Workflow Templates

This automated n8n workflow converts any technical documentation or blog post URL into a professional, step-by-step developer tutorial video complete with AI-generated narration, code syntax highlighting, terminal command animations, and visual diagrams. The system intelligently analyzes documentation structure, extracts code examples, generates natural voiceover narration, creates synchronized visual scenes, and automatically publishes the finished video to YouTube with SEO-optimized descriptions. Fundamental Aspects Webhook-Based Trigger**: Accepts HTTP POST requests containing a documentation URL to initiate the automated video creation pipeline on-demand. Intelligent Content Extraction**: Fetches HTML content, parses documentation structure, extracts code blocks with language detection, identifies headings for organization, and cleans irrelevant elements like navigation and scripts. AI-Powered Tutorial Planning**: Uses Claude AI to analyze documentation content and generate a comprehensive tutorial outline including section titles, duration estimates, narration scripts, visual types (code/terminal/diagram), and learning outcomes. Professional Audio Generation**: Converts narration scripts into high-quality audio using Google Cloud Text-to-Speech with natural-sounding neural voices, proper pacing, and timing synchronization. Dynamic Visual Scene Creation**: Generates code editor scenes with syntax highlighting and typewriter effects, terminal animations with command execution sequences, flowchart diagrams with progressive reveals, and text overlays with key points. Automated Video Rendering**: Combines audio narration with visual scenes using Remotion API to render publication-ready videos in 1080p resolution at 30fps with smooth transitions. Multi-Platform Distribution**: Automatically uploads completed videos to YouTube with AI-generated titles and descriptions, backs up to Google Drive for archival, and returns comprehensive metadata via webhook response. Setup Instructions Import the Workflow into n8n**: Download the workflow JSON file and import via n8n interface under "Workflows" → "Import from File" option. Configure Claude AI (Anthropic) Credentials**: Navigate to the "Analyze with Claude AI" node and click the credentials dropdown. Create new Anthropic credentials using your API key from console.anthropic.com. Ensure you have access to Claude Sonnet 4 model (claude-sonnet-4-20250514). Save and test the connection to verify API access. Set Up Google Cloud Text-to-Speech**: Go to Google Cloud Console and enable the Text-to-Speech API. Create a service account with "Cloud Text-to-Speech User" role. Generate and download a JSON key file for the service account. In n8n, navigate to "Generate Audio with Google TTS" node and add service account credentials. Upload the JSON key file when prompted. Configure Remotion API for Video Rendering**: Sign up for a Remotion account at remotion.dev and obtain API credentials. In the "Render Video with Remotion" node, add HTTP Header Auth credentials. Set authorization header with your Remotion API key. Ensure you have a Remotion composition named "TutorialVideo" deployed. Note: You may need to create a custom Remotion project for code highlighting and terminal animations. Add YouTube OAuth2 Credentials**: Navigate to "Upload to YouTube" node and create YouTube OAuth2 credentials. Follow Google's OAuth flow to authorize n8n to upload videos on your behalf. Ensure your YouTube account has upload permissions and is verified for videos longer than 15 minutes. Configure default privacy settings (public, unlisted, or private) in node parameters. Configure Google Drive Backup**: Go to "Backup to Google Drive" node and add Google Drive OAuth2 credentials. Authorize n8n to access your Google Drive. Optionally specify a folder ID in node options to organize video backups. Activate Webhook Endpoint**: Activate the workflow using the toggle switch in the top-right corner. Copy the webhook URL from the "Webhook Trigger" node (appears after activation). The URL will be in format: https://your-n8n-instance.com/webhook/create-video. Test the Workflow**: Send a test POST request to the webhook URL using curl, Postman, or HTTPie: curl -X POST https://your-n8n-instance.com/webhook/create-video \ -H "Content-Type: application/json" \ -d '{"documentationUrl": "https://docs.example.com/getting-started"}' Monitor the execution in n8n's "Executions" tab to track progress through each node. Check YouTube and Google Drive for the generated video (processing may take 5-15 minutes depending on content length). Verify Output Quality**: Review the generated video for audio quality, code highlighting accuracy, and pacing. Check YouTube description for proper formatting of prerequisites and learning outcomes. Ensure code snippets are readable and terminal animations are properly synchronized. Technical Dependencies