by irfan saeed
Auto-Generate YouTube Chapters with AI-Powered Transcript Analysis Overview This workflow uses YouTube Data API v3 and Google Gemini 1.5 Flash AI to automatically generate timestamped chapters for videos by analyzing SRT captions. It enhances viewer navigation, improves SEO , and saves creators time by automating manual tasks. Prerequisites YouTube API Setup Create a Google Cloud Project Go to the Google Cloud Console. Click Select a project > New Project and name it (e.g., "YouTube Chapters Automation") . Enable YouTube Data API v3 Navigate to APIs & Services > Library. Search for "YouTube Data API v3" and click Enable . Configure OAuth Consent Screen Go to APIs & Services > OAuth consent screen. Select External (public) or Internal (testing), then add required details (app name, support email) . Generate OAuth 2.0 Credentials Under Credentials, click Create Credentials > OAuth client ID. Choose Web app, then download the JSON key file . Add Credentials to n8n Other Requirements Google Gemini API**: Configure access for the gemini-1.5-flash-8b-exp-0924 model by getting the api key. Workflow Steps Set Video ID Input the target video ID (e.g., r1wqsrW2vmE) using the Set Video ID node. Fetch Video Metadata Use the YouTube API node to retrieve the video’s title, category, and existing description . Download SRT Captions Get Caption ID: Call https://www.googleapis.com/youtube/v3/captions to fetch the caption track ID . Download Transcript: Use the ID to retrieve SRT data via https://www.googleapis.com/youtube/v3/captions/{{ID}}?tfmt=srt . Analyze Transcript with Gemini AI Process the SRT file with Google Gemini AI to identify chapters using a prompt like: "Classify this transcript into timestamped chapters (e.g., 00:00 - Introduction)." Validate output with a structured parser (e.g., Structured Captions node) . Update Video Description Append chapters to the description using the YouTube API’s videos.update method . Value Proposition Viewer Experience**: Chapters improve navigation and reduce drop-off rates . SEO Benefits**: Structured descriptions enhance search visibility . Time Savings**: Eliminates manual chapter creation .
by Ranjan Dailata
Who this is for? This workflow is designed for professionals and teams who need real-time, structured insights from Google Search results without manual effort. What problem is this workflow solving? This n8n workflow solves the problem of automating Google Search result extraction, cleanup, summarization, and AI-enhanced formatting for downstream use like sending the results to a webhook or another system. What this workflow does Automates Google Search via Bright Data Uses Bright Data’s proxy-based SERP API to run a Google Search query programmatically. Makes the process repeatable and scriptable with different search terms and regions/zones. Cleans and Extracts Useful Content The Google Search Data Extractor uses LLM based cleaning to remove HTML/CSS/JS from the response and extract pure text data. Converts messy, unstructured web content into structured, machine-readable format. Summarizes Search Results Through the Gemini Flash + Summarization Chain, it generates a concise summary of the search results. Ideal for users who don’t have time to read full pages of search results. Formats Data Using AI Agent The AI Agent acts like a virtual assistant that: Understands search results Formats them in a readable, JSON-compatible form Prepares them for webhook delivery Delivers Results to Webhook Sends the final summary + structured search result to a webhook (could be your app, a Slack bot, Google Sheets, or CRM). Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. A Google Gemini API key (or access through Vertex AI or proxy). Update the Google Search query as you wish by navigating to the Set Google Search Query node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize This Workflow to your needs 1. Change the Search Input Default: It searches a fixed query or dataset. Customize: Accept input from a Google Sheet, Airtable, or a form. Auto-trigger searches based on keywords or schedules. 2. Customize Summarization Style (LLM Output) Default: General summary using Google Gemini or OpenAI. Customize: Add tone: formal, casual, technical, executive-summary, etc. Focus on specific sections: pricing, competitors, FAQs, etc. Translate the summaries into multiple languages. Add bullet points, pros/cons, or insight tags. 3.Choose Where the Results Go Options: Email, Slack, Notion, Airtable, Google Docs, or a dashboard. Auto-create content drafts for WordPress or newsletters. Feed into CRM notes or attach to Salesforce leads.
