Google Analytics Template

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.

This workflow automatically fetches popular keywords and related information from Google Trends in the Italian region, filters out new trending keywords, and uses the jina.ai API to obtain relevant webpage content to generate summaries. Finally, the data is stored in Google Sheets as an editorial planning database. Through this process, users can efficiently monitor market dynamics, avoid missing important information, and enhance the accuracy and efficiency of keyword monitoring, making it suitable for content marketing, SEO optimization, and market analysis scenarios.

This workflow demonstrates how to automate the retrieval and processing of API data, specifically by making paginated requests to fetch the favorite projects of GitHub users. It supports automatic incrementing of page numbers, determining the end condition for data, and achieving complete data retrieval. Additionally, this process illustrates how to extract article titles from random Wikipedia pages, combining HTTP requests with HTML content extraction. It is suitable for scenarios that require batch scraping and processing of data from multiple sources, helping users efficiently build automated workflows.