Claude AI (Anthropic)**: For intelligent content analysis, tutorial outline generation, section structuring, and narration script writing with natural language processing. Google Cloud Text-to-Speech**: For converting narration scripts into professional-quality audio with neural voice models (en-US-Neural2-J recommended for technical content). Remotion API**: For programmatic video rendering, scene composition, code syntax highlighting, terminal animations, and transition effects (requires custom React components). YouTube Data API v3**: For automated video uploads, metadata management, thumbnail generation, and playlist organization. Google Drive API**: For backup storage, file sharing, and archival of raw video files with organized folder structures. n8n Platform**: For workflow orchestration, webhook handling, conditional logic, error handling, and execution monitoring. JavaScript Runtime**: For custom content parsing, JSON manipulation, code language detection, timing calculations, and data transformation in Code nodes. Customization Possibilities Voice Customization**: Change narrator voice in "Generate Narration Script" node by modifying the voice parameter. Google TTS offers multiple voices (male, female, different accents). Adjust speed (0.25-4.0) and pitch (-20 to +20) for different pacing styles. Use different voices for intro/outro vs main content. Video Branding**: Add custom intro/outro animations by modifying Remotion composition. Include your logo, channel name, and subscribe animations. Customize color schemes in code editor themes (Dracula, Monokai, Solarized, One Dark). Add watermarks or corner branding throughout video. Code Editor Themes**: Change syntax highlighting themes in "Create Visual Scenes" node. Popular options include Dracula (default), VS Code Dark+, GitHub Light, Monokai Pro, Nord. Adjust font sizes, line spacing, and highlighting animation speeds for readability. Content Filtering**: Add pre-processing logic to filter specific documentation sections. Skip changelog entries, API reference tables, or installation instructions if not needed. Focus on tutorial-style content only. Add minimum/maximum content length thresholds. Multi-Language Support**: Extend the workflow to detect documentation language and use appropriate TTS voices. Support Spanish (es-ES), French (fr-FR), German (de-DE), Japanese (ja-JP), and other languages. Generate localized titles and descriptions. Advanced Visual Types**: Add screen recording capabilities for live demonstrations. Include animated flowcharts using Mermaid or D3.js. Generate architecture diagrams from code structure. Add picture-in-picture video of an instructor or animated avatar. Tutorial Complexity Detection**: Use Claude AI to assess documentation difficulty level and adjust pacing accordingly. Beginner content gets slower narration and more detailed explanations. Advanced content can move faster with less repetition. Interactive Elements**: Generate timestamp chapters for YouTube with clickable sections. Create accompanying blog post or GitHub repository with code examples. Generate quiz questions based on content for learning validation. Quality Assurance**: Add validation nodes to check video quality before upload. Verify audio levels are balanced, code is readable at 1080p, and total duration matches expectations. Implement retry logic for failed renders. Batch Processing**: Extend webhook to accept multiple URLs for bulk video generation. Create playlists automatically for related documentation pages. Schedule sequential uploads to avoid flooding your channel. Analytics Integration**: Track video performance by connecting to YouTube Analytics API. Monitor view counts, engagement rates, and audience retention. Use insights to improve future video generation parameters. Cost Optimization**: Implement caching for previously processed documentation URLs to avoid redundant API calls. Use cheaper TTS voices for internal testing. Compress videos before upload while maintaining quality. Set API rate limits to control costs. Custom Remotion Components**: Build specialized React components for your tech stack (e.g., database schema visualizers, API request/response animations, deployment pipeline diagrams). Create reusable templates for common tutorial patterns. Notification System**: Add email or Slack notifications when videos complete processing. Include video URLs, processing time, and any errors encountered. Send daily summaries of generated videos. SEO Enhancement**: Use Claude AI to generate SEO-optimized titles, descriptions, and tags. Research trending keywords in your niche. Auto-generate closed captions and subtitles for accessibility and searchability. Explore More AI Video Automation: Contact us to design custom video automation workflows for product demos, educational content, marketing videos, or AI-powered content creation pipelines tailored to your business needs.