by Ranjan Dailata
Who this is for? This workflow automates the process of Wikipedia data extraction using the Bright Data Web Unlocker, parsing and cleaning the data, and then sending the results to a specified webhook URL for downstream processing, reporting, or integration. What problem is this workflow solving? Researchers who need structured information from Wikipedia pages regularly. Data Engineers building knowledge bases or enriching datasets with factual data. Digital Marketers or Content Writers automating fact-checking or content sourcing. Automation Enthusiasts who want to trigger external systems with rich context from Wikipedia. What this workflow does This workflow addresses the challenges of manually retrieving, structuring, and using data from Wikipedia at scale. Workflow Breakdown Trigger Type: Scheduled or Manual Purpose: Starts the workflow either on a fixed schedule (e.g., daily) or on-demand via a manual trigger or incoming webhook. Bright Data Wikipedia Scraping Tool Used: Bright Data Web Unlocker Action: Scrape the HTML content of one or multiple Wikipedia article URLs. Parse & Extract Structured Data The Basic LLM Chain node is responsible for producing a human readable content. Summarization Summarize the Wikipedia content by utilizing the Summarization Chain node. Send to Webhook Initiates a Webhook notification to the specified URL as part of the "Summary Webhook Notifier" node. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Set Wikipedia URL with Bright Data Zone node with the Wikipedia URL and Bright Data Zone. Update the Summary Webhook Notifier node with the Webhook endpoint of your choice. How to customize this workflow to your needs Update Wikipedia URL Replace with your own Wikipedia URL of your interest. Make sure to set the Wikipedia URL as part of the "Set Wikipedia URL with Bright Data Zone" node. Modify Data Extraction Logic Extract entire article content or just specific sections by extending the "LLM Data Extractor" node prompt. Extend AI Summarization Extract key bullet points or entities. Create short-form summaries by extending the "Concise Summary Generator" node. Extend Summary Webhook Notifier Send to Slack, Discord, Telegram, MS Teams via the Webhook notification mechanism. Connect to your internal database/API via the Webhook notification mechanism.
by Yang
Who is this for? This workflow is built for marketers, sales teams, agencies, virtual assistants, and anyone who regularly researches or contacts local businesses. It's ideal for building lead lists, tracking competitors, or creating location-specific outreach campaigns. What problem is this workflow solving? Instead of manually searching Google Maps and copying business info into spreadsheets, this automation pulls structured business data (e.g. restaurants, gyms, service providers) and logs it directly into Google Sheets. It saves hours of work and ensures cleaner, more usable data. What this workflow does The workflow takes a Google Maps search query (like "best restaurants in New York") and sends it to Dumpling AI. It returns a list of places including their name, address, website, phone number, rating, and more. Each result is split into a row and automatically added to a Google Sheet. Setup Dumpling AI Sign up at Dumpling AI Generate your API key In the HTTP Request node, select Header Auth and paste your key in the Authorization field Google Sheets Create a sheet with tab name Leads Add the following column headers to row 1: Name, Address, Phone number, Website, Rating, Price Level, Type, Booking Link, Position Connect your Google Sheets account and link this sheet in the node Customize the Query In the HTTP node, replace the query string (e.g., "best+restaurants+in+New+York") with your own search term Run It Use the manual trigger to test Optionally swap in a Schedule or Webhook node to run it automatically How to customize this workflow to your needs Change the search query to target different cities or business types Use filters to only save leads with a minimum rating or price level Add GPT to summarize listings or qualify leads Swap Google Sheets for Airtable or a CRM system for deeper integration
by Xavier
