Workflow Templates

Gmail Lead Reply Analyzer → HubSpot Task + Slack Alert Most sales teams read every email, guess if it’s important, and tell teammates manually. This workflow does it automatically: check intent and sentiment with AI, create follow-up tasks, send Slack alerts, and save everything to Google Sheets. Benefits AI checks sentiment, intent, urgency, and priority Creates HubSpot tasks only if follow-up is needed Sends Slack message with lead summary Logs all results to Google Sheets for tracking Runs 24/7 with no manual sorting How It Works Gmail trigger watches a label for new replies Workflow extracts sender, subject, and message AI analyzes message and returns: sentiment, intent, urgency, next step Code step cleans result, adds date, and checks if follow-up is needed If follow-up = yes → create HubSpot task, send Slack alert, log to Sheets If follow-up = no → just log to Sheets Who Is This For Sales teams getting many leads by email Founders who handle leads themselves Agencies needing clear and fast lead triage Setup Connect Gmail (choose or create label) Add OpenAI API key (model: GPT-4o mini) Connect HubSpot (App Token for tasks) Connect Slack (channel for alerts) Connect Google Sheets (Spreadsheet + Tab) Optional: change how urgency/priority is scored in the code ROI & Monetization Save 3–6 hours per week on email sorting Answer faster and close more deals Sell as $1k–$3k/month “inbox automation” service Strategy Insights In the full walkthrough, I show how to: Make sure AI always returns valid JSON Adjust what counts as a follow-up lead Format Slack messages for quick reading Use Google Sheets as a simple dashboard Check Out My Channel For more AI automation systems that get real results, check out my YouTube channel where I share exactly how I build automation workflows, sell high-value services, and scale to $20k+ monthly revenue.

6 nodes1 views

LinkedIn Lead Personalization with Google Drive, Apify & AI

by Mariela Slavenova

This template enriches a lead list by analyzing each contact’s LinkedIn activity and auto-generating a single personalized opening line for cold outreach. Drop a spreadsheet into a Google Drive folder → the workflow parses rows, fetches LinkedIn content (recent post or profile), uses an LLM to craft a one-liner, writes the result back to Google Sheets, and sends a Telegram summary. ⸻ Good to know • Works with two paths: • Recent post found → personalize from the latest LinkedIn post. • No recent post → personalize from profile fields (headline, about, current role). • Requires valid Apify credentials for LinkedIn scrapers and LLM keys (Anthropic and/or OpenAI). • Costs depend on the LLM(s) you choose and scraping usage. • Replace all placeholders like [put your token here] and [put your Telegram Bot Chat ID here] before running. • Respect the target platform’s terms of service when scraping LinkedIn data. What this workflow does Trigger (Google Drive) – Watches a specific folder for newly uploaded lead spreadsheets. Download & Parse – Downloads the file and converts it to structured items (first name, last name, company, LinkedIn URL, email, website). Batch Loop – Processes each row individually. Fetch Activity – Calls Apify LinkedIn Profile Posts (latest post) and records current date for recency checks. Recency Check (LLM) – An OpenAI node returns true/false for “post is from the current year.” Branching • If TRUE → AI Agent (Anthropic) crafts a single, natural reference line based on the recent post. • If FALSE → Apify LinkedIn Profile → AI Agent (Anthropic) crafts a one-liner from profile data (headline/about/current role). Write Back (Google Sheets) – Updates the original sheet by matching on email and writing the personalization field. Notify (Telegram) – Sends a brief completion summary with sheet name and link. Requirements • Google Drive & Google Sheets connections • Apify account + token for LinkedIn scrapers • LLM keys: Anthropic (Claude) and/or OpenAI (you can use one or both) • Telegram bot for notifications (bot token + chat ID) How to use Connect credentials for Google, Apify, OpenAI/Anthropic, and Telegram. Set your folder in the Google Drive Trigger to the one where you’ll drop lead sheets. Map sheet columns to the expected headers (e.g., First Name, Last Name, Company Name for Emails, Person Linkedin Url, Email, Website). Replace placeholders ([put your token here], [put your Telegram Bot Chat ID here]) in the respective nodes. Upload a test spreadsheet to the watched folder and run once to validate the flow. Review results in your sheet (new personalization column) and check Telegram for the completion message. Setup Connect credentials - Google Drive/Sheets, Apify, OpenAI and/or Anthropic, Telegram. Configure the Drive trigger - Select the folder where you’ll upload your lead sheets. Map columns - Ensure your sheet has: First Name, Last Name, Company Name for Emails, Person Linkedin Url, Email, Website. Replace placeholders - In HTTP nodes: Bearer [put your token here]. In Telegram node: [put your Telegram Bot Chat ID here] (Optional) Adjust the recency rule - Current logic checks for current-year posts; change the prompt if you prefer 30-day windows. How to use Upload a test spreadsheet to the watched Drive folder. Execute the workflow once to validate. Open your Google Sheet to see the new personalization column populated. Check Telegram for the completion summary. Customizing this template • Data sources: Add company news, website content, or X/Twitter as fallback signals. • LLM choices: Use only Anthropic or only OpenAI; tweak temperature for tone. • Destinations: Write to a CRM (HubSpot/Salesforce/Airtable) instead of Sheets. • Notifications: Swap Telegram for Slack/Email/Discord. Who it’s for • Sales & SDR teams needing authentic, scalable personalization for cold outreach. • Lead gen agencies enriching spreadsheets with ready-to-use openers. • Marketing & growth teams improving reply rates by referencing real prospect activity. Limitations & compliance • LinkedIn scraping may be rate-limited or blocked; follow platform ToS and local laws. • Costs vary with scraping volume and LLM usage. Need help customizing? Contact me for consulting and support: LinkedIn

