Workflow Templates

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

This workflow automates the process of summarizing or transcribing a WordPress article, converting the text into speech using Eleven Labs API, and uploading the resulting MP3 file back to WordPress. How It Works Trigger – The workflow starts manually when the user clicks “Test Workflow”. Retrieve Article – It fetches a WordPress article based on a given post ID. Summarize or Transcribe – An LLM (GPT-4o-mini) generates either: • A summary of the article, or • A full transcription, depending on the chosen prompt. Generate Speech – The processed text (summary or transcription) is converted into an MP3 audio file using Eleven Labs API. Upload MP3 to WordPress – The generated MP3 file is uploaded to WordPress. Update WordPress Post – The article is updated with an embedded audio player, allowing users to listen to the summary or transcription. Set Up Steps WordPress API Credentials • Configure your WordPress API credentials in n8n. Eleven Labs API Key • Obtain an API Key from Eleven Labs and configure it in n8n. Choose Between Summary or Transcription • Modify the AI prompt to either generate a summary or keep the full transcription. Test the Workflow • Run the workflow and ensure the MP3 file is correctly generated and uploaded. 💡 Customization Options • Modify the AI prompt to switch between a summary and a transcription. • Change the voice model in Eleven Labs for different speech styles. • Adjust output format to higher/lower quality MP3. 🚀 This automation improves content accessibility and engagement by allowing users to listen to a summarized or full version of the article. Phil | Inforeole

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.

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

🧠 Writes original, thought-provoking blog posts using AI 🕓 Runs every 12 hours automatically ✍️ Publishes directly to Ghost blog with title, tags, and SEO meta 🔧 Features Scheduled every 12 hours OpenAI generates a multi-part blog post with metadata Markdown-compatible output (no HTML) Automatically published to Ghost CMS using authenticated API (🔐 no hardcoded keys) Fully modular and general-purpose — edit prompt for any blog theme! ⚙️ Nodes Overview Step Node Type Purpose 1️⃣ Schedule Trigger Runs every 12 hours 2️⃣ OpenAI Generates blog post + meta info 3️⃣ Code Extracts content, title, meta, and tags 4️⃣ Code Formats content as Ghost mobiledoc payload 5️⃣ HTTP Request Publishes post to Ghost via Admin API 📝 OpenAI Prompt (Generalized) Write a high-quality blog post on a creative or thought-provoking topic. The tone should be engaging and immersive. Length: 2–4 paragraphs. Then add a brief paragraph offering an alternative perspective or logical counterpoint. Finally, generate: Blog post title Meta description 5 tags 🔐 Notes ✅ No hardcoded API keys 🛠️ Ghost Admin API credentials must be set using the Credential Manager 📌 Prompt and Ghost URL are both easily customizable

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 n8n workflow automatically monitors RSS feeds for the latest AI vulnerability news, extracts key threat details, and creates a corresponding Security Incident in ServiceNow for each item. Schedule Trigger – Runs at scheduled intervals to check for updates. RSS Read – Fetches the latest AI vulnerability entries from the RSS feed. Read URL Content – Retrieves the full article for detailed analysis. Information Extractor (OpenAI Chat Model) – Parses and summarizes critical security information. Split Out – Processes each vulnerability alert separately. Create Incident – Generates a ServiceNow Security Incident with the extracted details. Ideal for security teams to track and respond quickly to emerging AI-related threats without manual feed monitoring.

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.

Overview Build your own AI Prompt Hub inside n8n. This template lets ChatGPT automatically search your saved prompts in Notion using semantic embeddings from HuggingFace. Each time a user sends a message, the workflow finds the most relevant prompt based on meaning - not keywords. Perfect for developers who maintain dozens of prompts and want ChatGPT to pick the right one automatically. Key Features 🔍 Semantic Prompt Search - Finds the best prompt using HuggingFace embeddings 🧠 AI Agent Integration - ChatGPT automatically calls the prompt-search workflow 📚 Notion Prompt Database - Store unlimited prompts with auto-generated embeddings ⚡ Automatic Embedding Sync - Regenerates vectors when prompts change This template is ideal for: AI automations Prompt engineering DevOps and backend engineers who reuse prompts Teams managing large prompt libraries How it works The user sends any message to the ChatGPT interface The n8n AI Agent calls a sub-workflow that performs semantic search in Notion HuggingFace converts both the message and saved prompts into vector embeddings The workflow returns the most similar prompt, which ChatGPT can use automatically Setup Instructions (15–20 minutes) Import this template into your n8n instance Set credentials for Notion, OpenAI, and HuggingFace Create a Notion database with: Prompt (Text) Embeddings (Text) Checksum (Text) Paste your Notion database ID in: “Get All Prompts” “On Page Update” “On Page Create” “Get All Prompts for Search” Enable the workflow and open the URL from “When chat message received” to start chatting Type any request - the system will search for a matching prompt automatically Documentation & Demo Full documentation and examples: https://github.com/YahorDubrouski/ai-planner/blob/main/documentation/prompt-hub/README.md

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Generates relevant keywords and questions from a a customer profile. Keyword data is enriched from ahref and everything is stored in a Google Sheet. This is great for market and customer research. Understanding search intent for a well defined audience and gives relevant actionable data in a fraction of time that manual research takes. How it works We'll define a customer profile in the 'Data' node We use an OpenAI LLM to fetch relevant search intent as keywords and questions We use an SEO MCP server to fetch keyword data from ahref free tooling The fetched data is stored in the Google sheet Set up steps Copy Google Sheet template and add it in all Google Sheet nodes Make sure that n8n has read & write permissions for your Google sheet. Add your list of domains in the first column in the Google sheet Add MCP credentials for seo-mcp Add OpenAI API credentials