This workflow creates nested Google Drive folders from a path string (like Projects/Clients/Reports). It automatically handles the necessary folder lookups and creation steps required by Google Drive, then outputs the final folder's ID for immediate use. How it works This workflow streamlines the creation of nested folders in Google Drive: Input: Provide a root_folder_id and a path (e.g., Projects/Clients/Reports) as input. Path Parsing: The workflow splits the path into individual folder names (based on the / separator) Iterative Check & Create: Loops through each part of your path: Searches within the current parent folder (starting with the root_folder_id) for a subfolder matching the name. If found: Retrieves the existing folder's ID to use as the parent for the next iteration. If not found: Creates a new folder with that name inside the current parent folder and uses the new folder's ID as the parent for the next iteration. Output: Returns the Google Drive Folder ID of the very last folder in the specified path (e.g., the ID for Reports in the example above). This ID can then be directly used in subsequent n8n nodes to upload files, create documents, or perform other actions within that specific folder. Set up steps Setting up this workflow requires configuring the connection to Google Drive and knowing where to start creating folders: Connect Google Drive Account: Ensure you have a Google Drive credential configured in your n8n instance. Then link your credentials in the workflow: there are 2 Google Drive nodes that will need to be updated. Identify Starting Folder ID: Determine the Google Drive Folder ID where your nested structure should begin. You can either use the root of your Google Drive or a specific folder: To use the root of Google Drive, simply set root_folder_id to root (also called "My Drive" in the UI) To use a specific folder, open the folder in a webbrowser and look at the URL. The folder ID will be in the last part of the URL: https://drive.google.com/drive/folders/THIS_IS_THE_FOLDER_ID. Prepare Inputs for Execution: When running the workflow (or triggering it), you will need to provide: google_drive_folder_id -> this is the root folder ID you identified in step 2. desired_path -> This is the path you want to create (e.g., Projects/Clients/Reports). Here's an example of how you can call this workflow in your other workflows:
by Yang
👥 Who is this for? This workflow is ideal for virtual assistants, researchers, developers, automation specialists, and data analysts who need to regularly extract and organize structured product information (like books) from a website. It’s especially useful for those working with catalog-based websites who want to automate extraction and delivery of clean, sorted data. 🧩 What problem is this solving? Manually copying product listings like book titles and prices from a website into a spreadsheet is slow and repetitive. This automation solves that problem by scraping content using Dumpling AI, extracting the right data using CSS selectors, and formatting it into a clean CSV file that is sent to your email—all triggered automatically when a new URL is added to Google Sheets. ⚙️ What this workflow does This template automates an entire content scraping and delivery process: Watches a Google Sheet for new URLs Scrapes the HTML content of the given webpage using Dumpling AI Uses CSS selectors in the HTML node to extract each book from the page Splits the HTML array into individual items Extracts the book title and price from each HTML block Sorts the books in descending order based on price Converts the sorted data to a CSV file Sends the CSV via email using Gmail 🛠️ Setup Google Sheets Create a sheet titled something like URLs Add your product listing URLs (e.g., http://books.toscrape.com) Connect the Google Sheets trigger node to your sheet Ensure you have proper credentials connected Dumpling AI Create an account at Dumpling AI) - Generate your API key Set the HTTP Method to POST and pass the URL dynamically from the Google Sheet Use Header Auth to include your API key in the request header Make sure "cleaned": "True" is included in the body for optimized HTML output HTML Node The first HTML node extracts the main book container blocks using: .row > li The second HTML node parses out the individual fields: title: h3 > a (via the title attribute) price: .price_color Sort Node Sorts books by price in descending order Note: price is extracted as a string, ensure it's parsable if you plan to use numeric filtering later Convert to CSV The JSON data is passed into a Convert node and transformed into a CSV file Gmail Sends the CSV as an attachment to a designated email 🔄 How to customize this workflow Extract more data**: Add more CSS selectors in the second HTML node to pull fields like author, availability, or product links Switch destinations**: Replace Gmail with Slack, Google Drive, Dropbox, or another platform Adjust sorting**: Sort alphabetically or based on another extracted value Use a different source**: As long as the site structure is consistent, this can scrape any listing-like page Trigger differently**: Use a webhook, form submission, or schedule trigger instead of Google Sheets ⚠️ Dependencies and Notes This workflow uses Dumpling AI to perform the web scraping. This requires an API key and uses credits per request. The HTML node depends on valid CSS selectors. If the site layout changes, the selectors may need to be updated. Ensure you’re not scraping content from websites that prohibit automated scraping.
