Workflow Templates

Shopify AI Automation Image-to-Product CSV Bulk Upload Automation This Shopify AI automation is an advanced n8n-powered workflow that converts raw product images into a Shopify-ready product CSV. It uses AI image analysis, Google Drive, Google Sheets, and Shopify APIs to fully automate product onboarding — from images to structured ecommerce data. Built for scalable ecommerce automation, this workflow is especially effective for image-first catalogs such as jewelry, fashion, and accessories. 🚀 Features 🖼️ AI Image Analysis — Analyzes product images one by one for higher accuracy and lower risk 🧠 Automatic Category Detection — Identifies main product category (e.g. Jewelry), easily customizable for any niche ✍️ AI Product Content Generation — Creates product names, descriptions (HTML), tags, and attributes 📄 Google Sheets Orchestration — Structures data and outputs a clean Shopify-compatible CSV 🛍️ Shopify Asset Upload — Uploads images to Shopify and retrieves CDN URLs 🧩 Workflow Preparation Before running the workflow: Upload all product images to Google Drive Name images using the format: <SKU><ColorCode> Example: 12345GR Place all images inside a folder named:<Brand Name> Root folder name : pending Example : Google_Drive/pending/Manish Collection/All Images Each image represents one product variant. ⚙️ How It Works The workflow follows a 6-step automation pipeline designed for reliability and scalability. Notes : You may connect all these step to make it fully automatic or shecdule it according to your suitable time. 🔄 Step-by-Step Process Step 1: Fetch Images from Google Drive Scans the pending/<brand_name> folder Fetches all images Extracts SKU and color code Stores references in Google Sheets Step 2: AI Image Analysis (One-by-One) Images are analyzed individually Slower than batch processing, but far more reliable Reduces hallucinations and incorrect attributes Ideal for production-grade Shopify automation. Step 3: Main Category Identification AI determines the primary product category (example: Jewelry) Prompts can be modified for any ecommerce niche Step 4: Conditional Product Content Generation Based on category: Product titles are generated Descriptions are written in Shopify-ready HTML Tags and attributes are created This replaces repetitive work typically handled via Shopify Flow or manual data entry. Step 5: Shopify Image Upload Images are uploaded to Shopify assets Shopify returns CDN URLs URLs are mapped back to product data Step 6: Shopify CSV Generation All enriched data is compiled into a new Google Sheet Output matches Shopify’s product import CSV format File is ready for bulk upload 🛠️ n8n Nodes Used Trigger Node (Manual / Schedule) Google Drive Node Google Sheets Node AI Agent Node (Image Analysis + Content) Switch Node (Category-based logic) Code Node (Formatting & CSV structure) Shopify Node / HTTP Node 🔐 Credentials Required Before running the workflow, configure the following credentials in n8n: Shopify Access Token** — For asset uploads and API calls AI Provider API Key** — For image analysis and content generation Google Drive OAuth** — To access product images Google Sheets OAuth** — To store and export data 👤 Ideal For This workflow is ideal for: Shopify store owners handling bulk product uploads Ecommerce teams managing image-heavy catalogs Agencies building scalable Shopify automation systems Anyone exploring how to automate Shopify product onboarding 💬 Extensibility This workflow is modular and easy to extend. You can add: Multi-language product descriptions Pricing and margin automation Shopify marketing automation triggers Shopify Flow integrations after product import Marketplace exports (Google Shopping, Meta, Amazon) 🔑 Keywords shopify ai shopify flow shopify marketing automation shopify automation ecommerce automation how to automate shopify 📌 Notes No AI fine-tuning required No fragile prompt chaining Designed for accuracy over speed Safe for production ecommerce workflows 📞 Support If you’re looking to customize or extend this workflow, feel free to reach out or fork the project. Happy automating 🚀

