Workflow Templates

This sub-workflow uses two custom Hugging Face regression models from Open Paws to evaluate and predict the real-world performance and advocacy alignment of text content. It’s designed to support animal advocacy organizations in optimizing their messaging across platforms like social media, email campaigns, and more. 🛠️ What It Does Sends input text to two deployed Hugging Face endpoints: Predicted Performance Model – Estimates real-world content success (e.g., engagement, shares, opens) based on patterns from real online data. Advocate Preference Model – Predicts how well the content will resonate with animal advocates (emotional impact, relevance, rationality, etc.) Outputs structured scores for both models Can be integrated into larger workflows for automated content review, filtering, or revision 📊 About the Models Text Performance Prediction Model** Trained on real-world data from 30+ animal advocacy organizations, this model predicts actual online performance of content—including social media, email marketing, and other outreach channels. Advocate Preference Prediction Model** Trained on ratings from animal advocates to evaluate how well a piece of text aligns with advocacy goals and values. Model Repositories: open-paws/text_performance_prediction_longform open-paws/animal_advocate_preference_prediction_longform > 📌 You must deploy each model as an inference endpoint on Hugging Face. Click "Deploy" on each model’s repo, then add the endpoint URL and your Hugging Face access token using n8n credentials. 📦 Use Cases Advocacy content review before publishing Automated scoring of outreach messages Filtering or flagging content with low predicted impact A/B testing support for message optimization

Luma Photon Flash Image Generator Description Accelerated variant of Photon prioritizing speed while maintaining quality Overview This n8n workflow integrates with the Replicate API to use the luma/photon-flash 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): Text prompt for image generation Optional Parameters seed** (integer, default: None): Random seed. Set for reproducible generation aspect_ratio** (string, default: 16:9): Aspect ratio of the generated image image_reference** (string, default: None): Reference image to guide generation style_reference** (string, default: None): Style reference image to guide generation character_reference** (string, default: None): Character reference image to guide generation image_reference_url** (string, default: None): Deprecated: Use image_reference instead style_reference_url** (string, default: None): Deprecated: Use style_reference instead image_reference_weight** (number, default: 0.85): Weight of the reference image. Larger values will make the reference image have a stronger influence on the generated image. style_reference_weight** (number, default: 0.85): Weight of the style reference image character_reference_url** (string, default: None): Deprecated: Use character_reference instead 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: luma/photon-flash API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Prunaai Flux Schnell Image Generator Description This is a 3x faster FLUX.1 [schnell] model from Black Forest Labs, optimised with pruna with minimal quality loss. Contact us for more at pruna.ai Overview This n8n workflow integrates with the Replicate API to use the prunaai/flux-schnell 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 Optional Parameters seed** (integer, default: None): Random seed. Set for reproducible generation megapixels** (string, default: 1): Approximate number of megapixels for generated image speed_mode** (string, default: Juiced 🔥 (default)): Run faster predictions with model optimized for speed num_outputs** (integer, default: 1): Number of outputs to generate aspect_ratio** (string, default: 1:1): Aspect ratio of the output image output_format** (string, default: jpg): Format of the output images output_quality** (integer, default: 80): Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs num_inference_steps** (integer, default: 4): Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster. 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: prunaai/flux-schnell API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

Prunaai Flux.1 Dev Image Generator Description This is the fastest Flux Dev endpoint in the world, contact us for more at pruna.ai Overview This n8n workflow integrates with the Replicate API to use the prunaai/flux.1-dev 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 Optional Parameters seed** (integer, default: -1): Seed guidance** (number, default: 3.5): Guidance scale image_size** (integer, default: 1024): Base image size (longest side) speed_mode** (string, default: Juiced 🔥 (default)): Speed optimization level aspect_ratio** (string, default: 1:1): Aspect ratio of the output image output_format** (string, default: jpg): Output format output_quality** (integer, default: 80): Output quality (for jpg and webp) num_inference_steps** (integer, default: 28): Number of inference steps 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: prunaai/flux.1-dev API Endpoint: https://api.replicate.com/v1/predictions Requirements Replicate API key n8n instance Basic understanding of image generation parameters

