Workflow Templates

How it works Triggers at a regular interval or via a webhook request. Solves AWS WAF challenge then makes a request to fetch the product page. Extracts product data from the retrieved HTML page. Compares the current and previously stored data to detect any changes. Sends an alert if data has changed; else logs no change. Returns results if triggered via webhook. Setup steps [ ] Configure schedule settings in 'Every 6 Hours' node. [ ] Set up AWS WAF credentials in 'Solve AWS WAF' nodes. [ ] Input target URL in 'Fetch Product Page' nodes. [ ] Configure webhook URL in 'Receive Monitor Request' node. Customization Adjust the target site URL in 'Fetch Product Page' and related nodes to match different sites or specific pages.

How it works Runs every 6 hours to pull contacts needing a data refresh from HubSpot All contacts are bulk-enriched with Lusha in a single API call Each contact is scored against your ICP criteria (seniority, company size, industry) CRM records are updated with fresh data; high-scoring fast-track leads trigger a Slack alert Set up steps Install the Lusha community node Add your Lusha API, HubSpot, and Slack credentials Customize the ICP scoring weights in the Code node Set the Slack channel for fast-track alerts and activate

Who is this for Freelancers, small business owners, and accounting teams in Japan who receive invoices and want to eliminate manual data entry into spreadsheets. What this workflow does Send an invoice photo to your LINE bot and the rest is automatic. The workflow fetches the image via LINE Messaging API, runs it through OCR.space to extract raw text, then passes that text to a Gemini AI Agent to parse the key fields — invoice number, date, due date, vendor name, subtotal, tax, and total. The result is appended as a new row in your Google Sheets ledger and a confirmation is sent back to LINE. If the image is not an invoice, a friendly error message is returned instead. How to set up Create a LINE Messaging API channel and get your Channel Access Token Get a free OCR.space API key at ocr.space/ocrapi/freekey Get a free Gemini API key at aistudio.google.com Set up Google Sheets OAuth2 credentials in n8n Enter your LINE token, OCR key, and Spreadsheet ID in the Variables node Set the Webhook URL in LINE Developers to your n8n instance URL Activate the workflow and send an invoice photo to your LINE bot Requirements LINE Messaging API channel and channel access token OCR.space API key (free tier: 25,000 requests/month) Google Gemini API key (free tier: 1,500 requests/day) Google Sheets with OAuth2 credentials configured in n8n How to customize Change SHEET_NAME in the Variables node to file invoices by month. Edit the Gemini prompt to capture extra fields like bank details or line items. Swap OCR.space for Google Cloud Vision API if you need better accuracy on complex layouts.

Notify on menu orders via ntfy and Home Assistant TTS with daily BAC tracking Receive instant push notifications on your phone and voice announcements on your Google Home every time someone orders from your intranet menu — with cumulative BAC tracking per person. > Full documentation & step-by-step setup guides: > 📖 Notion Documentation > 💻 GitHub Repository What This Workflow Does Every time a customer submits an order from your intranet menu website, this workflow: Receives the order via Webhook Logs it to an n8n DataTable and reads all orders for that person today Calculates the cumulative BAC (Blood Alcohol Content) using the Widmark formula Sends a push notification via ntfy (with order history + BAC level) Triggers a Home Assistant script → voice announcement on Google Home No cloud TTS fees. No external AI services. Fully self-hosted. Flow Webhook → Prepare Order → DataTable (INSERT) → Read Today's Orders → BAC Calculator → ntfy Push Notification → Home Assistant TTS Example Output Push notification (ntfy app): 🛎 Order from Mario Mario: 1x Pizza, 2x Beer 33cl 🕐 12:30 📋 Ordered before: 1x Prosecco 🕐 11:45 🍸 BAC ~0.54 g/L 🔴 Google Home voice announcement: Mario has ordered Pizza Prerequisites Before importing this workflow, you need: A running n8n instance (self-hosted or cloud) reachable from the internet Home Assistant** on your local network (e.g. homeassistant.local:8123) At least one Google Home (or Chromecast-capable) device added to HA via the Google Cast integration