Rename Workflow Nodes with AI for Clarity This workflow automates the tedious process of renaming nodes in your n8n workflows. Instead of manually editing each node, it uses an AI language model to analyze its function and assign a concise, descriptive new name. This ensures your workflows are clean, readable, and easy to maintain. Who's it for? This template is perfect for n8n developers and power users who build complex workflows. If you often find yourself struggling to understand the purpose of different nodes at a glance or spend too much time manually renaming them for documentation, this tool will save you significant time and effort. How it works / What it does The workflow operates in a simple, automated sequence: Configure Suffix: A "Set" node at the beginning allows you to easily define the suffix that will be appended to the new workflow's name (e.g., "- new node names"). Fetch Workflow: It then fetches the JSON data of a specified n8n workflow using its ID. AI-Powered Renaming: The workflow's JSON is sent to an AI model (like Google Gemini or Anthropic Claude), which has been prompted to act as an n8n expert. The AI analyzes the type and parameters of each node to understand its function. Generate New Names: Based on this analysis, the AI proposes new, meaningful names and returns them in a structured JSON format. Update and Recreate: A Code Node processes these suggestions, updates all node names, and correctly rebuilds the connections and expressions. Create & Activate New Workflow: Finally, it creates a new workflow with the updated name, deactivates the original to avoid confusion, and activates the new version.

YouTube Chapter Auto-Description with AI This n8n template automatically adds structured timestamp chapters to your latest YouTube video’s description using your RSS feed, SupaData for transcript extraction, and an AI tool for chapter generation. Ideal for creators who want every video to include chapter markers without doing it manually. Good to Know SupaData extracts full transcripts from YouTube videos via URL. The AI chapter generator converts long transcripts into formatted timestamps with short titles. This workflow edits the existing video description and appends the chapters to the bottom. How It Works The RSS Feed Trigger detects new uploads from your YouTube channel. The workflow checks Airtable to prevent duplicate processing. Transcript is fetched using SupaData API. The total video duration is extracted from the transcript. AI is prompted to generate well-formatted chapter timestamps. The existing description is fetched from YouTube. The chapters are appended and pushed back via the YouTube API. How to Use Start with the Manual Trigger to test the setup. Replace it with the RSS Trigger once you're ready for automation. Chapters are added only if the video hasn't been processed before. Requirements YouTube OAuth2** credentials in n8n SupaData API Key** Airtable account** (for optional deduplication logic) Customizing This Workflow Change the chapter format, or instruct the AI to use emojis, bold titles, or include sections like "sponsor" or "Q&A". Replace the RSS Trigger with a webhook if using a different publishing process.

AI-Powered Chart Generation from Web Data This n8n workflow automates the process of: Scraping real-time data from the web using GPT-4o with browsing capability Converting markdown tables into Chart.js-compatible JSON Rendering the chart using QuickChart.io Uploading the resulting image directly to your WordPress media library 🚀 Use Case Ideal for content creators, analysts, or automation engineers who need to: Automate generation of visual reports Create marketing-ready charts from live data Streamline research-to-publish workflows 🧠 How It Works 1. Prompt Input Trigger the workflow manually or via another workflow with a prompt string, e.g.: Generate a graph of apple's market share in the mobile phone market in Q1 2025 2. Web Search + Table Extraction The Message a model node uses GPT-4o with search to: Perform a real-time query Extract data into a markdown table Return the raw table + citation URLs 3. Chart Generation via AI Agent The Generate Chart AI Agent: Interprets the table Picks an appropriate chart type (bar, line, doughnut, etc.) Outputs valid Chart.js JSON using a strict schema 4. QuickChart API Integration The Create QuickChart node: Sends the Chart.js config to QuickChart.io Renders the chart into a PNG image 5. WordPress Image Upload The Upload image node: Uploads the PNG to your WordPress media library using REST API Uses proper headers for filename and content-type Returns the media GUID and full image URL 🧩 Nodes Used Manual Trigger or Execute Workflow Trigger OpenAI Chat Model (GPT-4o) LangChain Agent (Chart Generator) LangChain OutputParserStructured HTTP Request (QuickChart API + WordPress Upload) Code (Final result formatting) 🗂 Output Format The final Code node returns: { "research": { ...raw markdown table + citations... }, "graph_data": { ...Chart.js JSON... }, "graph_image": { ...WordPress upload metadata... }, "result_image_url": "https://your-wordpress.com/wp-content/uploads/...png" } ⚙️ Requirements OpenAI credentials (GPT-4o or GPT-4o-mini) WordPress REST API credentials with media write access QuickChart.io (free tier works) n8n v1.25+ recommended 📌 Notes Chart style and format are determined dynamically based on your table structure and AI interpretation. Make sure your OpenAI and WordPress credentials are connected properly. Outputs are schema-validated to ensure reliable rendering. 🖼 Sample Output