8 nodes

Multi-Modal Personal AI Assistant with Telegram & Google Gemini for Productivity Tools

by Anshul Chauhan

Automate Your Life: The Ultimate AI Assistant in Telegram (Powered by Google Gemini) Transform your Telegram messenger into a powerful, multi-modal personal or team assistant. This n8n workflow creates an intelligent agent that can understand text, voice, images, and documents, and take action by connecting to your favorite tools like Google Calendar, Gmail, Todoist, and more. At its core, a powerful Manager Agent, driven by Google Gemini, interprets your requests, orchestrates a team of specialized sub-agents, and delivers a coherent, final response, all while maintaining a persistent memory of your conversations. Key Features 🧠 Intelligent Automation: Uses Google Gemini as a central "Manager Agent" to understand complex requests and delegate tasks to the appropriate tool. 🗣️ Multi-Modal Input: Interact naturally by sending text, voice notes, photos, or documents directly into your Telegram chat. 🔌 Integrated Toolset: Comes pre-configured with agents to manage your memory, tasks, emails, calendar, research, and project sheets. 🗂️ Persistent Memory: Leverages Airtable as a knowledge base, allowing the assistant to save and recall personal details, company information, or past conversations for context-rich interactions. ⚙️ Smart Routing: Automatically detects the type of message you send and routes it through the correct processing pipeline (e.g., voice is transcribed, images are analyzed). 🔄 Conversational Context: Utilizes a window buffer to maintain short-term memory, ensuring follow-up questions and commands are understood within the current conversation. How It Works The Telegram Trigger node acts as the entry point, receiving all incoming messages (text, voice, photo, document). A Switch node intelligently routes the message based on its type: Voice**: The audio file is downloaded and transcribed into text using a voice-to-text service. Photo**: The image is downloaded, converted to a base64 string, and prepared for visual analysis. Document**: The file is routed to a document handler that extracts its text content for processing. Text**: The message is used as-is. A Merge node gathers the processed input into a unified prompt. The Manager Agent receives this prompt. It analyzes the user's intent and orchestrates one or more specialized agents/tools: memory_base (Airtable): For saving and retrieving information from your long-term knowledge base. todo_and_task_manager (Todoist): To create, assign, or check tasks. email_agent (Gmail): To compose, search, or send emails. calendar_agent (Google Calendar): To schedule events or check your agenda. research_agent (Wikipedia/Web Search): To look up information. project_management (Google Sheets): To provide updates on project trackers. After executing the required tasks, the Manager Agent formulates a final response and sends it back to you via the Telegram node. Setup Instructions Follow these steps to get your AI assistant up and running. Telegram Bot: Create a new bot using the BotFather in Telegram to get your Bot Token. In the n8n workflow, configure the Telegram Trigger node's webhook. Add your Bot Token to the credentials in all Telegram nodes. For proactive messages, replace the chatId placeholders with your personal Telegram Chat ID. Google Gemini AI: In the Google Gemini nodes, add your credentials by providing your Google Gemini API key. Airtable Knowledge Base: Set up an Airtable base to act as your assistant's long-term memory. In the memory_base nodes (Airtable nodes), configure the credentials and provide the Base ID and Table ID. Google Workspace APIs: Connect your Google account credentials for Gmail, Google Calendar, and Google Sheets. In the relevant nodes, specify the Document/Sheet IDs you want the assistant to manage. Connect Other Tools: Add your credentials for Todoist and any other integrated tool APIs. Configure Conversational Memory: This workflow is designed for multi-user support. Verify that the Session Key in the "Window Buffer Memory" nodes is correctly set to a unique user identifier from Telegram (e.g., {{ $json.chat.id }}). This ensures conversations from different users are kept separate. Review Schedule Triggers: Check any nodes designed to run on a schedule (e.g., "At a regular time"). Adjust their cron expressions, times, and timezone to fit your needs (e.g., for daily summaries). Test the Workflow: Activate the workflow. Send a text message to your bot (e.g., "Hello!"). Estimated Setup Time 30–60 minutes:** If you already have your API keys, account credentials, and service IDs (like Sheet IDs) ready. 2–3 hours:** For a complete, first-time setup, which includes creating API keys, setting up new spreadsheets or Airtable bases, and configuring detailed permissions.