ntfy** deployed via Docker (self-hosted push notification server) A public HTTPS URL for ntfy — recommended via Cloudflare Tunnel (no open ports needed) An Android or iOS device with the ntfy app installed Credentials Required | Credential | Used For | | ------------------------------------------------------ | ---------------------------------------- | | Home Assistant | Calling the annuncia_ordine TTS script | | Header Auth (Authorization: Bearer <ntfy_token>) | Sending authenticated requests to ntfy | n8n DataTable Schema Create a DataTable in n8n → Data → Tables with these columns: | Column | Type | Description | | -------------- | ------ | ------------------------------------------------------ | | item | Text | Full order string (e.g. "2x Negroni, 1x Messina 33cl") | | person | Text | Normalised person name | | alcol_grammi | Number | Total grams of alcohol for this order | | data | Text | ISO date (YYYY-MM-DD) | | orario | Text | Time string from the order | Copy the DataTable ID and paste it into the DataTable nodes inside the workflow. Webhook Payload Your menu website must POST this JSON to the Webhook Production URL: { "name": "Mario", "time": "26/03/2026, 12:30:00", "order": [ { "section": "Food", "name": "Pizza", "details": "", "quantity": 1 }, { "section": "Drinks", "name": "Beer", "details": "33cl", "quantity": 2 } ], "total": 3 } Setup Steps 1. Deploy ntfy (self-hosted push notifications) Add ntfy to your docker-compose.yml: ntfy: image: binwiederhier/ntfy command: serve volumes: ./ntfy/config:/etc/ntfy ./ntfy/data:/var/lib/ntfy ports: "8095:80" In server.yml, set auth-default-access: deny-all. Then create an admin user and generate a token: docker exec -e NTFY_PASSWORD="your_password" ntfy ntfy user add --role=admin admin docker exec ntfy ntfy token add admin 2. Expose ntfy over HTTPS (Cloudflare Tunnel) Add an ingress rule to your Cloudflare Tunnel config: hostname: ntfy.YOUR_DOMAIN.com service: http://localhost:8095 3. Configure Home Assistant TTS In HA, go to Settings → Automations & Scenes → Scripts → Add script → Edit in YAML and paste: alias: annuncia_ordine sequence: action: tts.speak target: entity_id: tts.google_translate_en_com data: media_player_entity_id: media_player.YOUR_GOOGLE_HOME_ENTITY message: "{{ message }}" language: it mode: single > Replace media_player.YOUR_GOOGLE_HOME_ENTITY with your actual entity ID. Then go to your HA profile → Security → Long-lived access tokens → create and copy a token. 4. Import and configure the workflow Import the workflow JSON into n8n Create the DataTable (columns listed above) — copy its ID Create the Header Auth credential for ntfy: Header Name: Authorization Header Value: Bearer YOUR_NTFY_TOKEN Create the Home Assistant credential: Host: http://homeassistant.local:8123 Access Token: your HA long-lived token In the ntfy notification node, replace YOUR_TOPIC in the URL In the Home Assistant TTS node, set the message attribute expression: ={{ $('Webhook').item.json.body.nome }} has ordered {{ $('Webhook').item.json.body.ordine[0].nome }} Activate the workflow and copy the Webhook Production URL 5. Configure your menu website Set the webhook URL in your menu site config: // menu-data.js "orderWebhook": "https://YOUR_N8N_DOMAIN/webhook/menu" BAC Calculation (Widmark Formula) BAC [g/L] = total_alcohol_grams_today / (70 × 0.68) Assumes ~70 kg body weight. Italian legal driving limit: 0.5 g/L | Emoji | Level | BAC | | ----- | ---------- | ------------- | | ⬜ | None | 0 g/L | | 🟢 | Low | < 0.2 g/L | | 🟡 | Warning | 0.2 – 0.5 g/L | | 🔴 | Over limit | > 0.5 g/L | BAC is cumulative per day per person and resets automatically the next day. Customization | What | How | | ----------------------------------------- | ------------------------------------------------------------ | | Change the TTS message | Edit the message expression in the HA TTS node | | Announce all ordered items | Use $json.body.ordine.map(i => i.quantita + ' ' + i.nome).join(', ') | | Change TTS language | Edit language in the annuncia_ordine HA script | | Announce on multiple Google Home devices | Add multiple media_player_entity_id entries to the HA script | | Disable TTS (keep only push notification) | Remove or deactivate the Home Assistant TTS node | Security | Layer | Mechanism | | -------------------- | ---------------------------------------------------- | | Transport (ntfy) | HTTPS via Cloudflare Tunnel | | ntfy access | auth-default-access: deny-all — no anonymous reads | | n8n → ntfy | Dedicated Bearer token (revokable, no expiry) | | n8n → Home Assistant | Long-lived access token | | Mobile app | Username/password on the ntfy server | Documentation & Source | | | | ------------------------ | ------------------------------------------------------------ | | 📖 Full documentation | Notion — step-by-step setup guides | | 💻 GitHub Repository | paoloronco/n8n-templates | Related Resources ntfy.sh documentation Home Assistant TTS documentation Home Assistant Scripts Google Cast integration n8n Home Assistant node

