Workflow Templates

Shopify Collections to AI Blog Automation Pipeline This Shopify AI automation is an advanced n8n-powered workflow that transforms Shopify product collections into SEO-optimized blog articles with images, while maintaining full visibility and control through Google Sheets. It combines Shopify APIs, Google Sheets, AI research agents, AI content generation, and AI image creation to automate the entire collection-to-content lifecycle — from detecting collections to publishing blogs. Built for scalable ecommerce content automation, this workflow is ideal for stores with large or growing catalogs that want consistent, high-quality SEO content without manual effort. 🚀 Features 🗂️ Automatic Collection Tracking — Captures both existing and newly created Shopify collections 📊 Google Sheets as Control Center — Centralized tracking with clear statuses for every collection 🧠 AI-Powered Collection Research — Buyer intent, pain points, use cases, and SEO insights ✍️ Long-Form Blog Generation — Conversion-focused, structured blog articles in HTML 🖼️ AI Image Generation — Creates and uploads collection-specific images to Shopify 🛍️ Automated Blog Publishing — Publishes articles to Shopify and stores live URLs 🔁 Fully Auditable Workflow — Every step is logged and updated back into Google Sheets 🧩 Workflow Preparation Before running the workflow: Ensure Shopify Admin API access is enabled Prepare a Google Sheet with required columns (id, title, handle, description, status, etc.) Decide your content workflow statuses (pending, generated, sent for approval, posted) Create or identify the Shopify blog where articles will be published This setup allows both manual control and fully automated execution. ⚙️ How It Works The workflow supports multiple triggers and follows a structured, production-safe pipeline. Notes: You can run this workflow manually, schedule it, or let it react automatically to new Shopify collections. 🔄 Step-by-Step Process Step 1: Collect Shopify Collection Data Fetches all existing collections via Shopify GraphQL Listens for newly created collections via Shopify trigger Normalizes collection data (ID, title, handle, description, updated time) Stores everything in Google Sheets with clear type labels Step 2: Select Pending Collections Filters collections marked as pending Processes items in controlled batches to avoid API limits Ensures safe and repeatable execution Step 3: AI Research & Buyer Intent Analysis AI analyzes each collection from a buyer and SEO perspective Identifies problems, motivations, objections, and search intent Outputs structured research JSON for downstream use Step 4: AI Blog Content Generation Converts research into long-form, conversion-focused blog articles Generates titles, sections, FAQs, tags, and image prompts Outputs Shopify-ready HTML content Step 5: AI Image Generation & Shopify Upload Generates collection images using AI Uploads images to Shopify using staged uploads Retrieves CDN URLs and maps them back to content Step 6: Publish Blog & Update Sheet Publishes the final article to the Shopify blog Writes the live article URL back to Google Sheets Updates status to reflect completion 🛠️ n8n Nodes Used Manual Trigger / Schedule Trigger / Shopify Trigger Shopify (GraphQL + REST via HTTP Request) Google Sheets AI Agent Nodes (Research, Writing, Image Generation) IF / Switch Nodes (Status & logic handling) Split In Batches (Controlled processing) Code Nodes (HTML structuring and replacements) 🔐 Credentials Required Before running the workflow, configure the following credentials in n8n: Shopify Admin API Access Token Google Sheets OAuth Google Gemini API (text + image models) 👤 Ideal For This workflow is ideal for: Shopify stores with many product collections Ecommerce teams scaling SEO content production Agencies building Shopify content automation systems Businesses replacing manual blog writing with AI-driven workflows 💬 Extensibility This workflow is modular and easy to extend. You can add: Multi-language blog generation Internal linking automation Category-specific content logic Approval workflows before publishing Social or email promotion triggers after publishing 🔑 Keywords shopify ai workflow shopify blog automation shopify marketing automation shopify automation ecommerce automation how to automate shopify blog 📌 Notes No AI fine-tuning required Research-driven, not promotional AI writing Designed for accuracy, traceability, and scale Safe for production ecommerce environments