🎯 CV Keyword Optimizer An AI-powered n8n workflow that automatically tailors your resume to any job description by injecting relevant keywords — without touching your formatting, layout, or design. How It Works Architecture ┌─────────────────────────────────────────────────────────────────┐ │ User Input (Form) │ │ CV Google Docs Link + Job URL or Pasted JD │ └──────────────────────────┬──────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 1. Read CV from Google Docs API │ │ 2. Extract full CV text (handles tables, paragraphs, etc.) │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 3. Get Job Description │ │ ├── URL provided? → Scrape job page, strip HTML to text │ │ │ └── Scrape failed? → Fall back to manual JD │ │ └── No URL? → Use manually pasted JD directly │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 4. 🏠 Local Ollama (llama3.1:8b) │ │ Analyzes JD + CV → Extracts & ranks 10-20 ATS keywords │ │ Output: keyword, priority, target bullet, reason │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 5. ☁️ Groq API (Llama 3.3 70B) │ │ Takes ranked keywords + CV → Produces find/replace pairs │ │ Naturally weaves keywords into experience bullet points │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 6. Copy original CV via Google Drive API │ │ (preserves ALL formatting, tables, styles) │ │ │ │ 7. Apply replacements via Google Docs batchUpdate API │ │ (replaceAllText — formatting stays intact) │ └──────────────────────────┬───────────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────────────────┐ │ 8. Output │ │ ✅ New Google Doc link │ │ 📋 Changelog: original text → updated text + keywords added │ └──────────────────────────────────────────────────────────────────┘ Why Two AI Models? | Step | Model | Why | |------|-------|-----| | Keyword Extraction | Ollama llama3.1:8b (local) | Free, private, no API costs. Reasoning about which keywords actually matter for ATS | | Text Rewriting | Groq llama-3.3-70b-versatile (cloud) | Larger model = better at natural language. Produces find/replace pairs that read naturally | What Gets Modified ✅ Experience/work bullet points ✅ Skills/technical skills lines ❌ Name, contact info, education, dates, company names, job titles — never touched Formatting Preservation The workflow copies your original Google Doc (not recreates it), then uses replaceAllText to swap text in-place. This means: ✅ Tables, columns, fonts, colors — all preserved ✅ Bold, italic, underline — all preserved ✅ Custom spacing, margins — all preserved ✅ Original doc is untouched (changes go to the copy) Setup Steps Prerequisites Docker installed Ollama installed locally A Groq API key (free tier works) Google account with Docs & Drive access 1. Install & Start Ollama macOS brew install ollama Start the Ollama server ollama serve Pull the model (in another terminal) ollama pull llama3.1:8b Verify it's running: curl http://localhost:11434/api/tags 2. Get a Groq API Key Go to console.groq.com Sign up / log in Navigate to API Keys → Create a new key Copy the key (starts with gsk_...) 3. Configure Environment cd cv-generator Create .env from template cp .env.example .env Edit .env and add your Groq key GROQ_API_KEY=gsk_your_key_here 4. Start n8n docker compose up -d n8n will be available at http://localhost:5678 Default credentials: Username: admin Password: changeme > ⚠️ Change these in docker-compose.yml for production use. 5. Import the Workflow Open n8n at http://localhost:5678 Go to Workflows → Import from File Select cv-keyword-optimizer.json You'll see credential warnings on some nodes — that's expected 6. Set Up Google Credentials In n8n, go to Settings → Credentials Create a Google Docs OAuth2 credential Follow n8n's OAuth2 setup guide for Google Required scopes: https://www.googleapis.com/auth/documents Create a Google Drive OAuth2 credential Required scopes: https://www.googleapis.com/auth/drive Click each node with a ⚠️ warning → select your credential from the dropdown 7. Activate & Use Toggle the workflow Active Open the form URL shown in the trigger node (or go to http://localhost:5678/form/cv-keyword-optimizer-form) Fill in: Google Docs CV Link (required) Job Posting URL or Job Description (at least one) Submit and wait ~30-60 seconds Get your optimized CV link + detailed changelog Project Structure cv-generator/ ├── cv-keyword-optimizer.json # n8n workflow definition ├── docker-compose.yml # n8n container config ├── .env # Environment variables (not committed) ├── .env.example # Template for .env ├── .gitignore # Ignores .env └── README.md # This file Troubleshooting | Issue | Solution | |-------|----------| | Ollama connection refused | Make sure ollama serve is running. n8n reaches it via host.docker.internal:11434 | | Groq 429 rate limit | Free tier has limits. Wait a minute and retry | | Scraping fails on LinkedIn | LinkedIn blocks scrapers. Paste the JD manually instead | | Google Docs auth error | Re-check OAuth2 credentials in n8n. Ensure correct scopes | | Replacements don't apply | The AI's "find" text must exactly match the CV. Check the Changes Summary for what was attempted | | Empty response from Ollama | Model may still be loading. First run takes longer. Timeout is set to 5 min |

