Workflow Templates

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically monitors competitor pricing changes and website updates to keep you informed of market movements. It saves you time by eliminating the need to manually check competitor websites and provides alerts only when actual changes occur, preventing information overload. Overview This workflow automatically scrapes competitor pricing pages (like ClickUp) and compares current pricing with previously stored data. It uses Bright Data to access competitor websites without being blocked and AI to intelligently extract pricing information, updating your tracking spreadsheet only when changes are detected. Tools Used n8n**: The automation platform that orchestrates the workflow Bright Data**: For scraping competitor websites without being blocked OpenAI**: AI agent for intelligent pricing data extraction and parsing Google Sheets**: For storing and comparing historical pricing data How to Install Import the Workflow: Download the .json file and import it into your n8n instance Configure Bright Data: Add your Bright Data credentials to the MCP Client node Set Up OpenAI: Configure your OpenAI API credentials Configure Google Sheets: Connect your Google Sheets account and set up your pricing tracking spreadsheet Customize: Set your competitor URLs and pricing monitoring schedule Use Cases Product Teams**: Monitor competitor feature and pricing changes for strategic planning Sales Teams**: Stay informed of competitor pricing to adjust sales strategies Marketing Teams**: Track competitor messaging and positioning changes Business Intelligence**: Build comprehensive competitor analysis databases Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #competitoranalysis #pricingmonitoring #brightdata #webscraping #competitortracking #marketintelligence #n8nworkflow #workflow #nocode #pricetracking #businessintelligence #competitiveanalysis #marketresearch #competitormonitoring #pricingdata #websitemonitoring #competitorpricing #marketanalysis #competitorwatch #pricingalerts #businessautomation #competitorinsights #markettrends #pricingchanges #competitorupdates #strategicanalysis #marketposition #competitiveintelligence

Introducing the Qualys Scan Slack Report Subworkflow—a robust solution designed to automate the generation and retrieval of security reports from the Qualys API. This workflow is a sub workflow of the Qualys Slack Shortcut Bot workflow. It is triggered when someone fills out the modal popup in slack generated by the Qualys Slack Shortcut Bot. When deploying this workflow, use the Demo Data node to simulate the data that is input via the Execute Workflow Trigger. That data flows into the Global Variables Node which is then referenced by the rest of the workflow. It includes nodes to Fetch the Report IDs and then Launch a report, and then check the report status periodically and download the completed report, which is then posted to Slack for easy access. For Security Operations Centers (SOCs), this workflow provides significant benefits by automating tedious tasks, ensuring timely updates, and facilitating efficient data handling. How It Works Fetch Report Templates:** The "Fetch Report IDs" node retrieves a list of available report templates from Qualys. This automated retrieval saves time and ensures that the latest templates are used, enhancing the accuracy and relevance of reports. Convert XML to JSON:** The response is converted to JSON format for easier manipulation. This step simplifies data handling, making it easier for SOC analysts to work with the data and integrate it into other tools or processes. Launch Report:** A POST request is sent to Qualys to initiate report generation using specified parameters like template ID and report title. Automating this step ensures consistency and reduces the chance of human error, improving the reliability of the reports generated. Loop and Check Status:** The workflow loops every minute to check if the report generation is complete. Continuous monitoring automates the waiting process, freeing up SOC analysts to focus on higher-priority tasks while ensuring they are promptly notified when reports are ready. Download Report:** Once the report is ready, it is downloaded from Qualys. Automated downloading ensures that the latest data is always available without manual intervention, improving efficiency. Post to Slack:** The final report is posted to a designated Slack channel for quick access. This integration with Slack ensures that the team can promptly access and review the reports, facilitating swift action and decision-making. Get Started Ensure your Slack and Qualys integrations are properly set up. Customize the workflow to fit your specific reporting needs. Link to parent workflow Link to Vulnerability Scan Trigger Need Help? Join the discussion on our Forum or check out resources on Discord! Deploy this workflow to streamline your security report generation process, improve response times, and enhance the efficiency of your security operations.

Who is this for? For developers and organizations that need to convert XLSX files to PDF. What problem is this workflow solving? The file format conversion problem. What this workflow does Downloads the XLSX file from the web. Converts the XLSX file to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Optionally, additional Body Parameters can be added for the converter.