8 nodes5 views

🤖 Self Improving Email AI Support with Human-in-the-Loop

by Lucas Peyrin

How it works This workflow creates a sophisticated, self-improving customer support system that automatically handles incoming emails. It's designed to answer common questions using an AI-powered knowledge base and, crucially, to learn from human experts when new or complex questions arise, continuously expanding its capabilities. Think of it like having an AI assistant with a smart memory and a human mentor. Here's the step-by-step process: New Email Received: The workflow is triggered whenever a new email arrives in your designated support inbox (via Gmail). Classify Request: An AI model (Google Gemini 2.5 Flash Lite) first classifies the incoming email to ensure it's a genuine support request, filtering out irrelevant messages. Retrieve Knowledge Base: The workflow fetches all existing Question and Answer pairs from your dedicated Google Sheet knowledge base. AI Answer Attempt: A powerful AI model (Google Gemini 2.5 Pro) analyzes the customer's email against the entire knowledge base. It attempts to find a highly relevant answer and drafts a complete HTML email response if successful. Decision Point: An IF node checks if the AI found a confident answer. If Answer Found: The AI-generated HTML response is immediately sent back to the customer via Gmail. If No Answer Found (Human-in-the-Loop): Escalate to Human: The customer's summarized question and original email are forwarded to a human expert (you or your team) via Gmail, requesting their assistance. Human Reply & AI Learning: The workflow waits for the human expert's reply. Once received, another AI model (Google Gemini 2.5 Flash) processes both the original customer question and the expert's reply to distill them into a new, generic, and reusable Question/Answer pair. Update Knowledge Base: This newly created Q&A pair is then automatically added as a new row to your Google Sheet knowledge base, ensuring the system can answer similar questions automatically in the future. Set up steps Setup time: ~10-15 minutes This workflow requires connecting your Gmail and Google Sheets accounts, and obtaining a Google AI API key. Follow these steps carefully: Connect Your Gmail Account: Select the On New Email Received node. Click the Credential dropdown and select + Create New Credential to connect your Gmail account. Grant the necessary permissions. Repeat this for the Send AI Answer and Ask Human for Help nodes, selecting the credential you just created. Connect Your Google Sheets Account: Select the Get Knowledge Base node. Click the Credential dropdown and select + Create New Credential to connect your Google account. Grant the necessary permissions. Repeat this for the Add to Knowledge Base node, selecting the credential you just created. Set up Your Google Sheet Knowledge Base: Create a new Google Sheet in your Google Drive. Rename the first sheet (tab) to QA Database. In the first row of QA Database, add two column headers: Question (in cell A1) and Answer (in cell B1). Go back to the Get Knowledge Base node in n8n. In the Document ID field, select your newly created Google Sheet. Do the same for the Add to Knowledge Base node. Get Your Google AI API Key (for Gemini Models): Visit Google AI Studio at aistudio.google.com/app/apikey. Click "Create API key in new project" and copy the key. In the workflow, go to the Google Gemini 2.5 Pro node, click the Credential dropdown, and select + Create New Credential. Paste your key into the API Key field and Save. Repeat this for the Google Gemini 2.5 Flash Lite and Google Gemini 2.5 Flash nodes, selecting the credential you just created. Configure Human Expert Email: Select the Ask Human for Help node. In the Send To field, replace the placeholder email address with the actual email address of your human expert (e.g., your own email or a team support email). Activate the Workflow: Once all credentials and configurations are set, activate the workflow using the toggle switch at the top right of your n8n canvas. Start Learning! Send a test email to the Gmail account connected to the On New Email Received node. Observe how the AI responds, or how it escalates to your expert email and then learns from the reply. Check your Google Sheet to see new Q&A pairs being added!