Who is this for? Content creators, media teams, and bloggers who run a YouTube channel and want to automatically repurpose video content into SEO-ready blog posts — without manual writing. Ideal for anyone publishing news or educational content in any language. What it does This workflow runs three fully automated pipelines that take a YouTube video all the way to a published WordPress post: Pipeline 1 — Transcript Collector runs on a schedule, fetches new videos from your YouTube playlist via the YouTube Data API, retrieves their full transcripts via RapidAPI, saves each transcript to a Google Doc, and logs metadata to Google Sheets. Pipeline 2 — AI Blog Generator picks up unprocessed transcripts, sends them to a Gemini AI Agent that reads the transcript and your existing published posts (for internal linking), then generates structured blog content: title, body (HTML), summary, tags, Telegram caption, image prompt, and publish priority. Results are saved to a second Google Sheet as pending. Pipeline 3 — Publisher runs every 3 hours, selects the highest-priority pending post (urgent > normal > evergreen), publishes it to WordPress, generates a featured image via an AI image API, uploads and attaches it to the post, then announces it to a Telegram channel. How to set up Import this workflow into n8n. Create two Google Sheets tabs: youtubeVideos and blogsAndNewsUploaded (column structures in the sticky notes). Configure all credentials: Google (Sheets, Docs), YouTube API key, RapidAPI key (youtube-transcript3), Gemini API, WordPress, Telegram Bot, and your AI image generation API. Set your YouTube Playlist ID in the first HTTP node. Set your Google Drive Folder ID for transcript storage. Activate all three schedule triggers independently. Requirements YouTube Data API v3 key (Google Cloud Console) RapidAPI subscription to youtube-transcript3 Google Gemini API key WordPress site with Application Password Telegram Bot token + channel AI image generation API (compatible with OpenAI images format) How to customize Adjust the Gemini system prompt in the AI Agent node to change content language, tone, or structure. Change publish_priority logic in the JS node to control posting frequency. Swap the image generation API with any provider (DALL-E, Stability AI, etc.). Add a Filter node before publishing to require manual approval of pending posts.

Automated Job Search & AI Relevance Evaluator Overview This n8n template automates the entire job hunting process by cross-referencing a candidate's PDF resume with live job listings from the JSearch API. It automatically filters for fresh, unapplied roles, uses Google Gemini AI to critically evaluate each job's relevance against the candidate's specific experience, and logs highly tailored matches directly into a Notion database for seamless tracking. 🚀 How it works Context & Extraction: The workflow fetches existing applications from your Notion database to prevent duplicate tracking, then reads and extracts plain text directly from a local PDF resume. Role Discovery: A Google Gemini node isolates the candidate's current job title to formulate a precise search query. This query is sent to the JSearch API (via RapidAPI) to pull live job listings. Smart Filtering: Natively filters out jobs posted more than 14 days ago and jobs that already exist in your Notion tracker, ensuring only fresh, unseen postings are processed. AI Evaluation: The core of the workflow! Google Gemini acts as an expert technical recruiter, comparing the candidate's resume against each job description. It generates a "Relevance Score" (1-100), a "Skill Match Score", extracts remote/salary info, and summarizes why the job is a good fit. Notion Logging: Structured insights for each matched role are formatted and pushed directly as a rich database page into your Notion tracking board. 🎮 How to use API Credentials: Add your Google Gemini API Key and your RapidAPI key (subscribed to the JSearch API) in their respective nodes. Notion Setup: Connect your Notion credential and update the two Notion nodes with your specific target Database ID. File Path: Update the File Selector to point to your PDF resume (e.g., /home/node/.n8n-files/My-Resume.pdf). Search Customization: Open the "Search for Jobs via RapidAPI" node to manually tweak your target location, industry keywords, or pagination limits. ⚙️ Requirements Google Gemini API Key RapidAPI Key (for JSearch API) Notion Account (with a pre-configured Job Tracker database) n8n Environment: Designed for self-hosted instances with local file access. 🎯 Use Cases Automated Job Hunting: Wake up to a pre-vetted, automatically scored list of highly relevant job openings perfectly matched to your exact resume. Recruiting Pipelines: Scale candidate sourcing by automatically comparing an inbound candidate's resume against thousands of active job board posts. Freelance Lead Generation: Independent contractors or agencies can use this to find companies actively hiring for the exact technical skills they offer.