Complete AI support system using website data (RAG pipeline) This template provides a full end-to-end Retrieval-Augmented Generation (RAG) system using n8n. It includes two connected workflows: A data ingestion pipeline that crawls a website and stores its content in a vector database. A customer support chatbot that retrieves this knowledge and answers user queries in real time. Together, these workflows allow you to turn any public website into an intelligent AI-powered support assistant grounded in real business data. Use cases AI customer support chatbot for your website Internal company knowledge assistant Product FAQ automation Helpdesk or IT support bot AI receptionist for services Semantic search over company content How it works Ingestion workflow Discover all URLs from a website sitemap. Filter and normalize the URLs. Fetch each page and extract readable text. Clean HTML into plain text. Split text into overlapping chunks. Generate embeddings using OpenAI. Store vectors in Pinecone with metadata. Chatbot workflow A user sends a message via chat webhook. The agent queries Pinecone for relevant knowledge. Retrieved content is passed to OpenAI. OpenAI generates a grounded response. Short-term memory maintains conversation context. How to use Step 1 – Run ingestion Set your target website URL. Add Firecrawl, OpenAI, and Pinecone credentials. Create a Pinecone index. Execute the ingestion workflow. Wait until all pages are indexed. Step 2 – Run chatbot Deploy the chatbot workflow. Set the same Pinecone index and namespace. Copy the chat webhook URL. Connect it to a website, chat widget, or WhatsApp bot. Start chatting with your AI assistant. Requirements Firecrawl account OpenAI API key Pinecone account and index Public website to crawl Optional: frontend chat interface Good to know The chatbot never answers from memory for business data. All company knowledge comes from Pinecone. If Pinecone returns nothing, the bot fails safely. HTML cleaning is basic and can be replaced with: Mozilla Readability Jina Reader Unstructured Chunk size and overlap affect retrieval quality. Pinecone can be replaced with: Qdrant Weaviate Supabase Vector Chroma Customising this workflow You can extend this system by: Adding PDF or document loaders Scheduling ingestion daily or weekly Connecting CRM or ticketing systems Adding appointment booking tools Switching to local or open-source models Adding multilingual support Storing raw content in a database Adding feedback or logging What this n8n template demonstrates Real-world RAG architecture Web crawling pipelines Text chunking strategies Vector database integration AI agent orchestration Memory-controlled conversations Production-grade AI support systems End-to-end AI infrastructure with n8n Architecture overview This template follows a modern AI system design: Website → Ingestion → Embeddings → Pinecone → Retrieval → OpenAI → User It separates: Data preparation (offline) Knowledge storage Runtime inference This makes the system scalable, maintainable, and safe for production use. Need a custom setup? If you want a similar AI system built for your business (custom data sources, CRM integration, WhatsApp bots, booking systems, dashboards, or private deployments), feel free to reach out at dinakars2003@gmail.com. I help companies design and deploy production-ready AI workflows.

Who is this for? This workflow is ideal for Gmail users and teams who receive a high volume of emails and want to streamline inbox management. It suits professionals seeking to organize messages automatically, including sales teams, project managers, support staff, and anyone who benefits from automated email categorization. What problem is this workflow solving? / Use case Manually labeling emails is time-consuming and can lead to inconsistent organization. This automated n8n workflow uses Gmail and OpenAI to analyze incoming messages and apply the appropriate labels, such as "Quotation", "Inquiry", "Project progress", and "Notification", based on content—improving productivity and ensuring important messages are prioritized. What this workflow does The workflow retrieves new Gmail messages, analyzes their content with OpenAI, and automatically assigns pre-defined Gmail labels that match the email’s intent. This ensures emails are sorted efficiently using AI-powered content analysis and Gmail’s labeling system. Setup Ensure Gmail labels (e.g., "Quotation", "Inquiry") are created in your Gmail account. Connect your Gmail and OpenAI accounts as credentials in n8n. Import the workflow into your n8n instance and update node configurations to match your Gmail label names. How to customize this workflow to your needs Edit or add Gmail labels both in your Gmail account and within the workflow logic. Adjust the prompt or parameters sent to OpenAI to better match your categorization style. Expand or refine the list of label categories to fit your team’s or business’s requirements.