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 |

What is this? This is an n8n workflow designed to supercharge your Sonarr setup. Instead of just waiting for releases to appear in your RSS feed, this workflow proactively runs on a schedule, finds what's missing, actively searches for it, and grabs the best result based on your specific criteria. It's a "set it and forget it" solution to ensure your library is always complete. Key Features 🚀 Proactive Searching: Doesn't wait for content to come to you. It actively triggers a search for missing episodes. 🗓️ Fully Automated & Scheduled: Runs every 12 hours by default to check for anything new that's missing. 🧠 Smart & Efficient: Searches only once per season, even if multiple episodes from that season are missing, preventing unnecessary API calls. 🎯 Precise Release Filtering: It validates search results against the exact quality name and language you define before telling Sonarr to grab it. This gives you more control than standard quality profiles. ✅ Automatic Download: Once a valid release is found, it's automatically pushed to your download client via Sonarr. How It Works Trigger: The workflow starts automatically on a schedule. Fetch Missing: It connects to your Sonarr instance and gets a list of all monitored, "wanted" episodes. Filter & Group: It intelligently creates a unique list of seasons that need searching. Search: It loops through each unique season and tells Sonarr to perform an interactive search. Validate: It inspects the search results and only allows releases that match both the pre-defined quality AND language. Grab: If a perfect match is found, it sends a final command to Sonarr to grab that specific release and begin the download. How to Use This Template Import the JSON file into your n8n instance. Find the node named "info" (it's a "Set" node near the beginning). This is your main configuration area. Update the following values in the "info" node: urlSonar: Change http://192.168.31.204:8989 to your Sonarr's URL. apikey: Paste your Sonarr API key here. quality: Set the exact quality name you want to match (e.g., WEBDL-1080p). languages: Set the exact language name you want to match (e.g., English, Spanish). Activate the workflow. That's it! You can also change the schedule by editing the "Schedule Trigger" node.

This workflow automatically monitors the functionality of a factory. The workflow logs machine data coming from factory sensors in a CrateDB database, generates an incident report in PagerDuty, and notifies the responsible staff members when the temperature of a machine crosses the threshold value. This workflow builds on a workflow that generates factory data. Read more about this use case and how to build both workflows with step-by-step instructions in the blog post How to automate your factory's incident reporting. Prerequisites A PagerDuty account and credentials AMQP, an ActiveMQ  connection, and credentials A CrateDB instance running locally or on a server, and credentials. Nodes AMQP Trigger node starts the workflow. IF node filters sensor values higher than 50°C. PagerDuty node creates an incident in the account. Set nodes set the required incident information and sensor data, respectively. CrateDB nodes ingest the information data and machine sensor data, respectively. Function node converts degrees from Celsius to Fahrenheit.