This workflow turns Slack into your content control hub and automates the full blog creation pipeline — from sourcing trending headlines, validating topics, drafting posts, and preparing content for your CMS. With one command in Slack, you can source news from RSS feeds, refine them with Gemini AI, generate high-quality blog posts, and get publish-ready output — all inside a single n8n workflow. ⸻ ⚙️ How It Works 1.Trigger in Slack Type start in a Slack channel to fetch trending headlines. Headlines are pulled from your configured RSS feeds. 2.Topic Generation (Gemini AI) Gemini rewrites RSS headlines into unique, non-duplicate topics. Slack displays these topics in a numbered list (e.g., reply with 2 to pick topic 2). 3.Content Validation When you reply with a number, Gemini validates and slightly rewrites the topic to ensure originality. Slack confirms the selected topic back to you. 4.Content Creation Gemini generates a LinkedIn/blog-style draft: Strong hook introduction 3–5 bullet insights A closing takeaway and CTA Optionally suggests asset ideas (e.g., image, infographic). 5.CMS-Ready Output Final draft is structured for publishing (markdown or plain text). You can expand this workflow to automatically send the output to your CMS (WordPress, Ghost, Notion, etc.). ⸻ 🛠 Setup Instructions Connect your Slack Bot to n8n. Configure your RSS Read nodes with feeds relevant to your niche. Add your Gemini API credentials in the AI node. Run the workflow: Type start in Slack → see trending topics. Reply with a number (e.g., gen 3) → get a generated blog draft in the same Slack thread. ⸻ 🎛 Customization Options • Change RSS sources to match your industry. • Adjust Gemini prompts for tone (educational, casual, professional). • Add moderation filters (skip sensitive or irrelevant topics). • Connect the final output step to your CMS, Notion, or Google Docs for publishing. ⸻ ✅ Why Use This Workflow? • One-stop flow: Sourcing → Validation → Writing → Publishing. • Hands-free control: Everything happens from Slack. • Flexible: Easily switch feeds, tone, or target CMS. • Scalable: Extend to newsletters, social posts, or knowledge bases.

Generate GitHub Release Notes with AI Automatically generate GitHub release notes using AI. This workflow compares your latest two GitHub releases, summarises the changes, and produces a clean, ready-to-paste changelog entry. It’s ideal for automating GitHub Releases, versioning workflows, and keeping your documentation or CHANGELOG.md up to date without manual editing. What this workflow does Listens for newly published GitHub Releases. Fetches and compares the latest two GitHub release versions. Uses an AI Chat Model to summarise changes and generate structured release notes. Outputs clean, reusable release note content for GitHub, documentation, or CI/CD pipelines. How it works GitHub Trigger detects a new published release. Release detail nodes extract the latest tag, body, and repository metadata. Comparison logic fetches the previous release and prepares a diff. Chat Model nodes (via OpenRouter) generate both a summary and a final, formatted release note. Requirements / Connections GitHub OAuth credential configured in n8n. OpenRouter API key connected to the Chat Model nodes. Setup instructions Import the template. Select your GitHub OAuth connection in all GitHub nodes. Add your OpenRouter credential to the Chat Model nodes. (Optional) Adjust the AI prompts to customise tone or formatting. Output The workflow produces: A concise summary of differences between the last two GitHub releases. A polished AI-generated GitHub release note ready to publish. Customisation ideas Push generated notes directly into a CHANGELOG.md or documentation repo. Send release summaries to Slack or Teams. Include commit messages, PR titles, or labels for deeper analysis.