6 nodes125 views

Personal Assistant Bot with Multi-Agent System using Telegram & Google Gemini

by Akil A

How It Works Telegram Trigger** receives incoming messages (text, voice, photo, document). Switch** routes by message type to appropriate processors: Text → forwarded as-is. Voice → downloaded and sent to Transcribe a recording. Photo → downloaded, converted to base64, then sent to Analyze image. Document → routed to document handler. Merge* collects the processed input and passes a unified prompt to *Manager Agent**. Manager Agent (LM: Google Gemini)** orchestrates specialized agents/tools: memory_base (Airtable) → saving & retrieving personal/company memory todo_and_task_manager (Todoist / Google Sheets) → tasks email_agent (Gmail) → composing/sending emails calendar_agent (Google Calendar) → scheduling research_agent (SerpAPI / Wikipedia / Wolfram) → web research project_management (Google Sheets) → project updates Manager Agent** updates memory windows and sends the final reply back to Telegram. Setup Steps Create and configure Telegram bot; set bot token/webhook in Telegram Trigger and Telegram nodes. Update chatId placeholders. Add Google Gemini (PaLM) credentials in the Gemini model nodes. Configure Airtable knowledge-base: set base ID & table IDs used by memory_base nodes. Connect Google APIs: Sheets, Calendar, Gmail credentials and set document/sheet IDs. Configure Todoist, SerpAPI, WolframAlpha credentials and any other tool API keys. Verify Window Buffer Memory sessionKey values (match user sessions). Check schedule triggers (cron expressions) and adjust times/timezone. Run quick tests: send text, voice, image, and confirm replies and memory writes. Estimated Setup Time 30–60 minutes** → if credentials & IDs are ready. 2–4 hours** → full setup (API keys, spreadsheets, Airtable, detailed permissions). 4–8 hours** → complex deployment (team permissions, multiple calendars, advanced tool tuning, production testing).

10 nodes

Auto-Generate WordPress Articles from News with Claude AI and LinkedIn Sharing

by Marco Venturi

How it works This workflow sources news from news websites. The information is then passed to an LLM, which processes the article's content. An editor approves or rejects the article. If accepted, the article is first published on the WordPress site and then on the LinkedIn page. Setup Instructions 1. Credentials You'll need to add credentials for the following services in your n8n instance: News API**: A credential for your chosen news provider. LLM**: Your API key for the LLM you want to use. Google OAuth**: For both Gmail and Google Sheets. WordPress OAuth2**: To publish articles via the API. See the WordPress Developer Docs. LinkedIn OAuth2**: To share the post on a company page. 2. Node Configuration Don't forget to: Fetch News (HTTP Request)**: Set the query parameters (keywords, language, etc.) for your news source. Basic LLM Chain: Review and **customize the prompt to match your desired tone, language, and style. Approval request (Gmail)**: Set your email address in the Send To field. HTTP Request WP - Push article**: Replace <site_Id> in the URL with your WordPress Site ID. getImageId (Code Node)**: Update the array with your image IDs from the WordPress Media Library. Create a post (LinkedIn)**: Enter your LinkedIn Organization ID. Append row in sheet (Google Sheets)**: Select your Google Sheet file and the target sheet. All Email Nodes**: Make sure the Send To field is your email.