Automated AI Research Assistant: From Query to Polished Report with Jina & Gemini Turn a single research question into a comprehensive, multi-source report with proper citations. This workflow automates the entire research process by leveraging the web-crawling power of Jina AI and the advanced reasoning capabilities of Google's Gemini models. Simply input your query, and this AI-powered assembly line will search the web, scrape relevant sources, summarize the content, draft a structured research paper, and finally, evaluate and polish the report for accuracy and formatting. ✨ Key Features 🔎 Dynamic Web Search**: Kicks off by searching the web with Jina AI based on your initial query. 📚 Multi-Source Content Scraping**: Automatically reads and extracts content from the top 10 search results. 🧠 AI-Powered Summarization**: Uses a Gemini agent to intelligently summarize each webpage, retaining the core information. ✍️ Automated Report Generation**: A specialized "Generator Agent" synthesizes the summarized data into a structured research paper, complete with an executive summary, introduction, discussion, and conclusion. ✅ Citation & Quality Verification**: A final "Evaluator Agent" meticulously checks the generated report for citation accuracy, logical flow, and markdown formatting, delivering a polished final document. 📈Rate-Limit Ready**: Includes a configurable Wait node to ensure stable execution when dealing with multiple API calls. 📝 What This Workflow Does This workflow is designed to be your personal research assistant. It addresses the time-consuming process of gathering, reading, and synthesizing information from multiple online sources. Instead of spending hours manually searching, reading, and citing, you can delegate the entire task to this workflow and receive a well-structured and cited report as the final output. It's perfect for students, researchers, content creators, and analysts who need to quickly compile information on any given topic. ⚙️ How It Works (Step-by-Step) Initiate with a Query: The workflow starts when you send your research question or topic to the Chat Trigger node. Search the Web: The user's query is passed to the Jina AI node, which performs a web search and returns the top 10 most relevant URLs. Scrape, Summarize, Repeat: The workflow then loops through each URL: Read Content: The Jina AI node scrapes the full text content from the URL. Summarize: A Summarizer Agent powered by Google Gemini reads the scraped content and the original user query, then generates a concise summary. Wait: A one-second pause helps to avoid hitting API rate limits before processing the next URL. Aggregate the Knowledge: Once the loop is complete, a Code node gathers all 10 individual summaries into a single, neatly structured list. Draft the Research Report: This aggregated data is fed to the Generator Agent. Following a detailed prompt, this Gemini-powered agent writes a full research report, structuring it with headings and adding inline citations for every piece of information it uses. Evaluate and Finalize: The generated draft is passed to the final Evaluator Chain. This agent acts as a quality control supervisor. It verifies that all claims are correctly cited, refines the content for clarity and academic tone, and polishes the markdown formatting to produce the final, ready-to-use report. 🚀 How to Use This Workflow Credentials: Click on Use template, then configure your credentials for the following nodes: Jina AI: You will need a Jina AI API key for the Search web and Read URL content nodes. Get your key from here: JinaAI API Key Google Gemini: You will need a Google Gemini API key for the Summarizer Model, Generator Model, and Evaluator Model nodes. Get your key from here: Gemini API Key Activate Workflow: Make sure the workflow is active in your n8n instance. Start Research: Send a chat message with your research topic to the webhook URL provided in the When chat message received node. Get Your Report: Check the output of the final node, Evaluator Chain, to find your completed and polished research report. Nodes Used Chat Trigger Jina AI Code (Python) Split in Batches (Looping) Wait AI Agent Basic LLM Chain Google Gemini Chat Model

This workflow allows you to receive updates about the positiong of the ISS and add it to a table in TimescaleDB. Cron node: The Cron node triggers the workflow every minute. You can configure the time based on your use-case. HTTP Request node: This node makes an HTTP Request to an API that returns the position of the ISS. Based on your use-case you may want to fetch data from a different URL. Enter the URL in the URL field. Set node: In the Set node we set the information that we need in the workflow. Since we only need the timestamp, latitude, and longitude we set this in the node. If you need other information, you can set them in this node. TimescaleDB node: This node stores the information in a table named iss. You can use a different table as well.

Who is this for? For developers and organizations that need to convert web page to PDF. What problem is this workflow solving? The web page conversion to PDF problem. What this workflow does Converts web page to PDF. Stores the PDF file in the local file system. How to customize this workflow to your needs Open the HTTP Request node. Adjust the URL parameter (all endpoints can be found here). Add your secret to the Query Auth account parameter. Please create a ConvertAPI account to get an authentication secret. Change the parameter url to the webpage you want to convert to pdf Optionally, additional Body Parameters can be added for the converter.