AI-Powered Daily Investment Idea Generator via Yahoo Finance & Gemini This workflow serves as your personal quantitative analyst. It automatically fetches your active watchlist, grabs real-time prices and breaking news from Yahoo Finance and uses Google Gemini AI to analyze the data and highlight the top 2 to 3 daily investment opportunities. Quick Implementation Steps: Import the Workflow: Copy the provided JSON and paste it directly into your n8n canvas. Set Up Credentials: Add your Google Gemini API key to the Google Gemini Chat Model node. Configure Data Tables: Ensure you have two n8n Data Tables ready—one for your Watchlist and one for the Daily Ideas output. Activate: Toggle the workflow to "Active" so the Schedule Trigger runs automatically at 8:00 AM every day. What It Does This automated system takes the manual labor out of daily market research. Every morning, a Schedule Trigger wakes the workflow up and immediately connects to your internal n8n Data Table to pull a list of financial assets (like crypto or stocks) that you actively want to monitor. Once the watchlist is loaded, the workflow loops through each ticker individually. It sends direct requests to public Yahoo Finance endpoints to gather the most recent trading prices, market charts and top news headlines. A packaging node neatly formats this raw data so that nothing gets lost or overwritten as the loop continues through your entire list. After all tickers are processed, the workflow bundles the packaged data into a comprehensive market report. This report is handed off to a Google Gemini AI Agent equipped with a strict financial analyst prompt. The AI evaluates the news sentiment and catalysts, identifies the most compelling investment setups and formats them into a clean JSON structure. Finally, a code node processes the AI's output and logs the finalized ideas directly into your database for easy reading. Who It's For This workflow is perfect for day traders, crypto enthusiasts, financial analysts and data-driven investors. If you spend hours every morning reading news headlines and checking stock prices to find the best trading setups, this automation acts as an invaluable, time-saving assistant. Requirements to use this workflow To successfully run this workflow, you will need: An active account of n8n (must support Advanced AI nodes and internal Data Tables). A Google Gemini API Key (or an alternative LLM like Groq/OpenAI, if you choose to swap the model node). Active internet access to reach the Yahoo Finance API endpoints. Two pre-configured n8n Data Tables to handle the input (Watchlist) and output (Daily Ideas). How It Works & Set Up Setting up this workflow involves understanding the data flow and configuring your environment to prevent execution errors. 1. Data Table Schemas For smooth data retrieval and storage, your n8n Data Tables must be structured correctly: Input Table (Watchlist):** Must contain a Status column (used to filter for "Active" items) and a Ticker column (e.g., BTC-USD, AAPL). Output Table (Daily Ideas):** Must contain four string columns exactly matching the AI's output: Ticker, Idea_Title, Catalyst_Summary and Conviction_Level. 2. Yahoo Finance API Considerations The workflow utilizes public, undocumented Yahoo Finance endpoints (query1.finance.yahoo.com and query2.finance.yahoo.com). While these do not require an explicit API key, they are subject to strict rate limits. Making hundreds of rapid, back-to-back requests can result in a temporary IP ban (HTTP 429 errors). Keep your active watchlist size manageable (under 50 items) or introduce a brief Wait node inside the loop if you are processing massive lists. 