How it works: Accesses a target website, searches for new PDFs, and downloads them automatically. Extracts content from each PDF and sends it to an AI for summarization. Delivers the AI-generated summary directly to a Discord channel. Marks processed URLs in Google Sheets to avoid duplicates. Set up steps: Configure the website URL in the HTTP Request node. Connect to Google Cloud API (enable Drive & Sheets) and link your spreadsheet. Set up an OpenRouter API key and choose your preferred AI model. Create a Discord webhook for notifications.

📘 Description This workflow analyzes real-time stock market sentiment and intent from public social media discussions and converts those signals into operations-ready actions. It exposes a webhook endpoint where a stock-market–related query can be submitted (for example, a stock, sector, index, or market event). The workflow then scans Twitter/X and Instagram for recent public discussions that indicate buying interest, selling pressure, fear, uncertainty, or emerging opportunities. An AI agent classifies each signal by intent type, sentiment, urgency, and strength. These insights are transformed into a prioritized Asana task for market or research teams and a concise Slack alert for leadership visibility. Built-in validation and error handling ensure reliable execution and fast debugging. This automation removes the need for manual social monitoring while keeping teams informed of emerging market risks and opportunities. ⚠️ Deployment Disclaimer This template is designed for self-hosted n8n installations only. It relies on external MCP tools and custom AI orchestration that are not supported on n8n Cloud. ⚙️ What This Workflow Does (Step-by-Step) 🌐 Receive Stock Market Query (Webhook Trigger) Accepts an external POST request containing a stock market query. 🧾 Extract Stock Market Query from Payload Normalizes and prepares the query for analysis. 🔎 Analyze Social Media for Stock Market Intent (AI) Scans public Twitter/X and Instagram posts to detect actionable market intent signals. 📡 Social Intelligence Data Fetch (MCP Tool) Retrieves relevant social data from external intelligence sources. 🧠 Transform Market Intent Signals into Ops-Ready Actions (AI) Structures insights into priorities, summaries, and recommended actions. 🧹 Parse Structured Ops Payload Validates and safely parses AI-generated JSON for downstream use. 📋 Create Asana Task for Market Signal Review Creates a prioritized task with key signals, context, and recommendations. 📣 Send Market Risk & Sentiment Alert to Slack Delivers an executive-friendly alert summarizing risks or opportunities. 🚨 Error Handler → Slack Alert Posts detailed error information if any workflow step fails. 🧩 Prerequisites • Self-hosted n8n instance • OpenAI and Azure OpenAI API credentials • MCP (Xpoz) social intelligence credentials • Asana OAuth credentials • Slack API credentials 🛠 Setup Instructions Deploy the workflow on a self-hosted n8n instance Configure the webhook endpoint and test with a sample query Connect OpenAI, Azure OpenAI, MCP, Asana, and Slack credentials Set the correct Asana workspace and project ID Select the Slack channel for alerts 🛠 Customization Tips • Adjust intent and sentiment classification rules in AI prompts • Modify task priority logic or due-date rules • Extend outputs to email reports or dashboards if required 💡 Key Benefits ✔ Real-time market sentiment detection from social media ✔ Converts unstructured signals into actionable tasks ✔ Provides leadership-ready Slack alerts ✔ Eliminates manual market monitoring ✔ Built-in validation and error visibility 👥 Perfect For Market research teams Investment and strategy teams Operations and risk teams Founders and analysts tracking market sentiment

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Who's it for Content creators, marketers, and researchers who need to monitor multiple RSS feeds and get AI-generated summaries without manual work. How it works This workflow automatically monitors RSS feeds, filters new articles from the last X days, checks for duplicates, and generates structured AI summaries. It fetches full article content, converts HTML to markdown, and uses Gemini AI to create consistent summaries with quick takeaways, key points, and practical insights. All data is saved to Google Sheets for easy access and sharing. The system processes RSS feeds in batches, ensuring no duplicate articles are processed twice by checking existing URLs in your Google Sheets. Each new article gets a comprehensive AI summary that includes the main message, key takeaways, important points, and practical applications. Requirements Google Sheets access OpenRouter API key for Gemini AI model or other language model RSS feed URLs to monitor How to set up Copy the template Google Sheet, add your RSS feeds in the "RSS FEEDS" tab, configure Google Sheets and OpenRouter credentials in n8n, and adjust the time filter in the Settings node. The workflow can run manually or on schedule every hour. How to customize Modify AI prompts for different summary styles, change the time filter duration, add more data fields to Google Sheets, or switch to a different AI model in the LLM Chat Model node.