Quick overview This workflow runs daily to pull recent community workflow templates from the n8n public catalog API, extract full workflow JSON, sanitize and scan it for potential exposed secrets, generate embeddings with Ollama, and upsert the resulting documents into a Qdrant vector collection for local retrieval. How it works Runs on a Schedule Trigger every day at 2:00 AM. Fetches the first two pages of templates from the n8n public template catalog search API and splits the returned workflows list into individual items. Retrieves the full template payload for each item from the n8n workflow template API. Builds a text document from the workflow metadata, sticky note content, system messages, and node configuration, sanitizes non-printable characters, and flags templates that match common API key/secret patterns. Deletes any existing Qdrant points whose metadata.template_id matches the templates being processed to prevent duplicate vectors. Generates embeddings with Ollama (nomic-embed-text:latest) and inserts the documents and metadata into the Qdrant n8n_templates collection. Setup Create a Qdrant collection named n8n_templates and add Qdrant credentials in n8n, ensuring the workflow can reach your Qdrant instance (the purge step calls http://qdrant:6333). Set up an Ollama instance with the nomic-embed-text:latest model available and add Ollama credentials in n8n. If your Qdrant host/port or collection name differs, update the purge request URL/body and the Qdrant Vector Store collection selection accordingly.

Structured Setup Guide: Narrative Chaining with N8N + AI 1. Input Setup Use a Google Sheet as the control panel. Fields required: Video URL (starting clip, ends with .mp4) Number of clips to extend (e.g., 2 extra scenes) Aspect ratio (horizontal, vertical, etc.) Model (V3 or V3 Fast) Narrative theme (guidance for story flow) Special requests (scene-by-scene instructions) Status column (e.g., "For Production", "Done") 👉 Example scene inputs: Scene 1: Naruto walks out with ramen is his hands Scene 2: Joker joins with chips 2. Workflow in N8N Step 1: Fetch Input Get rows in sheet → fetch the next row where status = For Production. Clear sheet 2 → reset the sheet that stores generated scenes. Edit fields (Initial Values): Video URL = starting clip Step = 1 Complete = total number of scenes requested Step 2: Looping Logic Looper Node: Runs until step = complete. Carries over current video URL → feeds into next generation. Step 3: Analyze Current Clip Send video URL to File.AI Video Understanding API. Request: Describe last frame + audio + scene details. Output: Detailed video analysis text. Step 4: Generate Prompt AI Agent creates the next scene prompt using: Context from video analysis Narrative theme (from sheet) Scene instructions (from sheet) Aspect ratio, model preference, etc. 👉 Output = video prompt for next scene Step 5: Extract Last Frame Call File.AI Extract Frame API. Parameters: Input video URL Frame = last Output = JPG image (last frame of current clip). Step 6: Generate New Scene Use Key.AI (V3 Fast) for economical video generation. POST request includes: Prompt (from AI Agent) Aspect ratio + model Image URL (last frame) → ensures seamless chaining Wait for generation to complete. 👉 Output = New clip URL (MP4) Step 7: Store & Increment Log new clip URL into Sheet 2. Increment Step by +1. Replace Video URL with the new clip. Loop back if Step < Complete. 3. Output Section Once all clips are generated: Gather all scene URLs from Sheet 2. Use File.AI Merge Videos API to stitch clips together: Original clip + all generated scenes. Save final MP4 output. Update Sheet 1 row with: Final video URL Status = Done 4. Costs Video analysis: ~$0.015 per 8s clip Frame extraction: ~0.002¢ (almost free) Clip merging: negligible (via ffmpeg backend) V3 Fast video generation (Key.AI): ~$0.30 per 8s clip

