting as a central talent database. Finally, it notifies the hiring team via Slack with a summary. Perfect for HR and TA teams, this automation eliminates the repetitive task of manually copying candidate details from CVs into spreadsheets, saving hours of admin work every week and keeping your hiring pipeline clean, fast, and up to date. 👤 Who’s it for This workflow is designed for: Recruiters* and *HR coordinators** who manage candidate profiles via Google Drive. Talent Acquisition teams** who want to automate CV parsing, enrichment, and database updating. Companies or hiring agencies** using spreadsheets for candidate tracking and CRM-like HR ops. ⚙️ How it works / What it does This smart and fully automated workflow: Monitors a Google Drive folder for any uploaded or updated resumes (PDFs). Downloads and extracts resume content using PDF parsing. Sends the raw text to GPT-4, which returns a structured profile (name, title, experience, skills, etc.). Verifies the profile and transforms it into a clean, row-based format. Upserts the candidate profile into a Google Sheet (insert or update by email). Notifies the hiring team in Slack or email that a profile was added or updated. This is a no-touch pipeline to keep your candidate data clean, current, and centralized. 🛠️ How to set up Step 1: Prepare your Google Drive folder Create a folder like /SmartHR/cv/ Upload sample resumes in .pdf format Step 2: Create your Google Sheet Columns to include: Email, FullName, JobTitle, Phone, Location, Experience, Education, Skills, etc. Optional: Add conditional formatting to highlight updates Step 3: Connect the n8n workflow Use the Google Drive Trigger: fileCreated → new profile uploaded fileUpdated → existing profile modified Use Google Drive (Download file) to fetch the resume Use Extract From PDF to get raw content Step 4: Configure GPT-4 node Use the structured system prompt to extract profile information Use json parser node to ensure safe formatting for next steps Step 5: Transform & Save Use a Function node to map fields to Google Sheet columns Use Append or update row (based on email as unique key) Optionally send Slack or email message to notify hiring team ✅ Requirements 🔑 OpenAI GPT-4 API key 🟩 n8n Cloud or Self-hosted with: Google Drive integration Google Sheets integration Email/Slack credentials (optional) 📄 Resume files in readable PDF format 📊 Google Sheet prepared with relevant headers ✏️ How to customize the workflow | Part | Customization Options | |----------------------------|----------------------------------------------------------------------------------------| | GPT Prompt | Tune for different job levels or fields (e.g., engineers vs marketers) | | Field Mapping | Update transform node to include other profile fields (LinkedIn, portfolio, etc.) | | Notification | Switch to Microsoft Teams, Telegram, or email alerts instead of Slack | | Data Store | Replace Google Sheet with Airtable, Notion, or database system | | Trigger Source | Trigger from email attachments or webhook instead of Google Drive if needed | | Output Format | Generate PDF profile cards or summary documents using HTML → PDF node |
by Zacharia Kimotho
This is an example of how we can build a slack bot in a few easy steps Before you can start, you need to o a few things Create a copy of this workflow Create a slack bot Create a slash command on slack and paste the webhook url to the slack command Note Make sure to configure this webhook using a https:// wrapper and don't use the default http://localhost:5678 as that will not be recognized by your slack webhook. Once the data has been sent to your webhook, the next step will be passing it via an AI Agent to process data based on the queries we pass to our agent. To have some sort of a memory, be sure to set the slack token to the memory node. This way you can refer to other chats from the history. The final message is relayed back to slack as a new message. Since we can not wait longer than 3000 ms for slack response, we will create a new message with reference to the input we passed. We can advance this using the tools or data sources for it to be more custom tailored for your company. Usage To use the slackbot, go to slack and click on your set slash command eg /Bob and send your desired message. This will send the message to your endpoint and get return the processed results as the message. If you would like help setting this up, feel free to reach out to zacharia@effibotics.com
by Yaron Been
Izzaanel Betia AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the izzaanel/betia 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: izzaanel/betia API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
by Yaron Been
Settyan Flash V2.0.1 Beta.10 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the settyan/flash-v2.0.1-beta.10 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: settyan/flash-v2.0.1-beta.10 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
by Yaron Been
Settyan Flash V2.0.0 Beta.10 AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the settyan/flash-v2.0.0-beta.10 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: settyan/flash-v2.0.0-beta.10 API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
by Yaron Been
Barbacoaexpert1 Ai Haircuts AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the barbacoaexpert1/ai-haircuts 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: barbacoaexpert1/ai-haircuts API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
by Yaron Been
Monexia Nietgoed AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the monexia/nietgoed 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: monexia/nietgoed API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters
by Harshil Agrawal
This example workflow demonstrates how to handle pagination. This example assumes that the API you are making the request to has pagination, and returns a cursor (something that points to the next page). This example workflow makes a request to the HubSpot API to fetch contacts. You will have to modify the parameters based on your API. Config URL node: This node sets the URL that the HTTP Request node calls. HTTP Request node: This node makes the API call and returns the data from the API. Based on your API, you will have to modify the parameters of the node. NoOp node and Wait node: These nodes help me avoiding any rate limits. If you're API has rate limits, make sure you configure the correct time in the Wait node. Check if pagination: This IF node checks if the API returns any cursor. If the API doesn't return any cursor, it means that there is no data to be fetched, and the node returns false. If the API returns a cursor, it means that there is still some data that needs to be fetched. In this case, the node returns true. Set next URL: This Set node is used to set the URL. In the next cycle, the HTTP Request node makes a call to this URL. Combine all data: This node combines all the data that gets returned by the API calls from the HTTP Request node.