by Paulo Ramirez
Upload your CRM contacts to telli and schedule AI voice-agent calls Introduction to telli and AI Voice-Agent Calls telli is an innovative platform that provides AI-powered voice agents capable of making calls and performing tasks tailored to specific customer use cases. These AI voice-agents can handle a wide range of communication tasks, from appointment scheduling to customer support, with remarkable efficiency and natural conversation flow. This template is designed for businesses and organizations looking to automate their outbound calling processes using telli's AI voice-agents in conjunction with Airtable as their CRM. It solves the problem of manual call scheduling and data transfer between your CRM and calling system, saving time and reducing human error. Prerequisites telli account Airtable base with contact information n8n instance Step-by-Step Setup Guide n8n Setup: Create a new workflow in n8n. Add the Airtable node to connect to your CRM table. telli API Configuration: Log in to your telli dashboard. Locate and copy your API key under telli - Settings - API/Webhooks. Workflow Configuration: Add two HTTP Request nodes to your n8n workflow. Set the "Authorization" header in both POST requests, replacing the value with your telli API key. Configure the first request to use the /add-contact endpoint. Set up the second request to use the /schedule-call endpoint. Data Mapping: Map the relevant fields from your Airtable node to the telli API requests. Testing and Activation: Run a test execution of your workflow. Once satisfied with the results, activate the workflow. API Endpoint Details Add Contact Endpoint URL**: https://api.telli.com/v1/add-contact Method**: POST Headers**: Authorization: YOUR-API-KEY Content-Type: application/json Payload**: { "external_contact_id": "string", "salutation": "string", "first_name": "string", "last_name": "string", "phone_number": "string", "email": "jsmith@example.com", "contact_details": {}, "timezone": "string" } Schedule Call Endpoint URL**: https://api.telli.com/v1/schedule-call Method**: POST Headers**: Authorization: YOUR-API-KEY Content-Type: application/json Payload**: { "contact_id": TELLI-CONTACT-ID, "agent_id": "string", "max_retry_days": 123, "call_details": { "message": "Hello, this is your friendly reminder!", "questions": [ { "fieldName": "email", "neededInformation": "email of the customer", "exampleQuestion": "What is your email address?", "responseFormat": "email string" } ] }, "override_from_number": "string" } Use Cases This template is versatile and can be applied to various scenarios, including: Lead Qualification*: Automatically schedule calls to new leads entered in your CRM. Appointment Reminders*: Set up calls to remind clients of upcoming appointments. Customer Feedback*: Schedule follow-up calls after product deliveries or service completions. Uploading Multiple Contacts For bulk operations, you have two options: Loop Node: Include a Loop node in your n8n workflow to process multiple contacts sequentially. Batch Endpoints: Instead of /add-contact and /schedule-call, use telli's batch endpoints: /add-contacts-batch: Add multiple contacts within an array. /schedule-calls-batch: Schedule multiple calls at once. Example of batch endpoint usage: { "contacts": [ {"name": "John Doe", "phone": "+1234567890"}, {"name": "Jane Smith", "phone": "+1987654321"} ] } By leveraging this template, you can seamlessly integrate your Airtable CRM with telli's powerful AI voice-agents, automating your outbound calling process and enhancing your customer communication strategy.