3. Execution Flow The Schedule Trigger initiates the run at 8 AM. The Get Tickers from Watchlist node filters your database for "Active" assets. The Loop Over Items node pushes one ticker at a time to the HTTP Request nodes. The Packager node maps the complex Yahoo JSON responses into clean variables (Price Data, News Data). The Bundler aggregates the items into a single array. The AI Agent reads the aggregated array and generates a JSON string of recommendations. The Array of Data node sanitizes the AI output, stripping away markdown backticks so the final DB-Table Daily Ideas node can easily insert the rows. How To Customize Nodes Change the Schedule:** Double-click the Schedule Trigger to adjust the run time or switch to a completely different trigger (like a Webhook or manual trigger). Modify the AI Prompt:** Open the AI Agent node and tweak the System Message. You can ask the AI to act more aggressively, focus entirely on negative sentiment or output additional JSON keys like Risk_Level. Swap AI Models:** If you prefer OpenAI or Anthropic, simply delete the Google Gemini Chat Model node and connect your preferred language model to the AI Agent. Adjust Data Packaging:** Open the Packager node to extract different data points from the Yahoo Finance payload, such as trading volume or publisher names. Add‑ons To make this automation even more powerful, you can also consider these extensions: The Gold Intelligence Engine Integration:** Use this workflow as a foundational data feeder. Pass the refined output table into a larger, multi-agent intelligence engine that cross-references these daily ideas with macroeconomic indicators. Slack and Notion Delivery:** Add nodes at the end of the workflow to automatically format the AI's top picks into a message, sending it directly to a Slack channel and logging it permanently into a structured Notion database. Automated Trading Webhooks:** Connect the final output to a broker's webhook (like Alpaca or Binance) to automatically execute micro-trades based on the AI's high-conviction ideas. Use Case Examples This workflow framework can be adapted for several valuable scenarios. There can be many more such use cases of this workflow depending on your creative requirements: Crypto Market Briefings: Feed the system top coin tickers (BTC, ETH, SOL) to get a daily breakdown of the crypto landscape before you start trading. Earnings Call Sentinel: Update the watchlist to track companies releasing earnings reports on a specific week, allowing the AI to summarize the sentiment immediately following the news. Commodities Monitoring: Track assets like Gold, Silver and Oil to capture macroeconomic shifts and global catalysts. Portfolio Risk Management: Feed your current holdings into the watchlist. Ask the AI to flag only the negative news, acting as an early warning system for your investments. Forex Pair Tracking: Monitor major currency pairs and global financial news to predict daily currency fluctuations. Troubleshooting Guide | Issue | Possible Cause | Solution | | :--- | :--- | :--- | | HTTP 429 Too Many Requests | Yahoo Finance is rate-limiting your IP address due to rapid polling. | Add a "Wait" node inside the loop (e.g., 2 seconds) to slow down the requests or reduce your total active watchlist size. | | JSON Parsing Error in Code Node | The AI included conversational text or formatting that broke the JSON.parse() command. | Review the prompt in the AI Agent to ensure it enforces strict JSON formatting. Consider lowering the AI temperature setting. | | Empty Output Table | The Data Table ID is incorrect or the column names do not perfectly match the schema. | Verify your Data Table ID in the final node and ensure your table columns exactly read: Ticker, Idea_Title, Catalyst_Summary and Conviction_Level. | | Missing Yahoo Data | The Ticker symbol provided from the watchlist is not recognized by Yahoo Finance. | Ensure symbols match Yahoo's exact format (e.g., use BTC-USD instead of just BTC). | Need Help? Building robust financial workflows and fine-tuning AI agents can sometimes get complicated, especially when integrating multiple APIs or dealing with complex data schemas. So if you need assistance configuring your data tables, customizing the AI prompts or building advanced add-ons to integrate this into your broader systems, please feel free to reach out to our n8n workflow development experts at WeblineIndia.