Product Video Creator with Nano Banana & Veo 3.1 via Telegram Who's it for This workflow is perfect for: E-commerce sellers needing quick product videos Social media marketers creating content at scale Small business owners without video editing skills Product photographers enhancing their offerings Anyone selling on Instagram, TikTok, or mobile-first platforms What it does Transform basic product photos into professional marketing videos in under 2 minutes: Send a product photo to your Telegram bot Nano Banana analyzes and enhances your image with studio-quality lighting Veo 3.1 generates an 8-second vertical video with motion and audio Receive your scroll-stopping marketing video automatically Perfect for creating engaging vertical content without expensive tools or editing expertise. How it works Input → User sends product photo via Telegram with optional caption AI Analysis → Nano Banana analyzes product and generates detailed enhancement prompt Image Enhancement → Nano Banana creates commercial-grade photo (9:16, studio lighting) Video Generation → Veo 3.1 creates 8-second 1080p video with motion and audio Delivery → Auto-polls status every 30s, delivers final video to Telegram Requirements Google Cloud Platform Vertex AI API** enabled for Veo 3.1 Generative Language API** enabled for Nano Banana OAuth2 credentials Get credentials from Google Cloud Console Telegram Bot token from @BotFather n8n Self-hosted or cloud instance Setup Import workflow JSON into n8n Add credentials: Telegram API (bot token) Google OAuth2 API (client id and secret) Google PaLM API (API key) Update your Project ID in both Veo 3.1 nodes Activate workflow and test with a product photo How to customize Aspect Ratio: Choose 9:16 (vertical), 16:9 (horizontal) in "Generate Enhanced Image" and "Initiate veo 3.1" nodes Duration: Set 2 to 8 seconds by adjusting durationSeconds in "Initiate veo 3.1 Video Generation" Quality: Select 720p or 1080p by changing resolution in "Initiate veo 3.1 Video Generation" Audio: Enable or disable background music by toggling generateAudio in "Initiate veo 3.1 Video Generation" Enhancement Style: Match your brand aesthetic by editing the prompt in "AI Design Analysis" node Polling Time: Adjust retry interval by changing wait time in "Processing Delay (30s)" node Key Features 🔐 Direct Google APIs – No third-party services. Uses Nano Banana and Veo 3.1 directly via Google Cloud for maximum reliability and privacy ⚡ Fully Automated – Send photo, receive video. Zero manual work required 🎨 Studio Quality – Nano Banana delivers professional lighting, composition, and AI-powered color grading 📱 Mobile-First – Default 9:16 vertical format optimized for Instagram Reels, TikTok, and Stories 🔄 Smart Retry Logic – Automatically polls Veo 3.1 status every 30 seconds until video generation completes 🎵 Audio Included – Veo 3.1 generates background music automatically (can be disabled)

Description This workflow is a practical, “dirty” solution for real-world scenarios where frontline workers keep using Excel in their daily processes. Instead of forcing change, we take their spreadsheets as-is, clean and normalize the data, generate embeddings, and store everything in Supabase. The benefit: frontline staff continue with their familiar tools, while data analysts gain clean, structured, and vectorized data ready for analysis or RAG-style AI applications. How it works Frontline workers continue with Excel** – no disruption to their daily routines. Upload & trigger** – The workflow runs when a new Excel sheet is ready. Read Excel rows** – Data is pulled from the specified workbook and worksheet. Clean & normalize** – HTML is stripped, Excel dates are fixed, and text fields are standardized. Batch & switch** – Rows are split and routed into Question/Answer processing paths. Generate embeddings** – Cleaned Questions and Answers are converted into vectors via OpenAI. Merge enriched records** – Original business data is combined with embeddings. Write into Supabase** – Data lands in a structured table (excel_records) with vector and FTS indexes. Why it’s “dirty but useful” No disruption** – frontline workers don’t need to change how they work. Analyst-ready data** – Supabase holds clean, queryable data for dashboards, reporting, or AI pipelines. Bridge between old and new** – Excel remains the input, but the backend becomes modern and scalable. Incremental modernization** – paves the way for future workflow upgrades without blocking current work. Outcome Frontline workers keep their Excel-based workflows, while data can immediately be structured, searchable, and vectorized in Supabase — enabling AI-powered search, reporting, and retrieval-augmented generation. Required setup Supabase account Create a project and enable the pgvector extension. OpenAI API Key Required for generating embeddings (text-embedding-3-small). Microsoft Excel credentials Needed to connect to your workbook and worksheet. Need Help 🔗 LinkedIn – Wessel Bulte