Move beyond generic AI-generated content and create articles that are high-quality, factually reliable, and aligned with your unique expertise. This template orchestrates a sophisticated "research-first" content creation process. Instead of simply asking an AI to write an article from scratch, it first uses an AI planner to break your topic down into logical sub-questions. It then queries a Super assistant—which you've connected to your own trusted knowledge sources like Notion, Google Drive, or PDFs—to build a comprehensive research brief. Only then is this fact-checked brief handed to a powerful AI writer to compose the final article, complete with source links. This is the ultimate workflow for scaling expert-level content creation. Who is this for? Content marketers & SEO specialists:** Scale the creation of authoritative, expert-level blog posts that are grounded in factual, source-based information. Technical writers & subject matter experts:** Transform your complex internal documentation into accessible public-facing articles, tutorials, and guides. Marketing agencies:** Quickly generate high-quality, well-researched drafts for clients by connecting the workflow to their provided brand and product materials. What problem does this solve? Reduces AI "hallucinations":** By grounding the entire writing process in your own trusted knowledge base, the AI generates content based on facts you provide, not on potentially incorrect information from its general training data. Ensures comprehensive topic coverage:** The initial AI-powered "topic breakdown" step acts like an expert outliner, ensuring the final article is well-structured and covers all key sub-topics. Automates source citation:** The workflow is designed to preserve and integrate source URLs from your knowledge base directly into the final article as hyperlinks, boosting credibility and saving you manual effort. Scales expert content creation:** It effectively mimics the workflow of a human expert (outline, research, consolidate, write) but in an automated, scalable, and incredibly fast way. How it works This workflow follows a sophisticated, multi-step process to ensure the highest quality output: Decomposition: You provide an article title and guidelines via the built-in form. An initial AI call then acts as a "planner," breaking down the main topic into an array of 5-8 logical sub-questions. Fact-based research (RAG): The workflow loops through each of these sub-questions and queries your Super assistant. This assistant, which you have pre-configured and connected to your own knowledge sources (Notion pages, Google Drive folders, PDFs, etc.), finds the relevant information and source links for each point. Consolidation: All the retrieved question-and-answer pairs are compiled into a single, comprehensive research brief. Final article generation: This complete, fact-checked brief is handed to a final, powerful AI writer (e.g., GPT-5). Its instructions are clear: write a high-quality article using only the provided information and integrate the source links as hyperlinks where appropriate. Implementing the template Set up your Super assistant (Prerequisite): First, go to Super, create an assistant, connect it to your knowledge sources (Notion, Drive, etc.), and copy its Assistant ID and your API Token. Configure the workflow: Connect your AI provider (e.g., OpenAI) credentials to the two Language Model nodes (GPT 5 mini and GPT 5 chat). In the Query Super Assistant (HTTP Request) node, paste your Assistant ID in the body and add your Super API Token for authentication (we recommend using a Bearer Token credential). Activate the workflow: Toggle the workflow to "Active" and use the built-in form to generate your first fact-checked article! Taking it further Automate publishing:* Connect the final *Article result* node to a *Webflow* or *WordPress** node to automatically create a draft post in your CMS. Generate content in bulk:* Replace the *Form Trigger* with an *Airtable* or *Google Sheet** trigger to automatically generate a whole batch of articles from your content calendar. Customize the writing style:* Tweak the system prompt in the final *New content - Generate the AI output** node to match your brand's specific tone of voice, add SEO keywords, or include specific calls-to-action.

Generating contextual summaries is an token-intensive approach for RAG embeddings which can quickly rack up costs if your inference provider charges by token usage. Featherless.ai is an inference provider with a different pricing model - they charge a flat subscription fee (starting from $10) and allows for unlimited token usage instead. If you're typically spending over $10 - $25 a month, you may find Featherless to be a cheaper and more manageable option for your projects or team. For this template, Featherless's unlimited token usage is well suited for generating contextual summaries at high volumes for a majority of RAG workloads. LLM: moonshotai/Kimi-K2-Instruct Embeddings: models/gemini-embedding-001 How it works A large document is imported into the workflow using the HTTP node and its text extracted via the Extract from file node. For this demonstration, the UK highway code is used an an example. Each page is processed individually and a contextual summary is generated for it. The contextual summary generation involves taking the current page, preceding and following pages together and summarising the contents of the current page. This summary is then converted to embeddings using Gemini-embedding-001 model. Note, we're using a http request to use the Gemini embedding API as at time of writing, n8n does not support the new API's schema. These embeddings are then stored in a Qdrant collection which can then be retrieved via an agent/MCP server or another workflow. How to use Replace the large document import with your own source of documents such as google drive or an internal repo. Replace the manual trigger if you want the workflow to run as soon as documents become available. If you're using Google Drive, check out my Push notifications for Google Drive template. Expand and/or tune embedding strategies to suit your data. You may want to additionally embed the content itself and perform multi-stage queries using both. Requirements Featherless.ai Account and API Key Gemini Account and API Key for Embeddings Qdrant Vector store Customising this workflow Sparse Vectors were not included in this template due to scope but should be the next step to getting the most our of contextual retrieval. Be sure to explore other models on the Featherless.ai platform or host your own custom/finetuned models.