Video Processing Pipeline with Thumbnail Generation and CDN Distribution Summary Automated video processing system that monitors S3 for new uploads, generates thumbnails and preview clips, extracts metadata, transcodes to multiple formats, and distributes to CDN with webhook notifications. Detailed Description A comprehensive video processing workflow that receives S3 events or manual triggers, validates video files, extracts metadata via FFprobe, generates thumbnails at key frames, creates animated GIF previews, transcodes to multiple resolutions, invalidates CDN cache, and sends completion notifications. Key Features S3 Event Monitoring**: Automatic detection of new video uploads Thumbnail Generation**: Multiple sizes at key frame intervals Video Metadata**: FFprobe extraction of duration, resolution, codec info Preview GIF**: Animated preview clips for video galleries Multi-Format Transcoding**: Convert to 1080p, 720p, 480p CDN Distribution**: Cloudflare cache invalidation and signed URLs Webhook Callbacks**: Notify origin system on completion Use Cases Video hosting platforms Media asset management systems Content delivery networks Video streaming services Social media platforms E-learning video processing User-generated content platforms Required Credentials AWS S3 Credentials (for video storage) FFmpeg API credentials (via HTTP) Cloudflare API Token (for CDN) Slack Bot Token (for notifications) Google Sheets OAuth (for logging) Node Count: 24 (19 functional + 5 sticky notes) Unique Aspects Uses Webhook for S3 event notifications Uses Code nodes for S3 info extraction and URL generation Uses If node for video format validation Uses HTTP Request nodes for FFprobe, FFmpeg, and CDN APIs Uses Aggregate node for collecting parallel processing results Uses Merge nodes for multiple workflow path consolidation Implements parallel processing for thumbnails, GIF, and transcoding Workflow Architecture [S3 Event Webhook] [Manual Webhook] | | +--------+----------+ | v [Merge Triggers] | v [Extract S3 Info] (Code) | v [Check Is Video] (If) / \ Yes No | | v v [Get Video Metadata] [Invalid Response] (FFprobe) | | | v | [Parse Video Metadata] | (Code) | /|\ | / | \ | v v v | Thumbs[Transcode] | \ | / | \ | / | v v | [Aggregate Results] | | | v | [Invalidate CDN Cache] | | | v | [Generate Signed URLs] | / \ | / \ | v v | [Log Sheet] [Slack] | \ / | \ / | v | [Merge Output Paths] | | | +---------+-------+ | v [Merge All Paths] | v [Respond to Webhook] Configuration Guide S3 Event: Configure S3 bucket notification to send events to webhook FFmpeg API: Use a hosted FFmpeg service (e.g., api.ffmpeg-service.com) Cloudflare: Set zone ID and API token for cache invalidation Slack Channel: Set #video-processing for notifications Google Sheets: Connect for processing metrics logging Supported Video Formats | Extension | MIME Type | |-----------|----------| | .mp4 | video/mp4 | | .mov | video/quicktime | | .avi | video/x-msvideo | | .mkv | video/x-matroska | | .webm | video/webm | | .m4v | video/x-m4v | Thumbnail Generation | Size | Dimensions | Suffix | |------|------------|--------| | Large | 1280x720 | _large | | Medium | 640x360 | _medium | | Small | 320x180 | _small | Thumbnails generated at: 10%, 30%, 50%, 70%, 90% of video duration Transcoding Presets | Preset | Resolution | Bitrate | Codec | |--------|------------|---------|-------| | 1080p | 1920x1080 | 5000k | H.264 | | 720p | 1280x720 | 2500k | H.264 | | 480p | 854x480 | 1000k | H.264 | Output Structure { "job_id": "job_1705312000_abc123", "status": "completed", "original": { "filename": "video.mp4", "resolution": "1920x1080", "duration": "00:05:30" }, "thumbnails": { "large": "https://cdn/thumbnails/job_id/thumb_0_large.jpg", "medium": "https://cdn/thumbnails/job_id/thumb_0_medium.jpg", "small": "https://cdn/thumbnails/job_id/thumb_0_small.jpg" }, "preview_gif": "https://cdn/previews/job_id/preview.gif", "transcoded": { "1080p": "https://cdn/transcoded/job_id/video_1080p.mp4", "720p": "https://cdn/transcoded/job_id/video_720p.mp4", "480p": "https://cdn/transcoded/job_id/video_480p.mp4" } } `