The Scrape and Analyze Amazon Product Info with Decodo + OpenAI workflow automates the process of extracting product information from an Amazon product page and transforming it into meaningful insights. The workflow then uses OpenAI to generate descriptive summaries, competitive positioning insights, and structured analytical output based on the extracted information. Disclaimer Please note - This workflow is only available on n8n self-hosted as it’s making use of the community node for the Decodo Web Scraping Who this is for? This workflow is ideal for: E-commerce product researchers Marketplace sellers (Amazon, Flipkart, Shopify, etc.) Competitive intelligence teams Product comparison bloggers and reviewers Pricing and product analytics engineers Automation builders needing AI-powered product insights What problem is this workflow solving? Manually extracting Amazon product details, ads, pricing, reviews, and competitive signals is: Time-consuming Requires switching across tools Difficult to analyze at scale Not structured for reporting Hard to compare products objectively This workflow automates: Web scraping of Amazon product pages Extraction of product features and ad listings AI-generated product summaries Competitive positioning analysis Generation of structured product insight output Export to Google Sheets for tracking and reporting What this workflow does This workflow performs an end-to-end product intelligence pipeline, including: Data Collection Scrapes an Amazon product page using Decodo Retrieves product details and advertisement placements Data Extraction Extracts: Product specs Key feature descriptions Ads data Supplemental metadata AI-Driven Analysis Generates: Descriptive product summary Competitive positioning insights Structured product insight schema Data Consolidation Merges descriptive, analytical, and structured outputs Export & Persistence Aggregates results Writes final dataset to Google Sheets for: tracking comparison reporting product research archives Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n instance** Decodo API credentials** OpenAI API credentials** Make sure to install the Decodo Community Node. Required Credentials Decodo API Go to Credentials Add Decodo API Enter API key Save as: Decodo Credentials account OpenAI API Go to Credentials Select OpenAI Enter API key Save as: OpenAi account Google Sheets Add Google Sheets OAuth Authorize via Google Save as desired account Inputs to configure Modify in Set the Input Fields node: product_url = https://www.amazon.in/Sony-DualSense-Controller-Grey-PlayStation/dp/B0BQXZ11B8 How to customize this workflow to your needs You can easily adapt this workflow for various use cases. Change the product being analyzed Modify: product_url Change AI model In OpenAI nodes: Replace gpt-4.1-mini Use Gemini, Claude, Mistral, Groq (if supported) Customize the insight schema Edit Product Insights node to include: sustainability markers sentiment extraction pricing bands safety compliance brand comparisons Expand data extraction You may extract: product reviews FAQs Q&A seller information delivery and logistics signals Change output destination Replace Google Sheets with: PostgreSQL MySQL Notion Slack Airtable Webhook delivery CSV export Turn it into a batch processor Loop over: multiple ASINs category listings search results pages Summary This workflow provides a complete automated product intelligence engine, combining Decodo’s scraping capabilities with OpenAI’s analytical reasoning to transform Amazon product pages into structured insights, competitive analysis, and summarized evaluations automatically stored for reporting and comparison.