Quick Overview This scheduled workflow audits Shopify product images with ShotEdit, uses Google Gemini to generate actionable fix notes, logs issues to Google Sheets, and posts a run summary to Slack. How it works Runs every Monday morning on a schedule. Retrieves a limited batch of products from Shopify and splits each product’s image list into one item per image. Sends each image URL to the ShotEdit Image Info API to read metadata (format, dimensions, file size) and return an optimization recommendation. Filters out images marked as OK so only images needing changes continue. Uses Google Gemini to convert the technical recommendation into a one-sentence fix instruction plus a priority. Appends or updates the finding in Google Sheets keyed by the image URL, then aggregates the run results and posts a summary message to a Slack channel. Setup Add credentials for Shopify (Admin API access token), Google Sheets, Slack, and Google Gemini. Update the audit parameters for your Google Sheet ID, sheet name, Slack channel, and the Shopify product limit. Create a Google Sheet tab matching the configured sheet name and include an image_url column so rows can be updated in place.

Cohere's new multimodal model releases make building your own Vision RAG agents a breeze. If you're new to Multimodal RAG and for the intent of this template, it means to embed and retrieve only document scans relevant to a query and then have a vision model read those scans to answer. The benefits being (1) the vision model doesn't need to keep all document scans in context (expensive) and (2) ability to query on graphical content such as charts, graphs and tables. How it works Page extracts from a technology report containing graphs and charts are downloaded, converted to base64 and embedded using Cohere's Embed v4 model. This produces embedding vectors which we will associate with the original page url and store them in our Qdrant vector store collection using the Qdrant community node. Our Vision RAG agent is split into 2 parts; one regular AI agent for chat and a second Q&A agent powered by Cohere's Command-A-vision model which is required to read contents of images. When a query requires access to the technology report, the Q&A agent branch is activated. This branch performs a vector search on our image embeddings and returns a list of matching image urls. These urls are then used as input for our vision model along with the user's original query. The Q&A vision agent can then reply to the user using the "respond to chat" node. Because both agents share the same memory space, it would be the same conversation to the user. How to use Ensure you have a Cohere account and sufficient credit to avoid rate limit or token usage restrictions. For embeddings, swap out the page extracts for your own. You may need to split and convert document pages to images if you want to use image embeddings. For chat, you may want to structure the agent(s) in another way which makes sense for your environment eg. using MCP servers. Requirements Cohere account for Embeddings and LLM Qdrant for vector store

Effortlessly generate, review, and publish SEO-optimized blog posts to WordPress using AI and automation. How It Works AI Topic Generation: Gemini suggests trending blog topics matching your agency's services. Content Research: Tavily fetches recent relevant articles for each generated topic. Human Review: Choose the preferred article for publishing through a Telegram notification. AI Rewriting: Gemini rewrites the selected article into a polished, SEO-friendly post. Image Generation & Publishing: The workflow creates a featured image with Gemini or OpenAI, then publishes the post (with dynamic categories and images) to WordPress. Audit Trail: Every published post is logged to Google Sheets, and final details are sent to Telegram. Set Up Steps Estimated setup time: 15–30 minutes (excluding API approval/wait times). Connect your WordPress, Gemini (Google), Tavily, Google Sheets, and Telegram accounts. Configure your preferred posting schedule in the “Schedule Trigger.” Adjust prompts or messages to fit your agency’s niche or editorial voice if needed. Note: Detailed customizations and advanced configuration tips are included in the sticky notes within the workflow.