Who's it for This workflow is perfect for app developers, SaaS founders, and mobile growth teams who need constant UGC-style video ads without hiring creators or agencies. If you're spending $500+ per creator and waiting weeks for videos, this automates the entire process for $2-5 per video in 8 minutes. How it works Upload an app screen recording, and this n8n workflow handles everything: Gemini AI analyzes the recording and generates a UGC ad structure (hook, problem, solution, CTA with word limits). It describes the ideal actor, emotional journey, and visual style. Sora 2 then generates a hyper-realistic AI actor delivering the script naturally. VideoBGRemover removes the actor's background and composites them picture-in-picture over your screen recording with audio mixing. The result is a scroll-stopping UGC ad ready for TikTok, Instagram, and Facebook. Set up steps Setup takes ~10 minutes and requires 3 API keys: Get your Gemini API key from Google AI Studio Get your FAL AI key for Sora 2 access Get your VideoBGRemover API key at https://videobgremover.com/n8n Add all three keys to n8n environment variables Connect Google Drive for output storage Test with the included sample screen recording The workflow includes 7 detailed sticky notes explaining each section (Gemini analysis, Sora generation, VideoBGRemover composition, output handling). Requirements Google Gemini API key (for video analysis) FAL AI API key (for Sora 2 access) VideoBGRemover API key Google Drive account (for final video storage) App screen recordings in vertical format (9:16 recommended, 4-12 seconds) How to customize Modify the Gemini prompt to adjust ad tone, script word limits, or actor descriptions. Change composition settings in the VideoBGRemover node (position, size, audio mix). Add your own post-processing nodes for branding, captions, or multi-platform exports.

What this workflow does This template automates the entire process of documenting Sprint Reviews in Scrum: Input Collection – Through a friendly form, users upload the transcript file (meeting notes, sprint review transcript, or VTT captions) and specify the sprint name and domain. Transcript Parsing – A Code node formats the transcript into clean [HH:MM:SS] Speaker: text lines, supporting VTT, Zoom, or custom timestamp formats used in Scrum events. AI-Driven Summary – The AI Agent (LangChain + OpenAI) produces a well-structured AI summarization in Markdown, including: A 3–5 bullet Executive Summary of sprint review highlights A Presentation Recap table (Timestamp | Presenter | Topics) A list of Action Items with owners (if recognizable from the transcript) Preview – The summary renders as a styled card with custom CSS for easy readability in n8n. Archive – Automatically appends a record to Google Sheets, saving the date, domain, sprint, transcript file name, and the AI-generated sprint review summary. Why it’s useful Zero manual summarizing** – AI extracts key insights from transcript files into Markdown you can instantly share with your Scrum team and stakeholders. Easy setup** – drag-and-drop import, plus form-based input for non-technical users during sprint reviews. Centralized tracking** – all past sprint summaries live in one spreadsheet for retrospectives, audits, and continuous improvement. Flexible and extendable** – you can switch to Airtable, Slack, or Notion, or refine the summary template to match your Scrum workflow. Ideal for Scrum Masters** wanting quick sprint review summaries for stakeholders Agile Coaches** analyzing sprint review transcripts, presentation patterns, and follow-up tasks Product Owners** keeping a searchable log of sprint outcomes and action items Prerequisites / Credentials OpenAI API Key** — required for the AI Agent node (or any other Agent for summarization) Google Sheets OAuth2 credentials** — required for saving sprint review data to Sheets (Optional) Ensure LangChain / AI Agent nodes are installed in your n8n instance How to Use This Template Import the workflow JSON into your n8n instance. Set up credentials: For the OpenAI LLM node, provide your OpenAI API key In Google Sheets, configure OAuth2 and specify your spreadsheet ID (replace YOUR_SHEET_ID) Create a new sheet with the following columns: Date Domain Sprint name Content VTT file Transcript Enable and run the workflow. Fill out the form: upload transcript file, enter sprint & domain, click Create Summary. View the AI-generated Markdown sprint review summary in the preview card. Verify the new entry appears in your Google Sheet with all sprint details.