SSL Expiry Alert System Who is this for? This workflow is ideal for administrators or IT professionals responsible for monitoring SSL certificates of multiple websites to ensure they do not expire unexpectedly. Problem SSL certificates play a crucial role in ensuring secure communication over the internet. However, if not monitored closely, they can expire, leading to potential security risks and service disruption. This workflow helps in proactively monitoring SSL certificate expiry dates. Functionality Pulls URLs to monitor from a Google Sheet. Checks SSL certificates using SSL-Checker.io. Updates Google Sheet with SSL details such as expiry date and certificate status. Sends email alerts for SSL certificates nearing expiry (<30 days) or invalid certificates. Setup Clone the provided Google Sheet and update the Google Sheet URL in the "URLs to Monitor" node. Set up Google Sheets and Gmail credentials in n8n. Configure the Discourse Trigger for weekly monitoring. Customize email/telegram/ntfy alert settings as needed. Customization Modify the frequency of monitoring by adjusting the trigger interval in the "Weekly Trigger" node. Customize email content and recipients in the "Send Alert Email" node. Extend functionality by adding additional checks or actions based on SSL certificate status. Note Ensure proper authentication and authorization for accessing Google Sheets, SSL-Checker.io, and Gmail accounts within the workflow.

How it works: This project creates a personal AI knowledge assistant that operates through Telegram. The assistant can extract summaries from YouTube videos or online articles, store them in Google Sheets for later reference, and retrieve stored summaries when requested by the user. Step-by-step: Google Sheets Trigger:* The workflow starts by detecting a new YouTube or article URL added to a dedicated sheet (Sheet2*). It checks whether the link is already processed. Link Type Detection:** The system identifies if the URL is from YouTube or a standard article. Data Retrieval:** If it’s YouTube: Uses Apify to fetch the transcript. If it’s an article: Uses an HTTP Request node to fetch the webpage content. AI Summarization:* The transcript or article content is passed to *Google Gemini** for refined summarization. Google Sheets Storage:* The summary and title are appended to another sheet (Sheet1*) for long-term storage, along with a “Stored” status update in Sheet2. Telegram Assistant:** A Telegram Trigger listens for messages from the user. The assistant searches stored summaries in Google Sheets. If a match is found, it returns the result to the user on Telegram; otherwise, it politely apologizes.

This n8n workflow shows an easy way to automate the creation of social media assets using AI and a service like BannerBear. Designed for the busy marketer, leveraging AI image generation capabilities can help cut down production times and allow reinvesting into higher quality content. How it works This workflow generates social media banners for online events. Using a form trigger, a user can define the banner text and suggest an image to be generated. This request is passed to OpenAI's Dalle-3 image generation service to produce a relevant graphic for the event banner. This generated image is uploaded and sent to BannerBear where a template will use it and the rest of the form data to produce the banner. BannerBear returns the final banner which can now be used in an assortment of posts and publications. Requirements A BannerBear.com account and template is required An OpenAI account to use the Dalle-3 service. Customising the workflow We've only shown a small section of what BannerBear has to offer. With experimentation and other asset generating services such as AI audio and video, you should be able to generate more than just static banners!

This workflow helps you keep an eye on your GitHub forks, notifying you when they fall behind or pull ahead of their upstream repositories. How It Works Fetches All Your Repos: The workflow starts by grabbing a list of all repositories owned by your GitHub account. Filters for Forks: It then intelligently filters this list to identify only your forked repositories. Compares Branches: For each identified fork, it compares its default branch against the upstream repository's default branch to find out how many commits it's ahead or behind. Filters for Changes: Only forks that are either ahead or behind their upstream (i.e., not perfectly in sync) are processed further. Generates Report: A concise, well-formatted report is compiled, highlighting the status and commit differences for each relevant fork. Sends Telegram Notification: Finally, this report is sent directly to your Telegram chat, keeping you informed in real-time. Setup Steps Copy the template Update triggers ( optional ) Update the credentials Prerequisites GitHub Credentials**: You'll need to provide your GitHub personal access token for the "Get All Repositories" and "Compare Branches API Call" nodes. Telegram Bot Setup**: Configure a Telegram Bot and obtain its API token and your chat ID for the "Send Report" node. Github Owner Username**: Update the "Get All Repositories" node with the GitHub username of the repository owner whose forks you want to monitor. Explore & Fine-Tune: All detailed instructions and explanations, including how to adjust the filtering logic or output formatting, are provided in sticky notes directly within the workflow canvas.