Overview Healthcare organizations face significant challenges in digitizing and processing medical records while maintaining strict HIPAA compliance. This workflow provides a secure, automated solution for extracting clinical data from various medical documents including discharge summaries, lab reports, clinical notes, prescription records, and scanned medical images (JPG, PNG). What You Can Do Extract clinical data from medical documents while maintaining HIPAA compliance Process handwritten notes and scanned medical images with OCR Automatically identify and protect PHI (Protected Health Information) Generate structured data from various medical document formats Maintain audit trails for regulatory compliance Who It's For Healthcare providers, medical billing companies, clinical research organizations, health information exchanges, and medical practice administrators who need to digitize and extract data from medical records while maintaining HIPAA compliance. The Problem It Solves Manual medical record processing is time-consuming, error-prone, and creates compliance risks. Healthcare organizations struggle to extract structured data from handwritten notes, scanned documents, and various medical forms while protecting PHI. This template automates the extraction process while maintaining the highest security standards for Protected Health Information. Setup Instructions: Configure Google Drive credentials with proper medical record access controls Install the PDF Vector community node from the n8n marketplace Configure PDF Vector API credentials with HIPAA-compliant settings Set up secure database storage with encryption at rest Define PHI handling rules and extraction parameters Configure audit logging for regulatory compliance Set up integration with your Electronic Health Record (EHR) system Key Features: Secure retrieval of medical documents from Google Drive HIPAA-compliant processing with automatic PHI masking OCR support for handwritten notes and scanned medical images Automatic extraction of diagnoses with ICD-10 code validation Medication list processing with dosage and frequency information Lab results extraction with reference ranges and flagging Vital signs capture and normalization Complete audit trail for regulatory compliance Integration-ready format for EHR systems Customization Options: Define institution-specific medical terminology and abbreviations Configure automated alerts for critical lab values or abnormal results Set up custom extraction fields for specialized medical forms Implement medication interaction warnings and contraindication checks Add support for multiple languages and international medical coding systems Configure integration with specific EHR platforms (Epic, Cerner, etc.) Set up automated quality assurance checks and validation rules Implementation Details: The workflow uses advanced AI with medical domain knowledge to understand clinical terminology and extract relevant information while automatically identifying and protecting PHI. It processes various document formats including handwritten prescriptions, lab reports, discharge summaries, and clinical notes. The system maintains strict security protocols with encryption at rest and in transit, ensuring full HIPAA compliance throughout the processing pipeline. Note: This workflow uses the PDF Vector community node. Make sure to install it from the n8n community nodes collection before using this template.

Multi-Source RAG System with GPT-4 Turbo, News & Academic Papers Integration This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. How it works This workflow provides an enterprise-grade RAG (Retrieval-Augmented Generation) system that intelligently searches multiple sources and generates AI-powered responses using GPT-4 Turbo. Key Steps Form Input - Collects user queries with customizable search scope, response style, and language preferences Intelligent Search - Routes queries to appropriate sources (web, academic papers, news, internal documents) Data Aggregation - Unifies and processes information from multiple sources with quality scoring AI Processing - Uses GPT-4 Turbo to generate context-aware, source-grounded responses Response Enhancement - Formats outputs in various styles (comprehensive, concise, technical, etc.) Multi-Channel Delivery - Delivers results via webhook, email, Slack, and optional PDF generation Data Sources & AI Models Search Sources Web Search**: Google, Bing, DuckDuckGo integration Academic Papers**: arXiv, PubMed, Google Scholar News Articles**: News API, RSS feeds, real-time news Technical Documentation**: GitHub, Stack Overflow, documentation sites Internal Knowledge**: Google Drive, Confluence, Notion integration AI Models GPT-4 Turbo**: Primary language model for response generation Embedding Models**: For semantic search and similarity matching Custom Prompts**: Specialized prompts for different response styles Set up steps Setup time: 15-20 minutes Configure API credentials - Set up OpenAI API, ScrapeGraphAI, Google Drive, and other service credentials Set up search sources - Configure academic databases, news APIs, and internal knowledge sources Connect analytics - Link Google Sheets for usage tracking and performance monitoring Configure notifications - Set up Slack channels and email templates for automated alerts Test the workflow - Run sample queries to verify all components are working correctly Keep detailed configuration notes in sticky notes inside your workflow