by JPres
👥 Who Is This For? Sales and marketing teams seeking efficient, hands‑free generation of personalized slide decks for each prospect from CSV lead lists. 🛠 What Problem Does This Solve? Manually editing presentation decks for large lead lists is slow and error‑prone. This workflow fully automates: Importing and parsing CSV lead data Logging leads and outputs in Google Sheets Duplicating a master Slides template per lead Injecting lead‑specific variables into slides 🔄 Node‑by‑Node Breakdown | Step | Node | Purpose | | ---- | ---------------------------------------- | -------------------------------------------------------- | | 1 | New Leads Arrived | Detect new CSV uploads in Drive | | 2 | File Type? | Filter for .csv files only | | 3 | Download by ID | Download the CSV content | | 4 | Create new Sheet | Create a Google Sheet to record lead data | | 5 | Combine Empty New Document with CSV Data | Structure each lead record for slide creation | | 6 | Merge Data for new Lead Document | Map template placeholders to lead values | | 7 | Get all Leads | Retrieve sheet rows to iterate through each lead | | 8 | MoveToLeadListFolder | Move processed CSV to an archive folder | | 9 | Copy Slides Template | Make a copy of the master Slides deck | | 10 | Create Custom Presentation | Replace placeholders in the copied deck with lead data | | 11 | Add Presentation ID to Lead | Write the generated presentation URL back into the Sheet | ⚙️ Pre‑conditions / Requirements n8n with Google Drive, Sheets, and Slides credentials A master Google Slides deck with placeholder tokens (e.g. {{Name}}, {{Company}}) A Drive folder for incoming CSV lead files ⚙️ Setup Instructions Import this workflow into your n8n instance. Configure the New Leads Arrived node to watch your CSV folder. Enter your Google credentials in the Drive, Sheets, and Slides nodes. Specify the master Slides template ID in the Copy Slides Template node. In Create Custom Presentation, map slide tokens to sheet column names. Disable “Keep Binary Data” in Copy Slides Template to conserve memory. Upload a sample CSV (with headers like Name, Company, Metric) to test. 🎨 How to Customize Add or remove variables by editing the CSV headers and updating the mapping in Merge Data for new Lead Document. Insert an AI/natural‑language node before slide creation to generate more advanced and personalized text blocks. Use SplitInBatches to throttle API calls and avoid rate‑limit errors. Add error‑handling branches to capture and log failed operations. 🔐 Security and Privacy The workflow uses placeholder variables for file and folder IDs, so no actual IDs are exposed in the template. Ensure OAuth scopes are limited to only the required Google APIs.
by JPres
👥 Who Is This For? Content creators, marketing teams, and channel managers who want a simple, hands‑off solution to upload videos and automatically generate optimized metadata from video transcripts. 🛠 What Problem Does This Solve? Manual video uploads with proper metadata creation is time‑consuming and repetitive. This workflow fully automates: Monitoring a specific Google Drive folder for new video uploads Seamless YouTube upload processing Transcript extraction for context understanding AI‑powered generation of titles, descriptions, and tags Metadata application to uploaded videos without manual intervention 🔄 Node‑by‑Node Breakdown | Step | Node Purpose | |------|---------------------------------------------------------------------| | 1 | New Video? (Trigger) – Monitors specified Google Drive folder | | 2 | Download New Video – Retrieves the video file from Google Drive | | 3 | Upload to YouTube – Uploads the video to YouTube with initial settings | | 4 | Get Transcript – Extracts transcript from the uploaded video | | 5 | Adjust Transcript Format – Formats raw transcript for processing | | 6 | Create Description – Generates SEO‑optimized description | | 7 | YT Tags (Message Model) – Creates relevant tags based on content | | 8 | YT Title (Message Model) – Generates compelling title | | 9 | Define File Path Upload Format (Optional) – Structures data paths | | 10 | Update Video’s Metadata – Applies generated title, description, tags| ⚙️ Pre‑conditions / Requirements n8n with Google Drive and YouTube API credentials configured (stored as n8n credentials/variables; no hard‑coded IDs) Dedicated Google Drive folder for video uploads YouTube channel with proper upload permissions AI service access for transcript processing and metadata generation Sufficient storage for temporary video handling ⚙️ Setup Instructions Import this workflow into your n8n instance. Configure Google Drive credentials; reference folder ID via n8n variable (do not hard‑code). Set up YouTube API credentials with upload and edit permissions. Specify the target Google Drive folder ID in the New Video? trigger node (via variable). Configure AI service credentials for transcript and metadata generation. Adjust message templates for title, description, and tag creation. Test with a small video file before production use. 🎨 How to Customize Modify AI prompts to match your channel’s tone and style. Add conditional logic based on video categories or naming conventions. Implement notification systems to alert when uploads complete. Create custom metadata templates for different content types. Include timestamps or chapter markers based on transcript analysis. Add social media sharing nodes to announce new uploads. ⚠️ Important Notes Video quality is preserved through the upload process. Consider YouTube API quotas when handling multiple uploads. Transcript quality affects metadata generation results. Videos are initially uploaded without visibility adjustments. Processing time depends on video length and transcript complexity. 🔐 Security and Privacy Store API credentials and folder IDs as n8n Credentials/Variables—remove any hard‑coded tokens or IDs. Video files are processed temporarily and not stored permanently. Limit Google Drive folder access to authorized users only. Manage YouTube upload permissions carefully (use OAuth/service accounts). Ensure compliance with organizational data‑handling policies.