8 nodes1 views

Automated Customer Support with GPT-4 Knowledge Base Agent for Gmail

by Yasser Sami

Customer Support AI Agent for Gmail This n8n template demonstrates how to build an AI-powered customer support workflow that automatically handles incoming Gmail messages, classifies them, finds answers from your knowledge base, and sends a personalized reply. Who’s it for SaaS founders or teams who want to automate customer support. Freelancers and solopreneurs who receive repetitive customer queries. Companies that want to reduce manual email triage and improve response times. How it works / What it does Trigger: A new email arrives in Gmail. Classification: The workflow uses a text classifier to decide whether the email is customer support-related or not. If not, it’s ignored. If yes, it proceeds. AI Agent: Queries a knowledge base (vector database with OpenAI embeddings). Retrieves the most relevant answer. Drafts a reply using AI (OpenAI or Google Gemini model). Post-processing: Labels the email in Gmail for organization. Sends a reply automatically. This ensures that your customers get timely, relevant responses without manual intervention. How to set up Import this template into your n8n account. Connect your Gmail account in the Gmail Trigger, Label, and Reply nodes. Connect your AI model provider (OpenAI or Google Gemini). Configure the knowledge base embeddings (upload your docs/FAQ into the vector database). Activate the workflow — and your AI customer support agent is live! Requirements n8n account. Gmail account (with API access enabled). OpenAI or Google Gemini account for LLM and embeddings. Knowledge base data (FAQ, documentation, or past tickets). Google Drive account for auto update your vector database(with API access enabled). How to customize the workflow Knowledge Base**: Replace or expand with your own company docs, FAQs, or past conversations. Classification Rules**: Train or adjust the classifier to handle more categories (e.g., Sales, Partnership, Technical Support). Reply Style**: Customize AI prompts for tone — professional, casual, or friendly. Labels**: Change Gmail labels to match your workflow (e.g., “Support,” “Sales,” “Priority”). Multi-language**: Add translation steps if your customers speak different languages. This template saves you hours of manual email triage and ensures your customers always get quick, accurate responses.

9 nodes1 views

Automatic Email Categorization & Labeling in Zoho Mail with GPT-4o-mini

by Ruthwik

📧 AI-Powered Email Categorization & Labeling in Zoho Mail This n8n template demonstrates how to use AI text classification to automatically categorize incoming emails in Zoho Mail and apply the correct label (e.g., Support, Billing, HR). It saves time by keeping your inbox structured and ensures emails are routed to the right category. Use cases include: Routing customer support requests to the correct team. Organizing billing and finance communications separately. Streamlining HR and recruitment email handling. Reducing inbox clutter and ensuring no important message is missed. ℹ️ Good to know You’ll need to configure Zoho OAuth credentials — see Self Client Overview, Authorization Code Flow, and Zoho Mail OAuth Guide. The labels must already exist in Zoho Mail (e.g., Support, Billing, HR). The workflow fetches these labels and applies them automatically. The Zoho Mail API domain changes depending on your account region: .com → Global accounts (https://mail.zoho.com/api/...) .eu → EU accounts (https://mail.zoho.eu/api/...) .in → India accounts (https://mail.zoho.in/api/...) Example: For an EU account, the endpoint would be: https://mail.zoho.eu/api/accounts/<accountID>/updatemessage The AI model used for text classification may incur costs depending on your provider (e.g., OpenRouter). Start by testing with a small set of emails before enabling for your full inbox. 🔄 How it works A new email in Zoho Mail triggers the workflow. OAuth authentication retrieves access to Zoho Mail’s API. All available labels are fetched, and a label map (display name → ID) is created. The AI model analyzes the subject and body to predict the correct category. The workflow routes the email to the right category branch. The matching Zoho Mail label is applied (final node is deactivated by default). 🛠️ How to use Create the required labels (e.g., Support, Billing, HR, etc.) in your Zoho Mail account before running the workflow. Replace the Zoho Mail Account ID in the Set Account ID node. Configure your Zoho OAuth credentials in the Get Access Token node. Update the API base URL to match your Zoho account’s region (.com, .eu, .in, etc.). Activate the Apply Label to Email node once ready for production. Optionally, adjust categories in the AI classifier prompt to fit your organization’s needs. 📋 Requirements Zoho Mail account with API access enabled. Labels created in Zoho Mail for each category you want to classify. OAuth credentials set up in n8n. Correct Zoho Mail API domain (.com, .eu, .in) based on your account region. An AI model (via OpenRouter or other provider) for text classification. 🎨 Customising this workflow This workflow can be adapted to many inbox management scenarios. Examples include: Auto-routing customer inquiries to specific departments. Prioritizing VIP client emails with special labels. Filtering job applications directly into an HR-managed folder.