Product Hunt Launch Monitor - Scraping & Summarization of Product Hunt Feedbacks This n8n template provides automated competitive intelligence by scraping and summarizing Product Hunt launch feedback with a specialized AI analyst. This workflow is essential for product managers, marketing teams, and founders who need to quickly gather and distill actionable insights from competitor launches to inform their own product strategy and positioning. Self-Hosted Only This Workflow uses a community contribution and is designed and tested for self-hosted n8n instances only. How it works The workflow can be triggered manually but is designed to be easily switched to a Schedule Trigger for continuous competitive monitoring. A Google Sheet node fetches a list of product names you wish to monitor, which the workflow processes in a loop. A BrowserAct node then initiates a web scraping task to collect all the public comments from the specified Product Hunt launch page. An AI Agent, powered by Google Gemini, acts as a competitive intelligence analyst, processing the raw comments. The AI distills the feedback into a structured format, providing a concise Summary, pinpointing key Positive and Negative feedback, and generating Recommendations for a similar product to be successful. The structured analysis is saved to a Google Sheet for easy review and tracking. Finally, a Slack notification confirms that the Product Hunt results have been processed and updated. Requirements BrowserAct** API account for web scraping BrowserAct* "Product Hunt Launch Monitor*" Template BrowserAct** n8n Community Node -> (n8n Nodes BrowserAct) Gemini** account for the AI Agent Google Sheets** credentials for input and saving the analysis Slack** credentials for sending notifications Need Help? How to Find Your BrowseAct API Key & Workflow ID How to Connect n8n to Browseract How to Use & Customize BrowserAct Templates How to Use the BrowserAct N8N Community Node Workflow Guidance and Showcase Steal Your Competitor's Weaknesses (Product Hunt + BrowserAct + n8n)

Spotify Music Downloader: Effortless Music Downloading from Spotify Description: The Spotify Music Downloader is an automation flow that allows users to easily download music from Spotify tracks. By leveraging the powerful Spotify Downloader API, the flow downloads Spotify tracks, uploads them to Google Drive, and logs the details to Google Sheets for better management. With this tool, you can save time and effort while enjoying the convenience of direct downloads and automatic organization. Use Case: Music Enthusiasts**: Easily download your favorite Spotify tracks and store them in Google Drive. Content Creators**: Automate the process of storing and tracking downloaded music files. Organizational Automation**: Store download links and file sizes in Google Sheets to track downloads systematically. Spotify Users**: Provides a hassle-free way to access and organize your Spotify music offline. Benefits: Simple & Automated**: Easily download and organize music from Spotify with minimal effort. Seamless Integration**: Directly integrates with Google Drive for storage and Google Sheets for tracking. Fast: Uses the **Spotify Downloader API to quickly fetch and download the tracks. Track Downloads**: Keep a record of each download, including size, download link, and other details. Google Drive Storage**: Automatic storage of downloaded tracks in your Google Drive, making it easy to access files anytime. Node-by-Node Explanation: On Form Submission: The flow starts when the user submits a Spotify track URL through a form. HTTP Request (Spotify Downloader API): Sends a request to the Spotify Downloader API to retrieve the music file associated with the given Spotify URL. If (Link Validation): Ensures that the provided Spotify URL is not empty. If1 (Success Validation): Checks if the API response was successful. Download Music: Downloads the music file from the provided Spotify link using the fetched download URL. Google Drive: Uploads the downloaded music file to Google Drive. Google Sheets: Logs the successful download details (like URL, download link, size, and timestamp) to Google Sheets. Code (File Size Calculation): Calculates the file size of the downloaded music file in MB. Wait: Introduces a delay to ensure that the upload process to Google Drive is complete before continuing. Wait1: Another wait node to allow additional time between the various steps in the flow. Google Sheets1 (Success Logs): Adds a new record for a successful download to a Google Sheet. Google Sheets2 (Failed Logs): Records failed download attempts in a secondary Google Sheet. By utilizing this flow, you can automate the process of downloading music from Spotify and storing it in Google Drive while keeping track of every download in Google Sheets. It's an ideal solution for music lovers, content creators, or anyone looking to automate their music download and storage process with ease. Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