Not sure what to eat tonight? Have recipes emailed to you daily based on your criterial. To run this workflow, you will need to have: A Recipe Search API key from Edamam An active email account with configured credentials To set up your credentials: Set your Edamam AppID and AppKey in the Search Criteria node Select (or create) your email credentials in the Send Recipes node (and set up the to: and from: email addresses while you are at it) To customize the recipes that you receive, open up the Search Criteria node and modify one or more of the following: RecipeCount** - the numner of recipes you would like to receive IngredientCount** - the maximum number of ingredients you would like each recipe to have CaloriesMin** - the minimum number of calories the recipe will have CaloriesMax** - the maximum number of calories the recipe will have TimeMin** - the minimum amount of time (in minutes) the recipe will take to prepare TimeMax** - the maximum amount of time (in minutes) the recipe will take to prepare Diet** - Select one of the following options: balanced - Protein/Fat/Carb values in 15/35/50 ratio high-fiber - More than 5g fiber per serving high-protein - More than 50% of total calories from proteins low-carb - Less than 20% of total calories from carbs low-fat - Less than 15% of total calories from fat low-sodium - Less than 140mg Na per serving random - selects a different random diet each day Health** - Select one of the following options: alcohol-free - No alcohol used or contained immuno-supportive - Recipes which fit a science-based approach to eating to strengthen the immune system celery-free - does not contain celery or derivatives crustacean-free - does not contain crustaceans (shrimp, lobster etc.) or derivatives dairy-free - No dairy; no lactose egg-free - No eggs or products containing eggs fish-free - No fish or fish derivatives fodmap-free - Does not contain FODMAP foods gluten-free - No ingredients containing gluten keto-friendly - Maximum 7 grams of net carbs per serving kidney-friendly - per serving – phosphorus less than 250 mg AND potassium less than 500 mg AND sodium: less than 500 mg kosher - contains only ingredients allowed by the kosher diet. However it does not guarantee kosher preparation of the ingredients themselves low-potassium - Less than 150mg per serving lupine-free - does not contain lupine or derivatives mustard-free - does not contain mustard or derivatives low-fat-abs - Less than 3g of fat per serving no-oil-added - No oil added except to what is contained in the basic ingredients low-sugar - No simple sugars – glucose, dextrose, galactose, fructose, sucrose, lactose, maltose paleo - Excludes what are perceived to be agricultural products; grains, legumes, dairy products, potatoes, refined salt, refined sugar, and processed oils peanut-free - No peanuts or products containing peanuts pecatarian - Does not contain meat or meat based products, can contain dairy and fish pork-free - does not contain pork or derivatives red-meat-free - does not contain beef, lamb, pork, duck, goose, game, horse, and other types of red meat or products containing red meat. sesame-free - does not contain sesame seed or derivatives shellfish-free - No shellfish or shellfish derivatives soy-free - No soy or products containing soy sugar-conscious - Less than 4g of sugar per serving tree-nut-free - No tree nuts or products containing tree nuts vegan - No meat, poultry, fish, dairy, eggs or honey vegetarian - No meat, poultry, or fish wheat-free - No wheat, can have gluten though random - selects a different random health option each day SearchItem* - the general term that you are looking for e.g. *chicken

If you have made some investments in cryptocurrency, this workflow will allow you to create an Airtable base that will update the value of your portfolio every hour. You can then track how well your investments are doing. You can check out my Airtable base to see how it works or even copy my base so that you can customize this workflow for yourself. To implement this workflow, you will need to update the Airtable nodes with your own credentials and make sure that they are pointing to your Airtable

This workflow demonstrates how to use noItemsLeft to check if there are items left to be processed by the SplitInBatches node. 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 check if all the data by the SplitInBatches are not processed or not. It uses the expression {{$node["SplitInBatches"].context["noItemsLeft"]}} which returns a boolean value. If there is data yet to be processed, the expression will return false, otherwise true. Set node: This node prints a message No Items Left. Based on your use-case, connect the false output of the IF node to the input of the node you want to execute, after the data is processed by the SplitInBatches node.

This workflow will allow you at the beginning of each day to copy your google calendar events into Trello so you can take notes, label, or automate your tasks. When deploying this, don't forget to change: Label ID for meeting type under "Create Trello Cards". You should be able to find instructions Here on how to find the label ID. Description for Trello cards under "Create Trello Cards". I currently pull in notes but it should be simple to change to pull the Gcal description instead. You can change the trigger time to fire at a different time.

This workflow is triggered when a typeform is submitted, then it saves the sender's information into HubSpot as a new contact. Typeform Trigger: triggers the workflow when a typeform is submitted. Set: sets the fields for the values from Typeform. HubSpot 1: creates a new contact with information from Typeform. IF: filters contacts who expressed their interest in business services. HubSpot 2: updates the contact's stage to opportunity. Gmail: sends an email to the opportunity contacts with informational material. NoOp: takes no action for contacts who are not interested.