Automated Blog Post Generator with GPT-4, DALL-E, and Wikipedia for WordPress

by Punit

WordPress AI Content Creator Overview Transform a few keywords into professionally written, SEO-optimized WordPress blog posts with custom featured images. This workflow leverages AI to research topics, structure content, write engaging articles, and publish them directly to your WordPress site as drafts ready for review. What This Workflow Does Core Features Keyword-to-Article Generation**: Converts simple keywords into comprehensive, well-structured articles Intelligent Content Planning**: Uses AI to create logical chapter structures and content flow Wikipedia Integration**: Researches factual information to ensure content accuracy and depth Multi-Chapter Writing**: Generates coherent, contextually-aware content across multiple sections Custom Image Creation**: Generates relevant featured images using DALL-E based on article content SEO Optimization**: Creates titles, subtitles, and content optimized for search engines WordPress Integration**: Automatically publishes articles as drafts with proper formatting and featured images Business Value Content Scale**: Produce high-quality blog posts in minutes instead of hours Research Efficiency**: Automatically incorporates factual information from reliable sources Consistency**: Maintains professional tone and structure across all generated content SEO Benefits**: Creates search-engine friendly content with proper HTML formatting Cost Savings**: Reduces need for external content creation services Prerequisites Required Accounts & Credentials WordPress Site with REST API enabled OpenAI API access (GPT-4 and DALL-E models) WordPress Application Password or JWT authentication Public-facing n8n instance for form access (or n8n Cloud) Technical Requirements WordPress REST API v2 enabled (standard on most WordPress sites) WordPress user account with publishing permissions n8n instance with LangChain nodes package installed Setup Instructions Step 1: WordPress Configuration Enable REST API (usually enabled by default): Check that yoursite.com/wp-json/wp/v2/ returns JSON data If not, contact hosting provider or install REST API plugin Create Application Password: In WordPress Admin: Users > Profile Scroll to "Application Passwords" Add new password with name "n8n Integration" Copy the generated password (save securely) Get WordPress Site URL: Note your full WordPress site URL (e.g., https://yourdomain.com) Step 2: OpenAI Configuration Obtain OpenAI API Key: Visit OpenAI Platform Create API key with access to: GPT-4 models (for content generation) DALL-E (for image creation) Add OpenAI Credentials in n8n: Navigate to Settings > Credentials Add "OpenAI API" credential Enter your API key Step 3: WordPress Credentials in n8n Add WordPress API Credentials: In n8n: Settings > Credentials > "WordPress API" URL: Your WordPress site URL Username: Your WordPress username Password: Application password from Step 1 Step 4: Update Workflow Settings Configure Settings Node: Open the "Settings" node Replace wordpress_url value with your actual WordPress URL Keep other settings as default or customize as needed Update Credential References: Ensure all WordPress nodes reference your WordPress credentials Verify OpenAI nodes use your OpenAI credentials Step 5: Deploy Form (Production Use) Activate Workflow: Toggle workflow to "Active" status Note the webhook URL from Form Trigger node Test Form Access: Copy the form URL Test form submission with sample data Verify workflow execution completes successfully Configuration Details Form Customization The form accepts three key inputs: Keywords**: Comma-separated topics for article generation Number of Chapters**: 1-10 chapters for content structure Max Word Count**: Total article length control You can modify form fields by editing the "Form" trigger node: Add additional input fields (category, author, publish date) Change field types (dropdown, checkboxes, file upload) Modify validation rules and requirements AI Content Parameters Article Structure Generation The "Create post title and structure" node uses these parameters: Model**: GPT-4-1106-preview for enhanced reasoning Max Tokens**: 2048 for comprehensive structure planning JSON Output**: Structured data for subsequent processing Chapter Writing The "Create chapters text" node configuration: Model**: GPT-4-0125-preview for consistent writing quality Context Awareness**: Each chapter knows about preceding/following content Word Count Distribution**: Automatically calculates per-chapter length Coherence Checking**: Ensures smooth transitions between sections Image Generation Settings DALL-E parameters in "Generate featured image": Size**: 1792x1024 (optimized for WordPress featured images) Style**: Natural (photographic look) Quality**: HD (higher quality output) Prompt Enhancement**: Adds photography keywords for better results Usage Instructions Basic Workflow Access the Form: Navigate to the form URL provided by the Form Trigger Enter your desired keywords (e.g., "artificial intelligence, machine learning, automation") Select number of chapters (3-5 recommended for most topics) Set word count (1000-2000 words typical) Submit and Wait: Click submit to trigger the workflow Processing takes 2-5 minutes depending on article length Monitor n8n execution log for progress Review Generated Content: Check WordPress admin for new draft post Review article structure and content quality Verify featured image is properly attached Edit as needed before publishing Advanced Usage Custom Prompts Modify AI prompts to change: Writing Style**: Formal, casual, technical, conversational Target Audience**: Beginners, experts, general public Content Focus**: How-to guides, opinion pieces, news analysis SEO Strategy**: Keyword density, meta descriptions, heading structure Bulk Content Creation For multiple articles: Create separate form submissions for each topic Schedule workflow executions with different keywords Use CSV upload to process multiple keyword sets Implement queue system for high-volume processing