Generate & publish SEO articles with Claude AI, Webflow & image generation
Content engine that ships fresh, SEO-ready articles every single day. Workflow: ⸻ Layout Blueprint • Purpose: Define content structure before writing begins. • What’s Included: • Search intent mapping • Internal link planning • Call-to-action (CTA) placement • Benefit: Ensures consistency, SEO alignment, and content goals are baked in early. ⸻ AI-Assisted Drafting • Tool: GPT generates the first draft. • Editor’s Role: • Focus on depth and accuracy • Align tone and style with existing site content • Context-Aware: Pulls insights from top-ranking articles already live on the site. ⸻ SEO Validation • Automated Checks for: • Keyword coverage • Readability scoring • Schema markup • Internal/external link quality • Outcome: Each piece is validated before hitting publish. ⸻ Media Production • Process: AI auto-generates relevant images. • Delivery: Visual assets are automatically added to the CMS library. ⸻ Optional Human Review: Team feedback via Slack or Teams if needed. ⸻ Automated Publishing • Action: Instantly publishes content to Webflow once approved. • Result: A fully streamlined pipeline from draft to live with minimal manual steps.
By MarkoNatural language Google Sheets data analysis with Gemini AI
This n8n workflow template creates an efficient data analysis system that uses Google Gemini AI to interpret user questions about spreadsheet data and processes them through a specialized sub-workflow for optimized token usage and faster responses. What This Workflow Does Smart Query Parsing: Uses Gemini AI to understand natural language questions about your data Efficient Processing: Routes calculations through a dedicated sub-workflow to minimize token consumption Structured Output: Automatically identifies the column, aggregation type, and grouping levels from user queries Multiple Aggregation Types: Supports sum, average, count, count distinct, min, and max operations Flexible Grouping: Can aggregate data by single or multiple dimensions Token Optimization: Processes large datasets without overwhelming AI context limits Tools Used Google Gemini Chat Model - Natural language query understanding and response formatting Google Sheets Tool - Data access and column metadata extraction Execute Workflow - Sub-workflow processing for data calculations Structured Output Parser - Converts AI responses to actionable parameters Memory Buffer Window - Basic conversation context management Switch Node - Routes to appropriate aggregation method Summarize Nodes - Performs various data aggregations 📋 MAIN WORKFLOW - Query Parser What This Workflow Does The main workflow receives natural language questions from users and converts them into structured parameters that the sub-workflow can process. It uses Google Gemini AI to understand the intent and extract the necessary information. Prerequisites for Main Workflow Google Cloud Platform account with Gemini API access Google account with access to Google Sheets n8n instance (cloud or self-hosted) Main Workflow Setup Instructions Import the Main Workflow Copy the main workflow JSON provided In your n8n instance, go to Workflows → Import from JSON Paste the JSON and click Import Save with name: "Gemini Data Query Parser" Set Up Google Gemini Connection Go to Google AI Studio Sign in with your Google account Go to Get API Key section Create a new API key or use an existing one Copy the API key Configure in n8n: Click on Google Gemini Chat Model node Click Create New Credential Select Google PaLM API Paste your API key Save the credential Set Up Google Sheets Connection for Main Workflow Go to Google Cloud Console Create a new project or select existing one Enable the Google Sheets API Create OAuth 2.0 Client ID credentials In n8n, click on Get Column Info node Create Google Sheets OAuth2 API credential Complete OAuth flow Configure Your Data Source Option A: Use Sample Data The workflow is pre-configured for: Sample Marketing Data Make a copy to your Google Drive Option B: Use Your Own Sheet Update Get Column Info node with your Sheet ID Ensure you have a "Columns" sheet for metadata Update sheet references as needed Set Up Workflow Trigger Configure how you want to trigger this workflow (webhook, manual, etc.) The workflow will output structured JSON for the sub-workflow --- ⚙️ SUB-WORKFLOW - Data Processor What This Workflow Does The sub-workflow receives structured parameters from the main workflow and performs the actual data calculations. It handles fetching data, routing to appropriate aggregation methods, and formatting results. Sub-Workflow Setup Instructions Import the Sub-Workflow Create a new workflow in n8n Copy the sub-workflow JSON (embedded in the Execute Workflow node) Import as a separate workflow Save with name: "Data Processing Sub-Workflow" Configure Google Sheets Connection for Sub-Workflow Apply the same Google Sheets OAuth2 credential you created for the main workflow Update the Get Data node with your Sheet ID Ensure it points to your data sheet (e.g., "Data" sheet) Configure Google Gemini for Output Formatting Apply the same Gemini API credential to the Google Gemini Chat Model1 node This handles final result formatting Link Workflows Together In the main workflow, find the Execute Workflow - Summarize Data node Update the workflow reference to point to your sub-workflow Ensure the sub-workflow is set to accept execution from other workflows Sub-Workflow Components When Executed by Another Workflow: Trigger that receives parameters Get Data: Fetches all data from Google Sheets Type of Aggregation: Switch node that routes based on aggregation type Multiple Summarize Nodes: Handle different aggregation types (sum, avg, count, etc.) Bring All Data Together: Combines results from different aggregation paths Write into Table Output: Formats final results using Gemini AI Example Usage Once both workflows are set up, you can ask questions like: Overall Metrics: "Show total Spend ($)" "Show total Clicks" "Show average Conversions" Single Dimension: "Show total Spend ($) by Channel" "Show total Clicks by Campaign" Two Dimensions: "Show total Spend ($) by Channel and Campaign" "Show average Clicks by Channel and Campaign" Data Flow Between Workflows Main Workflow: User question → Gemini AI → Structured JSON output Sub-Workflow: Receives JSON → Fetches data → Performs calculations → Returns formatted table --- Contact Information For support, customization, or questions about this template: Email: robert@ynteractive.com LinkedIn: Robert Breen Need help implementing these workflows, want to remove limitations, or require custom modifications? Reach out for professional n8n automation services and AI integration support.
