Workflow Templates

Modular Hazard Analysis Workflow : Free Version Business Value Proposition Accelerates ISO 26262 compliance for automotive/industrial systems by automating safety analysis while maintaining rigorous audit standards. :chart_with_upwards_trend: Key Benefits Time Instant report generation vs. weeks of documentation for HAZOP Risk Mitigation Pre-validated templates reduce human error Quick guide Input a systems_description file to the workflow Provide an OPENAI_API_KEY to the chat model. You can also replace the chat model with the model of your interest. :play_or_pause_button: Running the Workflow Refer to the github repo to understand in detail about how the workflow can be used :email: Contact For collaboration proposals or security issues, contact me by Email. :warning: Validation & Limitations AI-Assisted Analysis Considerations | Advantage | Mitigation Strategy | Implementation Example | |-----------|---------------------|------------------------| | Rapid hazard identification | Human validation layer | Manual review nodes in workflow | | Consistent S/E/C scoring | Rule-based validation | ASIL-D → Redundancy check | | Edge case coverage | Cross-reference with historical data | Integration with incident databases |

Who is this for? This workflow is ideal for sales teams, marketers, and virtual assistants who manage outbound campaigns and want to improve their cold outreach personalization. It helps automate the research and writing process for each lead, saving time while improving quality. What problem is this workflow solving? Cold outreach often lacks personalization because manually reviewing each lead's website takes time. This workflow eliminates that bottleneck by using AI to auto-generate personalized icebreakers, summaries, and outreach emails based on a lead’s website—without human research. What this workflow does This n8n workflow runs on a schedule and pulls leads from Airtable who don't yet have an "Ice breaker" field filled out. For each lead, it does the following: Trigger: Scheduled daily via the Run Daily to Process New Leads node. Search Airtable: Finds leads in Airtable where the Ice breaker field is empty using the Search Cold Leads Without Icebreaker node. Split in Batches: Iterates through each lead one by one using Loop Through Each Lead. Rate Limiting: Waits briefly before each request using Wait Before Making Request to avoid rate limits. Scrape Website: Sends each lead’s website to Dumpling AI's /scrape endpoint via the Scrape Lead Website with Dumpling AI HTTP request. Generate AI Copy: Sends the scraped content to GPT-4o using the Generate Icebreaker, Summary & Email (GPT-4o) node. It asks the LLM to create: A short personalized icebreaker A 2–3 line website summary A short email body for cold outreach Save Results: Updates the original Airtable record with the generated content using the Save AI Output Back to Airtable node. Sticky Note: Provides an overview of the workflow and usage instructions for future editors or collaborators. This loop continues for all leads found, updating Airtable with fresh AI-generated outreach content. Integration Requirements Airtable (Personal Access Token) Dumpling AI API Key (Header Auth) OpenAI (GPT-4o)

Who is this template for ? Basically anyone involved in recurring recruiting processes and looking to save a considerable amount of time and energy (Talent acquisitions Managers, recruiting consultants, hiring managers, founders…etc) What it does : It takes a messy and raw transcript from an “intake meeting” between a recruiter and a Hiring manager and turns it into a clean and exhaustive brief + scorecard templates for each interview rounds It does it under 1 MINUTE while the usual “manual” process usually takes several hours How to customize this workflow to your needs Google doc is the default choice because it allows easy modification of the output, but you can choose to output this under any format and / or store it wherever you want I strongly suggest to choose one of the latest LLM models for better output quality Both LLM prompts can be revised to match your expectations better