Do you want to avoid communication problems when launching phone calls? This workflow verifies landline and mobile phone numbers using the uProc Get Parsed and validated phone tool with worldwide coverage. You need to add your credentials (Email and API Key - real -) located at Integration section to n8n. Node "Create Phone Item" can be replaced by any other supported service with phone values, like databases (MySQL, Postgres), or Typeform. The "uProc" node returns the next fields per every parsed and validated phone number: country_prefix: contains the international country phone prefix number. country_code: contains the 2-digit ISO country code of the phone number. local_number: contains the phone number without international prefix. formatted: contains a formatted version of the phone number, according to country detected. valid: detects if the phone number has a valid format and prefix. type: the phone number type (mobile, landline, or something else). "If" node checks if the phone number is valid. You can use the result to mark invalid phone numbers in your database or discard them from future telemarketing campaigns.

Vector Database as a Big Data Analysis Tool for AI Agents Workflows from the webinar "Build production-ready AI Agents with Qdrant and n8n". This series of workflows shows how to build big data analysis tools for production-ready AI agents with the help of vector databases. These pipelines are adaptable to any dataset of images, hence, many production use cases. Uploading (image) datasets to Qdrant Set up meta-variables for anomaly detection in Qdrant Anomaly detection tool KNN classifier tool For anomaly detection 1. This is the first pipeline to upload an image dataset to Qdrant. The second pipeline is to set up cluster (class) centres & cluster (class) threshold scores needed for anomaly detection. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant to detect if it's an anomaly to the uploaded dataset. For KNN (k nearest neighbours) classification 1. This is the first pipeline to upload an image dataset to Qdrant. The second is the KNN classifier tool, which takes any image as input and classifies it on the uploaded to Qdrant dataset. To recreate both You'll have to upload crops and lands datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to Qdrant Cloud (you can use Free Tier cluster), Voyage AI API & Google Cloud Storage. [This workflow] Batch Uploading Images Dataset to Qdrant This template imports dataset images from Google Could Storage, creates Voyage AI embeddings for them in batches, and uploads them to Qdrant, also in batches. In this particular template, we work with crops dataset. However, it's analogous to uploading lands dataset, and in general, it's adaptable to any dataset consisting of image URLs (as the following pipelines are). First, check for an existing Qdrant collection to use; otherwise, create it here. Additionally, when creating the collection, we'll create a payload index, which is required for a particular type of Qdrant requests we will use later. Next, import all (dataset) images from Google Cloud Storage but keep only non-tomato-related ones (for anomaly detection testing). Create (per batch) embeddings for all imported images using the Voyage AI multimodal embeddings API. Finally, upload the resulting embeddings and image descriptors to Qdrant via batch upload.

This workflow automates the process of fetching agricultural transaction data from the Taiwan Agricultural Products Open Data Platform and storing it in a Google Sheets document for further analysis. Key Features Manual Trigger: Allows manual execution of the workflow to control when data is fetched. HTTP Request: Sends a request to the Open Data Platform's API to retrieve detailed transaction data, including: Pricing (Upper, Middle, Lower, Average) Transaction quantities Crop and market details Split Out Node: Processes each record individually, ensuring accurate handling of every data entry. Google Sheets Integration: Appends the data into a structured Google Sheets document for easy access and analysis. Node Configurations 1. Manual Trigger Purpose**: Start the workflow manually. Configuration**: No setup needed. 2. HTTP Request Purpose**: Fetch agricultural data. Configuration**: URL: https://data.moa.gov.tw/api/v1/SheepQuotation Query Parameters: Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> Headers: accept: application/json 3. Split Out Purpose**: Split the API response data array into individual items. Configuration**: Field to Split Out: Data 4. Google Sheets Purpose**: Append the data to Google Sheets. Configuration**: Operation: Append Document ID: <your_document_id> Sheet Name: Sheet1 Mapped Fields: TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 此 Workflow 從 台灣農業產品開放資料平臺 獲取農產品交易數據，並將其儲存到 Google Sheets 文件 中進行進一步分析。 主要功能 Manual Trigger：允許手動執行工作流程，以控制數據獲取的時間。 HTTP Request：向開放資料平臺的 API 發送請求，獲取詳細的交易數據，包括： 價格 (Upper, Middle, Lower, Average) 交易數量 作物和市場詳細資料 Split Out Node：逐筆處理每一筆記錄，確保數據準確無誤。 Google Sheets Integration：將數據追加到結構化的 Google Sheets 文件中，方便存取和分析。 節點設定 1. Manual Trigger 用途**：手動啟動工作流程。 設定**：無需額外設定。 2. HTTP Request 用途**：抓取農產品數據。 設定**： URL: https://data.moa.gov.tw/api/v1/SheepQuotation 查詢參數 (Query Parameters)： Start_time: 2024/12/01 End_time: 2024/12/31 MarketName: 台北二 api_key: <your_api_key> 標頭 (Headers)： accept: application/json 3. Split Out 用途**：將 API 回應的數據陣列分解為個別項目。 設定**： Field to Split Out: Data 4. Google Sheets 用途**：將數據追加至 Google Sheets。 設定**： Operation：Append Document ID：<your_document_id> Sheet Name：Sheet1 映射欄位 (Mapped Fields)： TransDate, TcType, CropCode, CropName, MarketCode, MarketName Upper_Price, Middle_Price, Lower_Price, Avg_Price, Trans_Quantity 請多利用 Curl Import 功能 例如 curl -X GET "https://data.moa.gov.tw/api/v1/AgriProductsTransType/?Start_time=114.01.01&End_time=114.01.01&MarketName=%E5%8F%B0%E5%8C%97%E4%BA%8C" -H "accept: application/json" 農業資料開放平台 文件

