This workflow is specifically designed for YouTube content creators, enabling automated collection of video information and comment data. It utilizes AI language models for sentiment analysis and topic extraction, generating structured reports. The reports cover video performance, audience sentiment, trending topics, and improvement suggestions, helping creators understand audience needs and optimize content strategies, thereby enhancing video engagement and dissemination effectiveness.

This workflow automatically extracts text content from newly uploaded PDF files and images in a specified Google Drive folder. It uses AI models from Google Vertex AI (Gemini) and Openrouter for intelligent analysis, ultimately converting the structured data into CSV format and uploading it back to Google Drive. It supports multiple file formats, enhances text recognition accuracy, and fully automates data processing, making it suitable for fields such as finance and operations, significantly improving work efficiency and data accuracy.

This workflow automatically captures structured data information from Amazon's best-selling electronics list. It combines web crawling and advanced AI extraction technology to transform complex web content into clear product information. Users receive the organized data in real-time via Webhook, making it suitable for scenarios such as e-commerce market analysis and product operation decision-making. It effectively reduces manual intervention, enhances data processing efficiency, and supports precise decision-making and content innovation.

This workflow automatically collects swimming, cycling, and running data by monitoring sports activities on Strava in real-time. It utilizes a powerful AI model for in-depth analysis, generating personalized training feedback and improvement suggestions. The analysis results are output in a structured HTML format and sent through multiple channels such as email or WhatsApp, ensuring that users receive timely and scientific fitness guidance. This intelligent training assistance solves the cumbersome process of manual data import, enhancing athletes' training efficiency and performance.

This workflow utilizes AI intelligent agents and the official YouTube API to automatically mine trending videos in specific fields from the past two days. Through multiple rounds of intelligent searches and data analysis, it extracts key metrics such as view counts, likes, and comments, providing insights into content tags and thematic patterns to help creators grasp popular directions. It addresses the challenge creators face in quickly capturing real-time trending content, enhancing the efficiency and accuracy of topic selection, and providing data-driven references for content creation.

This workflow automatically retrieves the latest time records through the Toggl trigger, enabling real-time monitoring and collection of work time data, significantly enhancing the automation and efficiency of time management. It addresses the cumbersome and error-prone issues of manually tracking work hours, making it suitable for freelancers, project managers, and team leaders. It helps them gain real-time insights into time investment, optimize time allocation and resource scheduling, and improve data accuracy and management efficiency.

This workflow automatically collects and analyzes usage data of creators and their works, generating detailed ranking reports to help users understand the most popular workflows and active contributors within the community. Utilizing AI for intelligent processing, it outputs structured Markdown reports to simplify data comprehension, promote knowledge sharing and community collaboration. It is suitable for community managers, workflow developers, and novice users, enhancing engagement and optimizing strategies.

This workflow is manually triggered to automatically retrieve website traffic data from Google Analytics, including session counts and visitor countries, and stores the organized information in Airtable. It addresses the issues of traditional data dispersion and management difficulties, achieving automated data collection and centralized storage, thereby improving the efficiency and accuracy of data processing. It is suitable for website operators, data analysts, and marketing teams.

This workflow enables the automatic synchronization of product data from the Shopify e-commerce platform to Google Sheets. It retrieves product information in bulk through the GraphQL interface, including titles, tags, descriptions, and prices, and automatically organizes and writes this data into a specified Google Sheets document. It supports incremental synchronization to avoid duplicate data retrieval and updates daily on a schedule, significantly enhancing data management efficiency. This helps the e-commerce team manage inventory and pricing more conveniently, reduces labor costs, and improves decision-making capabilities.

This workflow provides users with AI-based intelligent data analysis of the OpenSea NFT market through the Telegram platform. Users can send query requests, and the system automatically identifies the needs and invokes specialized sub-agents to conduct various analyses, including market trends, NFT metadata, and transaction monitoring. By integrating OpenAI's intelligent reasoning, users can obtain structured market insights and data results in real-time, supporting complex multi-dimensional queries and enhancing the efficiency and accuracy of investment decisions and market research.

This workflow is designed to automate the extraction of blog and event data from a specified Squarespace website and to synchronize it in a structured manner to Google Sheets. Through scheduled triggers and paginated scraping, users can efficiently obtain complete data, avoiding errors and omissions that may occur during manual export processes. It is suitable for scenarios such as content operations, marketing, and data analysis, significantly enhancing data processing efficiency and ensuring the timeliness and accuracy of information.

This workflow automates the processing of qualified lead data and synchronizes it to Google Sheets, enhancing data management efficiency. It triggers through a Postgres database and filters out internal email addresses that do not meet the criteria, ensuring that only external customer information is retained. It is suitable for sales and marketing teams to access a list of qualified leads in real time, simplifying the data processing workflow, reducing the risk of manual entry, and helping to improve work efficiency and communication effectiveness. Users can customize the settings as needed to adapt to different business scenarios.