by Yang
Who is this for? This template is for sales teams, agencies, or local service providers who want to quickly generate cold outreach lists and automatically call local businesses with a Vapi AI assistant. It’s perfect for automating cold calls from scraped local listings with no manual dialing or research. What problem is this workflow solving? Finding leads and initiating outreach calls can be time-consuming. This workflow automates the process: it scrapes business listings from Google Maps using Dumpling AI, extracts phone numbers, filters out incomplete data, formats the numbers, and uses Vapi to make outbound AI-powered calls. Every call is logged in Google Sheets for follow-up and tracking. What this workflow does Starts manually and pulls search queries (e.g., "plumbers in Austin") from Google Sheets. Sends each query to Dumpling AI’s Google Maps scraping endpoint. Splits the returned business data into individual leads. Extracts key info like business name, website, and phone number. Filters to only keep leads with valid phone numbers. Formats phone numbers for Vapi dialing (adds +1). Calls each business using Vapi AI. Logs each successful call in a Google Sheet. Setup Google Sheets Setup Create a sheet with business search queries in the first column (e.g., best+restaurants+in+Chicago) Make sure the tab name is set and authorized in your credentials. Connect your Google Sheets account in the Get Search Keywords from Google Sheets node. Dumpling AI Setup Go to dumplingai.com Generate an API Key and connect it as a header token in the Scrape Google Map Businesses using Dumpling AI node Vapi Setup Sign into Vapi and create an assistant Get your assistantId and phoneNumberId Insert these into the JSON payload of the Initiate Vapi AI Call to Business node Add your Vapi API key to the credentials section Call Logging Create another tab in your sheet (e.g., “leads”) with these headers: company name phone number website This will be used in the Log Called Business Info to Sheet node How to customize this workflow to your needs Modify the business search terms in your Google Sheet to target specific industries or locations. Add filters to exclude certain businesses based on ratings, keywords, or location. Update your Vapi assistant script to match the type of outreach or pitch you’re using. Add additional integrations (e.g., CRM logging, Slack notifications, follow-up emails). Change the trigger to run on a schedule or webhook instead of manually. Nodes and Functions Breakdown Start Workflow Manually: Initiates the automation manually for testing or controlled runs. Get Search Keywords from Google Sheets: Reads search phrases from the spreadsheet. Scrape Google Map Businesses using Dumpling AI: Sends each search query to Dumpling AI and receives matching local business data. Split Each Business Result: Breaks the returned array of businesses into individual records for processing. Extract Business Name, Phone and website: Extracts title, phone, and website from each business record. Filter Valid Phone Numbers Only: Ensures only entries with a phone number move forward. Format Phone Number for Calling: Adds a +1 country code and strips non-numeric characters. Initiate Vapi AI Call to Business: Uses the business name and number to initiate a Vapi AI outbound call. Log Called Business Info to Sheet: Appends business details into a Google Sheet for tracking. Notes You must have valid API keys and authorized connections for Dumpling AI, Google Sheets, and Vapi. Make sure to handle API rate limits if you're running the workflow on large datasets. This workflow is optimized for US-based leads (+1 country code); adjust the formatting node if calling internationally.