Quick overview This workflow turns science keywords from Google Sheets into researched Ghost newsletter posts by discovering and screening YouTube videos with Apify, validating claims using Brave Search, storing sources in Qdrant with Cohere embeddings, drafting content with LLMs, and publishing via a custom Ghost HTTP endpoint. How it works Runs either manually to qualify “FAIR GAME” keywords from Google Sheets or on a schedule to start the automated publishing pipeline. Uses Brave Search with an LLM agent to research each keyword’s current context and writes a structured relevance, domain, and angle assessment back to the Keywords sheet. On schedule, randomly selects an eligible keyword, uses Apify’s YouTube Scraper to fetch recent videos with English transcripts, and appends normalized candidates to the Candidate Videos sheet. Scores each candidate’s virality from views, engagement, channel size, and publish age, marks rows as VIRAL or NOT VIRAL, and archives NOT VIRAL items to an Archive sheet. Reviews VIRAL candidates with an editorial LLM agent to select exactly one evidence-worthy video (or reject all), updates statuses and reasoning in Google Sheets, and archives rejected videos. For the selected video, discovers 5–8 supplementary sources via Brave Search, extracts readable text with Jina AI, embeds and upserts source chunks into Qdrant, and drafts a Ghost block-based article using the transcript plus Qdrant retrieval. Queues the draft in a Blog Outputs sheet as PENDING, searches Brave Image results for each image placeholder, uses an LLM agent to pick acceptable images (or a fallback), patches the blocks, and publishes the final post and newsletter through the Ghost publisher HTTP endpoint. Setup Create one Google Sheets spreadsheet with tabs for Keywords, Candidate Videos, Blog Outputs, and Archive, and ensure the header names match the workflow’s expected columns. Add Google Sheets credentials and set up a Global Constants credential (n8n-nodes-globals) with your Sheet ID, tab names, n8n base URL, Qdrant base URL, GhostPublisherURL, BlogName (Qdrant collection), and a licensed FallbackImageURL. Add credentials for Brave Search, Apify, Qdrant (REST and/or node), Cohere (embed endpoint), Jina AI, Redis (for agent memory), and at least one configured chat model provider used by the agents (OpenAI, Anthropic, and/or Mistral). Deploy or configure the custom Ghost publishing/bridge endpoint referenced by GhostPublisherURL to accept the JSON payload (title, excerpt, status, visibility, feature_image, content, seo, newsletter). Activate the workflow so the internal webhooks are reachable, and test safely by switching Ghost publishing to draft and disabling newsletter.send before enabling live delivery.