Welcome to my Automated Image Metadata Tagging Workflow! DISCLAIMER: This workflow only works with self-hosted n8n instances! You have to install the n8n-nodes-exif-data Community Node! This workflow automatically analyzes the image content with the help of AI and writes it directly back into the image file as keywords. (https://n8n.io/workflows/2995).** This workflow has the following steps: Google Drive trigger (scan for new files added in a specific folder) Download the added image file Analyse the content of the image Merge Metadata and image file Write the Keywords into the Metadata (dc:subject/keywords) and create new image file Update the original file in the Google Drive folder The following accesses are required for the workflow: You have to install the n8n-nodes-exif-data Community Node** Google Drive: Documentation AI API access (e.g. via OpenAI, Anthropic, Google or Ollama) You can contact me via LinkedIn, if you have any questions: https://www.linkedin.com/in/friedemann-schuetz

Who is it for? If you are having a lot of meetings as a project manager, CFO, CTO, CEO or any other role that requires handling many meetings, AND you are working with people in different timezones, you may have noticed that it is not uncommon that daylight savings time change day may differ from timezone to timezone. This may be very troublesome at times. If DST change day differs between timezones, then you might need to adjust your meetings time accordingly. And this happens twice a year. So it's good to get notification beforehand (at least a day before). This automation will notify you if tomorrow you can expect DST in any zone you provide. How It Works? Script runs daily and loops through provided timezones Checks if there is DST change to or from the tomorrow (if you want to be notified sooner, just adjust number of days) If there is DST change, script provides you with Slack notification (replace with email if needed) How to set up? Add and/or edit timezones you want to monitor in "Timezones List" node Adjust "Calculate Tomorrow's Date" if you want to be notified sooner than 1 day before DST change Adjust "Send Notification on Upcoming Change" to set where on Slack you want to be notified And that's it. Hope that you won't miss any other meeting because of DST!

Lucataco Seed X Ppo Text Generator Description Seed-X-PPO-7B by ByteDance-Seed, a powerful series of open-source multilingual translation language models Overview This n8n workflow integrates with the Replicate API to use the lucataco/seed-x-ppo model. This powerful AI model can generate high-quality text 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 text** (string): Text to translate target_language** (string): Target language (e.g., 'Chinese', 'French', 'Spanish') Optional Parameters num_beams** (integer, default: 4): Number of beams for beam search max_length** (integer, default: 512): Maximum length of generated text source_language** (string, default: auto): Source language (use 'auto' for automatic detection) 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 text content Access the generated output from the final node API Reference Model: lucataco/seed-x-ppo API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of text generation parameters

This n8n workflow was developed to evaluate and categorize incoming leads based on certain criteria. The workflow is triggered by adding a new row in a Google Sheets document. The workflow uses the OpenAI node to process the lead information. The system query contains detailed qualification rules and the response format. The user message contains the data for the individual lead. The JSON response from the OpenAI node is then processed by the Edit Fields node to extract the response. This response is merged together with the original lead data by the Merge node. Finally, the Google Sheets node updates the original lead entry in the Google Sheets document with the qualification result ("qualified" or "not qualified") in a separate column. This allows for easy tracking and sorting of the qualified leads.

This workflow performs various Git operations. It starts with a manual trigger, sets the local repository path, decodes a file and then updates a file's content, adds, commits, and pushes changes to a GitHub repository, and finally pulls changes. The upper branch of the workflow retrieves a specific file ("README.md") from a GitHub repository ("git_push_article") owned by "teds-tech-talks." It then decodes the file's binary data into readable text using a code node. The decoded content is used to update the file by adding a timestamp and data. Finally, the modified file is pushed back to the repository using a GitHub node, completing the process of editing and updating the file directly via the workflow. This bottom branch of the workflow makes changes to a local Git repository. It starts by updating the "README.md" file with a timestamp and some content. Then, it adds the modified files, commits the changes with a message, and pushes them to a remote GitHub repository owned by "teds-tech-talks." Additionally, the workflow allows pulling changes from the remote repository into the local repository. The goal is to demonstrate how to perform various Git operations using n8n nodes, including adding, committing, pushing, and pulling changes.