by Don Jayamaha Jr
🕒 Evaluate Tesla (TSLA) price action and market structure on the 1-hour timeframe using 6 real-time indicators. This sub-agent is designed to feed mid-term technical insights into the Tesla Financial Market Data Analyst Tool. It uses GPT-4.1 to interpret Alpha Vantage indicator data delivered via secure webhooks. ⚠️ This workflow is not standalone and is executed via Execute Workflow. 🔌 Requires: Tesla Quant Technical Indicators Webhooks Tool Alpha Vantage Premium API Key 🔧 Connected Indicators This tool fetches and analyzes the latest 20 datapoints for: RSI (Relative Strength Index)** MACD (Moving Average Convergence Divergence)** BBANDS (Bollinger Bands)** SMA (Simple Moving Average)** EMA (Exponential Moving Average)** ADX (Average Directional Index)** 📋 Sample Output { "summary": "TSLA is gaining strength on the 1-hour chart. RSI is rising, MACD has crossed bullish, and BBANDS are widening.", "timeframe": "1h", "indicators": { "RSI": 62.1, "BBANDS": { "upper": 176.90, "lower": 169.70, "middle": 173.30, "close": 176.30 }, "SMA": 174.20, "EMA": 175.60, "ADX": 27.5, "MACD": { "macd": 0.84, "signal": 0.65, "histogram": 0.19 } } } 🧠 Agent Components | Component | Role | | ------------------------------ | -------------------------------------------------- | | 1hour Data | Pulls Alpha Vantage indicator data via webhook | | Tesla 1hour Indicators Agent | Interprets signals using structured GPT-4.1 prompt | | OpenAI Chat Model | GPT-4.1 LLM performs analysis | | Simple Memory | Maintains session context | 🛠️ Setup Instructions Import Workflow into n8n Name it: Tesla_1hour_Indicators_Tool Install the Webhook Fetcher Tool 👉 Required: Tesla_Quant_Technical_Indicators_Webhooks_Tool This agent expects webhook /1hourData to return pre-cleaned data Add Credentials Alpha Vantage Premium API Key (via HTTP Query Auth) OpenAI GPT-4.1 credentials Configure for Sub-Agent Use Triggered only via Execute Workflow from: 👉 Tesla Financial Market Data Analyst Tool Inputs: message (optional) sessionId (required for memory linkage) 📌 Sticky Notes Overview 🟢 Trigger Setup – Activated only by the parent agent 📊 1h Webhook Fetcher – Calls Alpha Vantage via secured endpoint 🧠 AI Agent Summary – Interprets trend/momentum from indicator data 🔗 GPT Model Notes – GPT-4.1 parses and explains technical alignment 📘 Documentation Sticky – Embedded in canvas with full walkthrough 🔐 Licensing & Support © 2025 Treasurium Capital Limited Company This tool is part of a proprietary multi-agent AI architecture. No commercial reuse or redistribution permitted. 🔗 Author: Don Jayamaha 🔗 Templates: https://n8n.io/creators/don-the-gem-dealer/ 🚀 Detect TSLA trend shifts and validate setups with 1-hour technical clarity—powered by Alpha Vantage + GPT-4.1. This tool is required by the Tesla Financial Market Data Analyst Tool.
by Bao Duy Nguyen
Who is this for? This template is ideal for developers, DevOps engineers, and automation managers who manage their n8n workflows using GitHub. It helps teams streamline their CI/CD automation by syncing changes from GitHub directly into n8n after a pull request (PR) is merged. What problem is this workflow solving? Manually restoring workflows after reviewing and merging code in GitHub can be tedious and error-prone. This workflow solves that by automating the restore process, ensuring that any new or updated workflow committed to your GitHub repo is automatically imported into your n8n environment. What this workflow does Triggers when a GitHub pull request is closed and merged. Fetches the details of the merge commit. Retrieves the list of added and modified workflow files. Downloads and decodes each workflow file. Creates or updates** the corresponding workflow in your n8n instance automatically. Setup Connect GitHub: Use the GitHub Trigger node and configure GitHub API credentials. Note: I'd recommended to use GitHub PAT (Personal Access Token) classic with repo and admin:repo_hook permission scopes enabled. Connect n8n API: Provide your n8n API credentials in the n8n nodes. - Check this doc Set repository variables: Update github_owner and repo_name in the Define Local Variables node. Enable webhook: Make sure your GitHub repository has a webhook for pull_request events pointing to this workflow. How to customize this workflow to your needs Modify filters to handle only certain branches or file paths. Add Slack or email notifications to confirm successful imports. Insert logging or version tagging for better traceability. Extend with conditional logic for workflow testing before applying changes. This automated flow provides a seamless CI/CD loop between GitHub and n8n, empowering teams to manage workflow versioning efficiently and securely.