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

How it works Input your proposal basics - Manually enter the core details and key points for your proposal Dual AI processing - OpenAI expands your inputs into a comprehensive draft, then Claude refines it for clarity and readability Automated document output - The workflow copies your Google Doc template, replaces all variables with the AI-generated content, and delivers your finished proposal Set up steps Estimated time: 10-15 minutes Create an OpenRouter account - Sign up at OpenRouter to get API access for Claude Set up your Google Doc template - Create a template document with placeholder variables (variable names are listed in the 'Update proposal' node) Configure API credentials - Add your OpenAI and OpenRouter API keys to the workflow Connect Google Drive - Authenticate your Google account to enable document creation 💡 Detailed configuration instructions and variable naming conventions can be found in the sticky notes within the workflow. `

Generate professional sales proposals from a simple form—AI writes the content, you deliver the document. Fill out client details, pain points, and pricing, and this workflow creates a polished proposal with calculated ROI metrics, executive summary, solution strategy, and team bios. How It Works Sales rep submits a form with client name, industry, pain points, and pricing Code node calculates ROI, net savings, and break-even period Gemini AI generates proposal content: executive summary, key challenges, solution strategy, team bios, and call to action Copies your Google Doc template and replaces all placeholders with generated content Final proposal is ready in Google Drive Setup Import the workflow JSON Create a Google Doc template with placeholders: {{client_name}}, {{executive_summary}}, {{key_challenges}} {{solution_strategy}}, {{team_bios}}, {{next_steps}} {{formatted_roi}}, {{formatted_net_savings}}, {{formatted_break_even}} {{formatted_solution_cost}}, {{date}} Add credentials: Google Drive → OAuth2 Google Docs → OAuth2 Google Gemini → API key from aistudio.google.com Configure "Copy proposal template" node → Point to your template document Customize the AI → Edit system message in "Generate proposal content" to match your tone Test → Submit the form and check the generated proposal Activate

Automated Invoice Collection & Data Extraction Using Vision API and LLM This workflow automates the process of collecting uploaded invoices, extracting text using Google Vision API, and processing the extracted text with an LLM to produce structured data containing key transaction details such as date, voucher number, transaction detail, vendor, and transaction value. The final data is saved to Google Sheets and a notification is sent to Telegram in real time. ✨ Key Features Invoice Upload Form** Users can upload invoice images through a provided form. Google Drive Integration** Files are stored in a specified Google Drive folder with a shareable preview link. OCR via Google Vision API** Converts invoice images to text using TEXT_DETECTION. Data Structuring via LLM** Uses LLM model to parse and structure data. Structured Output Parser** Ensures consistent output with required columns. Data Cleaning** Cleans and formats numeric values without currency symbols. Google Sheets Sync** Appends or updates transaction data in Google Sheets (matched by file ID). Template: Google Sheets Telegram Notification** Sends a transaction summary directly to a Telegram chat/group. 🔐 Required Credentials Google Vision API Key** → for OCR processing. OpenRouter API Key** → to access the Gemini Flash LLM. Google Drive OAuth2** → to upload and download invoice files. Google Sheets OAuth2** → to write or update spreadsheet data. Telegram Bot Token** → to send notifications to Telegram. Telegram Chat ID** → target chat/group for notifications. 🎁 Benefits Fully automated** from invoice upload to structured reporting. Time-saving** by eliminating manual transaction data entry. Real-time integration** with Google Sheets for reporting and auditing. Instant notifications** via Telegram for quick transaction monitoring. Duplicate prevention** using file ID as a matching key. Flexible** for accounting, finance, or administrative teams.