Expected Outputs Article Structure Generated articles include: SEO-Optimized Title**: Compelling, keyword-rich headline Descriptive Subtitle**: Supporting context for the main title Introduction**: ~60 words introducing the topic Chapter Sections**: Logical flow with HTML formatting Conclusions**: ~60 words summarizing key points Featured Image**: Custom DALL-E generated visual Content Quality Features Factual Accuracy**: Wikipedia integration ensures reliable information Proper HTML Formatting**: Bold, italic, and list elements for readability Logical Flow**: Chapters build upon each other coherently SEO Elements**: Optimized for search engine visibility Professional Tone**: Consistent, engaging writing style WordPress Integration Draft Status**: Articles saved as drafts for review Featured Image**: Automatically uploaded and assigned Proper Formatting**: HTML preserved in WordPress editor Metadata**: Title and content properly structured Troubleshooting Common Issues "No Article Structure Generated" Cause: AI couldn't create valid structure from keywords Solutions: Use more specific, descriptive keywords Reduce number of chapters requested Check OpenAI API quotas and usage Verify keywords are in English (default language) "Chapter Content Missing" Cause: Individual chapter generation failed Solutions: Increase max tokens in chapter generation node Simplify chapter prompts Check for API rate limiting Verify internet connectivity for Wikipedia tool "WordPress Publication Failed" Cause: Authentication or permission issues Solutions: Verify WordPress credentials are correct Check WordPress user has publishing permissions Ensure WordPress REST API is accessible Test WordPress URL accessibility "Featured Image Not Attached" Cause: Image generation or upload failure Solutions: Check DALL-E API access and quotas Verify image upload permissions in WordPress Review image file size and format compatibility Test manual image upload to WordPress Performance Optimization Large Articles (2000+ words) Increase timeout values in HTTP request nodes Consider splitting very long articles into multiple posts Implement progress tracking for user feedback Add retry mechanisms for failed API calls High-Volume Usage Implement queue system for multiple simultaneous requests Add rate limiting to respect OpenAI API limits Consider batch processing for efficiency Monitor and optimize token usage Customization Examples Different Content Types Product Reviews Modify prompts to include: Pros and cons sections Feature comparisons Rating systems Purchase recommendations Technical Tutorials Adjust structure for: Step-by-step instructions Code examples Prerequisites sections Troubleshooting guides News Articles Configure for: Who, what, when, where, why structure Quote integration Fact checking emphasis Timeline organization Alternative Platforms Replace WordPress with Other CMS Ghost**: Use Ghost API for publishing Webflow**: Integrate with Webflow CMS Strapi**: Connect to headless CMS Medium**: Publish to Medium platform Different AI Models Claude**: Replace OpenAI with Anthropic's Claude Gemini**: Use Google's Gemini for content generation Local Models**: Integrate with self-hosted AI models Multiple Models**: Use different models for different tasks Enhanced Features SEO Optimization Add nodes for: Meta Description Generation**: AI-created descriptions Tag Suggestions**: Relevant WordPress tags Internal Linking**: Suggest related content links Schema Markup**: Add structured data Content Enhancement Include additional processing: Plagiarism Checking**: Verify content originality Readability Analysis**: Assess content accessibility Fact Verification**: Multiple source confirmation Image Optimization**: Compress and optimize images Security Considerations API Security Store all credentials securely in n8n credential system Use environment variables for sensitive configuration Regularly rotate API keys and passwords Monitor API usage for unusual activity Content Moderation Review generated content before publishing Implement content filtering for inappropriate material Consider legal implications of auto-generated content Maintain editorial oversight and fact-checking WordPress Security Use application passwords instead of main account password Limit WordPress user permissions to minimum required Keep WordPress and plugins updated Monitor for unauthorized access attempts Legal and Ethical Considerations Content Ownership Understand OpenAI's terms regarding generated content Consider copyright implications for Wikipedia-sourced information Implement proper attribution where required Review content licensing requirements Disclosure Requirements Consider disclosing AI-generated content to readers Follow platform-specific guidelines for automated content Ensure compliance with advertising and content standards Respect intellectual property rights Support and Maintenance Regular Maintenance Monitor OpenAI API usage and costs Update AI prompts based on output quality Review and update Wikipedia search strategies Optimize workflow performance based on usage patterns Quality Assurance Regularly review generated content quality Implement feedback loops for improvement Test workflow with diverse keyword sets Monitor WordPress site performance impact Updates and Improvements Stay updated with OpenAI model improvements Monitor n8n platform updates for new features Engage with community for workflow enhancements Document custom modifications for future reference Cost Optimization OpenAI Usage Monitor token consumption patterns Optimize prompts for efficiency Consider using different models for different tasks Implement usage limits and budgets Alternative Approaches Use local AI models for cost reduction Implement caching for repeated topics Batch similar requests for efficiency Consider hybrid human-AI content creation License and Attribution This workflow template is provided under MIT license. Attribution to original creator appreciated when sharing or modifying. Generated content is subject to OpenAI's usage policies and terms of service.

