Workflow Templates

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.

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.

🧠 Automated Resume & Cover Letter Generator This project is an automation workflow that generates a personalized resume and cover letter for each job listing. 🚀 Features Automated Resume Crafting Generates an HTML resume from your data. Hosts it live on GitHub Pages. Converts it to PDF using Gotenberg and saves it to Google Drive. Automated Cover Letter Generation Uses an LLM to create a tailored cover letter for each job listing. Simple Input Database Agent Stores your experience in an n8n Data Table with the following fields: role, summary, task, skills, tools, industry. The main agent pulls this data using RAG (Retrieval-Augmented Generation) to personalize the outputs. One-Time GitHub Setup Initializes a blank GitHub repository to host HTML files online, allowing Gotenberg to access and convert them. 🧩 Tech Stack Gotenberg** – Converts HTML to PDF GitHub Pages** – Hosts live HTML files n8n** – Handles data tables and workflow automation LLM (OpenAI / Cohere / etc.)** – Generates cover letters Google Drive** – Stores the final PDFs ⚙️ Installation & Setup 1. Create a GitHub Repository This repo will host your HTML resume through GitHub Pages. 2. Set the Webhook URL In the notify-n8n.yml file, replace: role | summary | task | skills | tools | industry 3. Create the n8n Data Table Add the following columns: role | summary | task | skills | tools | industry 4. Create a Google Spreadsheet Add these columns: company | cover_letter | resume 5. Install Gotenberg Follow the installation instructions on the Gotenberg GitHub repository: https://github.com/thecodingmachine/gotenberg 6. Customize the HTML Template Modify the HTML resume to your liking. You can use an LLM to locate and edit specific sections. 7. Add Authentication and Link Your GitHub Repo Ensure your workflow has permission to push updates to your GitHub Pages branch. 8. Run the Workflow Once everything is connected, trigger the workflow to automatically generate and save personalized resumes and cover letters. 📝 How to Use Copy and paste the job listing description into the Telegram bot. Wait for the "Done" notification before submitting another job. Do not use the bot again until the notification appears. The process usually takes a few minutes to complete. ✅ Notes This workflow is designed to save time and personalize your job applications efficiently. By combining n8n automation, LLMs, and open-source tools like Gotenberg, you can maintain full control over your data while generating high-quality resumes and cover letters for every job opportunity.

AI Workflow Description and Template Generator This workflow automates the creation of professional documentation and template-ready sticky notes for any n8n workflow using AI. How it works Receives an n8n workflow JSON file via Telegram Validates the input file type and extracts workflow data Scrubs sensitive information and analyzes workflow structure Uses Google Gemini AI to generate comprehensive documentation Assembles a complete template with main workflow sticky note and logical section stickies Sends back the documented workflow file, usage checklist, and setup guide via Telegram Setup Configure Telegram Trigger credentials for receiving files Configure Telegram API credentials for sending messages Configure Google Gemini Chat Model (Google PaLM API) credentials Customization Adjust the prompt in the "AI Template Generator" node to modify documentation style, detail level, or specific requirements for your use case.

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. `

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 🚀

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

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.

Description Who's it for: Content creators, marketers, and businesses who publish on both YouTube and blog platforms. What it does: Monitors your YouTube channel for new videos and automatically creates SEO-optimized blog posts using AI, then publishes to WordPress or Webflow. How it works: RSS Feed Trigger polls YouTube videos (every X amount of time) Extracts video metadata (title, description, thumbnail) YouTube node extracts full description for extra context Uses OpenAI (you can choose any model) to generate 600-800 word blog post Publishes to WordPress AND/OR Webflow with error handling Sends notifications to Telegram if publishing fails Requirements: YouTube channel ID (avoid tutorial channels for better results) OpenAI API key (or similar) WordPress OR Webflow credentials Telegram bot (optional, for error notifications) Setup steps: Replace YOUR_CHANNEL_ID in RSS Feed Trigger Add OpenAI credentials in AI generation node Configure WordPress and/or Webflow credentials Add Telegram bot for error notifications (optional). If you choose to set up Telegram, you need to input your channel ID. Test with manual execution first Customization: Modify AI prompt for different content styles Adjust polling frequency (30-60 minutes recommended) Add more CMS platforms Add content verification (is content larger than 600 characters? if not, improve)

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.

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

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