This workflow implements the functionality of automatically scraping book information from a specified website. It utilizes advanced technology to clean and extract HTML content, accurately obtaining book titles and prices, which are then organized in descending order by price. Ultimately, the data is converted into CSV format and sent via email to designated recipients. This process significantly enhances the efficiency of data collection, organization, and distribution, making it suitable for online bookstore operations, market research, and automated data processing needs, facilitating the quick acquisition and sharing of important information.

This workflow automatically generates 10 data entries after being manually triggered and processes them one by one. During the processing, flexible flow control is achieved through conditional judgments. When processing the 6th data entry, a specific operation is triggered, and the loop ends. This design effectively addresses the need for executing tasks on batch data one by one while allowing for immediate interruption of subsequent operations when specific conditions are met, thereby improving processing efficiency and intelligence. It is suitable for scenarios such as data cleaning and approval processes.

This workflow integrates Bright Data and Google Gemini AI to achieve intelligent web data scraping and processing. Users only need to input the target URL and format instructions, and the AI agent will automatically select the appropriate scraping tool, supporting multiple data format outputs, and push the results via Webhook. At the same time, the scraped content will be saved as a local file for easy subsequent analysis. This system lowers the technical barriers to web scraping, improves efficiency, and is suitable for various scenarios such as market research, content aggregation, and data analysis.

This workflow implements the automatic collection and sentiment analysis of customer feedback, ensuring that data processing is efficient and accurate. After customers submit feedback through a customized form, the system automatically utilizes AI technology for sentiment classification and integrates the analysis results with the original data, ultimately storing it in Google Sheets. This process not only enhances the response speed of the customer service team but also helps product managers and market researchers quickly gain insights into customer satisfaction and needs, facilitating improved business decision-making and service quality.

This workflow combines web data scraping and large language models to achieve structured data extraction and deep analysis of web pages. Users can automatically retrieve and parse web content, extract themes, identify trends, and conduct sentiment analysis, generating easy-to-understand reports. It supports saving results as local files and provides real-time notifications via Webhook, making it suitable for various scenarios such as media monitoring, market research, and data processing, significantly improving the efficiency and accuracy of data analysis.

The main function of this workflow is to automatically retrieve website traffic data from Google Analytics, analyzing page engagement, search performance, and country distribution over the past two weeks. By utilizing AI to intelligently interpret the data, it generates professional SEO optimization recommendations and saves the results to a Baserow database for easier management and tracking. This process simplifies data comparison and analysis, enhancing the efficiency and accuracy of SEO decision-making, making it highly suitable for website operators and digital marketing teams.

This workflow is designed to convert webpage HTML content into structured Markdown format and extract all links from the webpage. By utilizing the Firecrawl.dev API, it supports batch processing of URLs, automatically managing request rates to ensure stable and efficient content crawling and conversion. It is suitable for scenarios such as data analysis, content aggregation, and market research, helping users quickly acquire and process large amounts of webpage information, reducing manual operations and improving work efficiency.

The smart factory data generator periodically generates simulated operational data for factory machines, including machine ID, temperature, runtime, and timestamps, and sends it to a designated message queue via the AMQP protocol. This workflow effectively addresses the lack of real-time data sources in smart factory and industrial IoT environments, supporting developers and testers in system functionality validation, performance tuning, and data analysis without the need for real devices, thereby enhancing overall work efficiency.

This workflow is a web content scraping and processing tool that can automatically retrieve web page content from specified URLs and convert it into Markdown format. It supports two scraping modes: complete and simplified. The simplified mode reduces links and images to prevent excessively long content from wasting computational resources. The built-in error handling mechanism intelligently responds to request exceptions, ensuring the stability and accuracy of the scraping process. It is suitable for various scenarios such as AI chatbots, data scraping, and content summarization.

This workflow aims to automate the scraping of customer reviews for specified companies on Trustpilot, utilizing a vector database for efficient management and analysis. It employs the K-means clustering algorithm to identify review themes and applies a large language model for in-depth summarization. The final analysis results are exported to Google Sheets for easy sharing and decision-making within the team. This process significantly enhances the efficiency of customer review data processing, helping businesses quickly identify key themes and sentiment trends that matter to customers, thereby optimizing customer experience and product strategies.

This workflow automates the scraping and sentiment analysis of Twitter and external form content. It regularly monitors the latest tweets related to "strapi" or "n8n.io" and filters out unnecessary information. Using natural language processing technology, it intelligently assesses the sentiment of the text and automatically stores positively rated content in the Strapi content management system, enhancing data integration efficiency. It is suitable for brand reputation monitoring, market research, and customer relationship management, providing data support and high-quality content for decision-making.

This workflow automates the collection and structured processing of e-commerce product information. By scraping the HTML content of specified web pages, it intelligently extracts key information such as product names, descriptions, ratings, number of reviews, and prices using an AI model. The data is then cleaned and structured, with the final results stored in Google Sheets. This process significantly enhances the efficiency and accuracy of data collection, making it suitable for market research, e-commerce operations, and data analysis scenarios.