Workflow Overview This workflow automates the process of scraping Trustpilot reviews, extracting key details, analyzing sentiment, and saving the results to Google Sheets. It uses OpenAI for sentiment analysis and HTML parsing for review extraction. How It Works 1. Scrape Trustpilot Reviews HTTP Request**: Fetches review pages from Trustpilot (https://it.trustpilot.com/review/{{company_id}}). Paginates through pages (up to max_page limit). HTML Parsing**: Extracts review URLs using CSS selectors Splits the URLs into individual review links. 2. Extract Review Details Information Extractor**: Uses DeepSeek to extract structured data from the review: Author: Name of the reviewer. Rating: Numeric rating (1-5). Date: Review date in YYYY-MM-DD format. Title: Review title. Text: Full review text. Total Reviews: Number of reviews by the user. Country: Reviewer’s country (2-letter code). 3. Sentiment Analysis Sentiment Analysis Node**: Uses OpenAI to classify the review text as Positive, Neutral, or Negative. Example output: { "category": "Positive", "confidence": 0.95 } 4. Save to Google Sheets Google Sheets Node**: Appends or updates the extracted data to a Google Sheet Set Up Steps 1. Configure Trustpilot Scraping Edit Fields1 Node**: Set company_id to the Trustpilot company name Set max_page to limit the number of pages scraped. 2. Configure Google Sheets Google Sheets Node**: Update the documentId with your Google Sheet ID Ensure the sheet has the required columns (Id, Data, Nome, etc.). 3. Configure OpenAI OpenAI Chat Model Node**: Add your OpenAI API key. Sentiment Analysis Node**: Ensure the categories match your desired sentiment labels (Positive, Neutral, Negative). Key Components Nodes**: HTTP Request/HTML: Scrape and parse Trustpilot reviews. Information Extractor: Extract structured review data using DeepSeek. Sentiment Analysis: Classify review sentiment. Google Sheets: Save and update review data. Credentials**: OpenAI API key. DeepSeek API key. Google Sheets OAuth2.

This workflow implements a Retrieval-Augmented Generation (RAG) system that: Stores vectorized documents in Qdrant, Retrieves relevant content based on user input, Generates AI answers using Google Gemini, Automatically cites the document sources (from Google Drive). Workflow Steps Create Qdrant Collection A REST API node creates a new collection in Qdrant with specified vector size (1536) and cosine similarity. Load Files from Google Drive The workflow lists all files in a Google Drive folder, downloads them as plain text, and loops through each. Text Preprocessing & Embedding Documents are split into chunks (500 characters, with 50-character overlap). Embeddings are created using OpenAI embeddings (text-embedding-3-small assumed). Metadata (file name and ID) is attached to each chunk. Store in Qdrant All vectors, along with metadata, are inserted into the Qdrant collection. Chat Input & Retrieval When a chat message is received, the question is embedded and matched against Qdrant. Top 5 relevant document chunks are retrieved. A Gemini model is used to generate the answer based on those sources. Source Aggregation & Response File IDs and names are deduplicated. The AI response is combined with a list of cited documents (filenames). Final output: AI Response Sources: ["Document1", "Document2"] Main Advantages End-to-end Automation**: From document ingestion to chat response generation, fully automated with no manual steps. Scalable Knowledge Base**: Easy to expand by simply adding files to the Google Drive folder. Traceable Responses**: Each answer includes its source files, increasing transparency and trustworthiness. Modular Design**: Each step (embedding, storage, retrieval, response) is isolated and reusable. Multi-provider AI**: Combines OpenAI (for embeddings) and Google Gemini (for chat), optimizing performance and flexibility. Secure & Customizable**: Uses API credentials and configurable chunk size, collection name, etc. How It Works Document Processing & Vectorization The workflow retrieves documents from a specified Google Drive folder. Each file is downloaded, split into chunks (using a recursive text splitter), and converted into embeddings via OpenAI. The embeddings, along with metadata (file ID and name), are stored in a Qdrant vector database under the collection negozio-emporio-verde. Query Handling & Response Generation When a user submits a chat message, the workflow: Embeds the query using OpenAI. Retrieves the top 5 relevant document chunks from Qdrant. Uses Google Gemini to generate a response based on the retrieved context. Aggregates and deduplicates the source file names from the retrieved chunks. The final output includes both the AI-generated response and a list of source documents (e.g., Sources: ["FAQ.pdf", "Policy.txt"]). Set Up Steps Configure Qdrant Collection Replace QDRANTURL and COLLECTION in the "Create collection" HTTP node to initialize the Qdrant collection with: Vector size: 1536 (OpenAI embedding dimension). Distance metric: Cosine. Ensure the "Clear collection" node is configured to reset the collection if needed. Google Drive & OpenAI Integration Link the Google Drive node to the target folder (Test Negozio in this example). Verify OpenAI and Google Gemini API credentials are correctly set in their respective nodes. Metadata & Output Customization Adjust the "Aggregate" and "Response" nodes if additional metadata fields are needed. Modify the "Output" node to format the response (e.g., changing Sources: {{...}} to match your preferred style). Testing Trigger the workflow manually to test document ingestion. Use the chat interface to verify responses include accurate source attribution. Note: Replace placeholder values (e.g., QDRANTURL) with actual endpoints before deployment. Need help customizing? Contact me for consulting and support or add me on Linkedin.