🤖AI & Machine Learning

beginner

5 nodes

Generate AI-Powered End-of-Day Reports from ClickUp & GoHighLevel to Multi-Channel

by Rahul Joshi

Description Automatically generate and distribute detailed End-of-Day (EOD) reports combining task progress from ClickUp and opportunity data from GoHighLevel. This workflow uses AI to analyze daily performance, summarize key metrics, identify blockers, and deliver polished reports directly to Slack, Email, and Google Drive. ⚙️📊💬 What This Template Does Triggers automatically every weekday at 6:00 PM (Mon–Fri). ⏰ Fetches all completed ClickUp tasks and won GoHighLevel opportunities for the day. 📥 Merges and transforms both datasets into a unified structure. 🔄 Uses Azure OpenAI GPT-4 to analyze performance and generate structured summaries. 🤖 Formats three output versions — Slack (Markdown), Email (HTML), and Google Drive (Text). 🧾 Routes and sends reports automatically to connected channels. 📤 Uploads the generated text report to Google Drive with timestamped filenames. ☁️ Key Benefits ✅ Saves time by automating daily performance reporting. ✅ Unifies task and deal data into a single AI-generated summary. ✅ Provides real-time visibility into productivity and outcomes. ✅ Delivers beautifully formatted, channel-specific reports. ✅ Maintains historical reports in Google Drive for reference. ✅ Helps managers identify wins, blockers, and next steps quickly. Features Automated scheduling via cron (Mon–Fri, 6 PM). ClickUp task and GHL opportunity integration for daily data sync. AI-powered analysis for contextual, actionable summaries. Dynamic formatting for Slack, Email, and Drive outputs. Parallel routing for simultaneous delivery across platforms. No manual steps — runs fully hands-free after setup. Requirements ClickUp OAuth2 credentials for task retrieval. GoHighLevel OAuth2 credentials for deal data. Azure OpenAI GPT-4 API credentials. Slack Bot credentials for message posting. SMTP (Gmail/Outlook) credentials for email reports. Google Drive OAuth2 credentials for report upload. Target Audience 🎯 Sales, marketing, and operations teams tracking daily performance. 📈 Project managers monitoring team productivity and blockers. 🤝 Client success teams summarizing EOD outcomes for leadership. 🧠 Business automation teams seeking end-of-day visibility. Step-by-Step Setup Instructions Connect ClickUp, GoHighLevel, Slack, Gmail/SMTP, and Google Drive credentials. 🔑 Set your team, space, folder, and list IDs in the ClickUp node. 📋 Update your Slack channel ID in the Slack node configuration. 💬 Configure your email sender and recipients in the email node. 📧 (Optional) Modify the cron expression for different reporting times. ⏰ Test the workflow manually once, then activate for automated EOD execution. ✅

10 nodes

Beginner's Guide to Workflow Automation with OpenAI, LangChain & API Integrations

by Meelioo