AI LinkedIn Content Assistant using Bright Data and NocoDB Who’s it for This template is designed for creators, founders, and automation builders who publish regularly on LinkedIn and want to analyze their content performance using real data. It’s especially useful for users who are already comfortable with n8n and want to build data-grounded AI assistants instead of relying on generic prompts or manual spreadsheets. What this workflow does This workflow builds an AI-powered LinkedIn content assistant backed by real engagement data. It automatically: Scrapes LinkedIn posts and engagement metrics using Bright Data Stores structured post data in NocoDB Enables an AI chat interface in n8n to query and analyze your content Returns insights based on historical performance (not hallucinated data) You can ask questions like: “Which posts performed best last month?” “What content got the most engagement?” “What should I post next?” Requirements Self-hosted or cloud n8n instance Bright Data – LinkedIn scraping & data extraction NocoDB – Open-source Airtable-style database Open AI API – For AI reasoning & insights Setup Import the workflow into your n8n instance Open the Config node and fill in required variables Connect your credentials for Bright Data, NocoDB, and Open AI API Activate the workflow and run the scraper once to populate data How to customize the workflow You can extend this template by: Adding new metrics or post fields in NocoDB Scheduling regular data refreshes Changing the AI system prompt to match your content strategy Connecting additional channels (email, Slack, dashboards) This template is fully modular and designed to be adapted to your workflow. Questions or Need Help? For setup help, customization, or advanced AI workflows, join my 🌟 FREE 🌟 community: Tech Builders Club Happy building! 🚀 - Kornel Dubieniecki

Screen resumes & save candidate scores to Notion with OpenAI This template helps you automate the initial screening of job candidates by analyzing resumes against your specific job descriptions using AI. 📺 How It Works The workflow automatically monitors a Notion database for new job applications. When a new candidate is added: It checks if the candidate has already been processed to avoid duplicates. It downloads the resume file (supporting both PDF and DOCX formats). It extracts the raw text and sends it to OpenAI along with the specific job description and requirements. The AI acts as a "Senior Technical Recruiter," scoring the candidate on skills, experience, and stability. Finally, it updates the Notion entry with a fit score (0-100), a one-line summary, detected skills, and a detailed analysis. 📄 Notion Database Structure You will need two databases in Notion: Jobs (containing descriptions/requirements) and Candidates (containing resume files). Candidates DB Fields:** AI Comments (Text), Resume Score (Text), Top Skills Detected (Text), Feedback (Select), One Line Summary (Text), Resume File (Files & Media). Jobs DB Fields:** Job Description (Text), Requirements (Text). 👤 Who’s it for This workflow is for recruiters, HR managers, founders, and hiring teams who want to reduce the time spent on manual resume screening. Whether you are handling high-volume applications or looking for specific niche skills, this tool ensures every resume gets a consistent, unbiased first-pass review. 🔧 How to set up Create the required databases in Notion (as described above). Import the .json workflow into your n8n instance. Set up credentials for Notion and OpenAI. Link those credentials in the workflow nodes. Update Database IDs: Open the "Fetch Job Description" and "On New Candidate" nodes and select your specific Notion databases. Run a test with a sample candidate and validate the output in Notion. 📋 Requirements An n8n instance (Cloud or Self-hosted) A Notion account OpenAI API Key (GPT-4o or GPT-4 Turbo recommended for best reasoning) 🧩 How to customize the workflow The system is fully modular. You can: Adjust the Persona:** In the Analyze Candidate agent nodes, edit the system prompt to change the "Recruiter" persona (e.g., make it stricter or focus on soft skills). Change Scoring:** Modify the scoring matrix in the prompt to weight "Education" or "Experience" differently. Filter Logic:** Add a node to automatically disqualify candidates below a certain score (e.g., < 50) and move them to a "Rejected" status in Notion. Multi-language:** Update the prompt to translate summaries into your local language if the resume is in English.