By Robert BreenServer & network monitoring alerts via WhatsApp using HetrixTools
This workflow integrates HetrixTools with WhatsApp via the GOWA API to automate notifications about server monitoring events. It distinguishes between Uptime Monitoring and Resource Usage Monitoring events, formats the message accordingly, and sends it to a WhatsApp number using the GOWA WhatsApp REST API. It's especially useful for DevOps, sysadmins, or teams who need real-time server alerts delivered via WhatsApp. --- ⚙️ Setup Instructions Set up HetrixTools: Create a HetrixTools account at https://hetrixtools.com/register Create your Uptime Monitors and/or enable Resource Usage Monitoring for your servers. Go to your HetrixTools contact settings and add the n8n Webhook URL provided by the workflow. Make sure to select this contact in your monitor’s alert settings. Configure n8n Webhook: Set the Webhook node to HTTP method: POST Ensure it is accessible via a public URL (you can use n8n Cloud, reverse proxy, or tunnel like ngrok for testing). Customize WhatsApp Message: The workflow includes a conditional branch to check whether the event is a Resource Usage alert or an Uptime alert. Each branch contains editable text nodes for customizing the WhatsApp message content. Set up GOWA WhatsApp API: Make sure your GOWA instance is running and accessible. Create necessary credentials (API key, base URL, etc.). In n8n, add the credentials and fill in the sendChatPresence and sendText nodes accordingly. Deploy the Workflow: Save and activate the workflow. Trigger a test alert from HetrixTools to verify if messages are received on WhatsApp. --- 🧱 Prerequisites An active HetrixTools account with Uptime Monitors or Resource Usage Monitoring enabled. A publicly accessible instance of n8n with Webhook node enabled. Access to a running and configured GOWA (WhatsApp REST API) server. Required credentials configured in n8n for GOWA (API key, URL, phone number, etc.). ---
By Syamsul BahriSend links from Telegram channel to Hoarder and Readeck
What this template is made for: I have a personal Telegram channel and a bot inside it where I save interesting links that I want to save or read later. The idea is that n8n will take care of reading the new links added to this channel and send them, through the corresponding API, to the Hoarder and Readeck installations. How it works Since my server where n8n runs is not always on, a "Schedule Trigger" will be responsible for checking every so often if there is any new content in the Telegram channel where I store the links. This request is made through "http request" and the Telegram API. Next, a code block is responsible for filtering out everything that is not a hyperlink. At this point, the flow splits into two so that parallel and similar processes are performed for Hoarder and Readeck. The corresponding API is accessed to get a list of all the links saved in the corresponding service. A code block is responsible for filtering the list of hyperlinks previously obtained from Telegram so that only those that are not already saved in the service continue. Finally, another "Http Request" node is responsible for using the service API to save the link in the corresponding service. Configuration instructions The template makes use of the environment variables that I have declared in the n8n "docker-compose.yml" file through an external ".env" file. These are the variables I use: txt Telegram Bot Token Sherlink TGSHERLINKBOT_TOKEN=XXXXXXXX:XXXXXXXXXXXXXXXX Id Telegram Channel Sherlink TGSHERLINKID=-XXXXXXXXXXXXX Readeck server READECK_SERVER=http://readeck.midomain.com READECKAPIKEY=xxxxxxxxxxxxx Hoarder server HOARDER_SERVER=http://hoarder.midomain.com HOARDERAPIKEY=xxxxxxxxxxxxxx Created in 1.85.4 n8n version
By Sherlockes🛠️ CircleCI tool MCP server
🛠️ CircleCI Tool MCP Server Complete MCP server exposing all CircleCI Tool operations to AI agents. Zero configuration needed - all 3 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every CircleCI Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n CircleCI Tool tool with full error handling 📋 Available Operations (3 total) Every possible CircleCI Tool operation is included: 🔧 Pipeline (3 operations) • Get a pipeline • Get many pipelines • Trigger a pipeline 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native CircleCI Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every CircleCI Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.
By David Ashby