This workflow demonstrates how to use currentRunIndex to get the running index. Function node: This node generates mock data for the workflow. Replace it with the node whose data you want to split into batches. SplitInBatches node: This node splits the data with the batch size equal to 1. Based on your use-case, set the value of the Batch Size. IF node: This node checks the running index. If the running index equals 5 the node returns true and breaks the loop. The node uses the expression {{$node["SplitInBatches"].context["currentRunIndex"];}}, which returns the running index. Set node: This node prints a message Loop Ended. Based on your use-case, connect the false output of the IF node to the input of the node you want to execute if the condition is false.

Ibm Granite Granite Speech 3.3 8b Text Generator Description Granite-speech-3.3-8b is a compact and efficient speech-language model, specifically designed for automatic speech recognition (ASR) and automatic speech translation (AST). Overview This n8n workflow integrates with the Replicate API to use the ibm-granite/granite-speech-3.3-8b model. This powerful AI model can generate high-quality text 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): Random seed. Leave blank to randomize the seed. audio** (array, default: None): Audio inputs for the model. top_k** (integer, default: 50): The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering). top_p** (number, default: 0.9): A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751). prompt** (string, default: ): User prompt to send to the model. max_tokens** (integer, default: 512): The maximum number of tokens the model should generate as output. min_tokens** (integer, default: 0): The minimum number of tokens the model should generate as output. temperature** (number, default: 0.6): The value used to modulate the next token probabilities. chat_template** (string, default: None): A template to format the prompt with. If not provided, the default prompt template will be used. system_prompt** (string, default: None): System prompt to send to the model.The chat template provides a good default. 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 text content Access the generated output from the final node API Reference Model: ibm-granite/granite-speech-3.3-8b API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of text generation parameters

Ndreca Hunyuan3d 2 Test AI Generator Description None Overview This n8n workflow integrates with the Replicate API to use the ndreca/hunyuan3d-2-test 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 image** (string): Input image for generating 3D shape Optional Parameters seed** (integer, default: 1234): Random seed for generation steps** (integer, default: 50): Number of inference steps num_chunks** (integer, default: 200000): Number of chunks for mesh generation max_facenum** (integer, default: 40000): Maximum number of faces for mesh generation guidance_scale** (number, default: 5.5): Guidance scale for generation octree_resolution** (string, default: 512): Octree resolution for mesh generation remove_background** (boolean, default: True): Whether to remove background from input 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: ndreca/hunyuan3d-2-test API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of other generation parameters

Tags*: Supply Chain, Logistics, Route Planning, Transportation, GPS API Context Hi! I’m Samir — a Supply Chain Engineer and Data Scientist based in Paris, and founder of LogiGreen Consulting. I help companies improve their logistics operations using data, AI, and automation to reduce costs and minimize environmental footprint. > Let’s use n8n to build smarter and greener transport operations! 📬 For business inquiries, you can add find me on LinkedIn Who is this template for? This workflow is designed for logistics and transport teams who want to automate distance and travel time calculations for truck shipments. Ideal for: Control tower dashboards Transport cost simulations Route optimization studies How does it work? This n8n workflow connects to a Google Sheet where you store city-to-city shipment lanes, and uses the OpenRouteService API to calculate: 📏 Distance (in meters) ⏱️ Travel time (in seconds) 🪪 Number of route steps Steps: ✅ Load departure/destination city coordinates from a Google Sheet 🔁 Loop through each record 🚚 Query OpenRouteService using the truck (driving-hgv) profile 🧾 Extract and store results: distance, duration, number of steps 📤 Update the Google Sheet with new values What do I need to get started? This workflow is beginner-friendly and requires: A Google Sheet with route pairs (departure and destination coordinates) A free OpenRouteService API key 👉 Get one here Next Steps 🗒️ Follow the sticky notes inside the workflow to: Select your sheet Plug in your API key Launch the flow! 🎥 Check the Tutorial 🚀 You can customize the workflow to: Add CO2 emission estimates for Sustainability Reporting Connect to your TMS via API or EDI This template was built using n8n v1.93.0 Submitted: June 1, 2025