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.

The Dashboard workflow automatically fetches and integrates key metrics from multiple platforms such as Docker Hub, npm, GitHub, and Product Hunt, updating and displaying them in a customized dashboard in real-time. It addresses the issues of data fragmentation and delayed updates that developers face when managing open-source projects, enhancing the efficiency and accuracy of data retrieval. This workflow is suitable for open-source project maintainers, product managers, and others, helping them to comprehensively monitor project health, optimize decision-making, and manage community operations.

This workflow automates the pagination retrieval and integration of contact data through the HubSpot CRM API, simplifying the complexity of manually managing pagination logic. Users only need to manually trigger the process, and the system will loop through requests for all paginated data and consolidate it into a complete list. This process prevents data omissions and enhances the efficiency and accuracy of data retrieval, making it suitable for various scenarios such as marketing, customer management, and data analysis, helping businesses manage customer resources more effectively.

This workflow automates the process of batch uploading contact data from a CSV file to Airtable. It supports real-time monitoring of newly uploaded files, automatically downloading and parsing the content. It can intelligently determine marketing campaign fields, batch create or update contact records, and update the upload status in real-time, ensuring efficient and accurate data management. This solution addresses the cumbersome and error-prone issues of manual imports, making it particularly suitable for marketing and sales teams.

This workflow focuses on generating and transforming simulated data, providing efficient data preprocessing capabilities. It splits the initial array-form simulated data into independent data items, facilitating subsequent processing and operations. It is suitable for testing and debugging during the process development phase, as well as for scenarios that require batch data processing. It can quickly address issues related to mismatched simulated data formats and item-by-item processing, enhancing the efficiency and flexibility of workflow design.

This workflow is designed to help businesses efficiently manage customer data by manually triggering the automatic retrieval of customer information. It allows for multi-condition filtering and classification distribution based on fields such as country and name. The workflow supports both single-condition and composite-condition judgments, enabling precise data filtering and multi-route processing. It includes detailed annotations for user understanding and configuration, making it suitable for various scenarios such as marketing, customer service, and data analysis. This enhances the automation and accuracy of data processing while reducing manual intervention.

This workflow automates the scraping of Yelp restaurant reviews to achieve efficient data extraction and summary generation. Utilizing advanced web crawling technology and AI language models, users can quickly obtain and analyze review information for their target businesses, simplifying the cumbersome process of traditional manual handling. It supports customizable URLs and data notifications, making it widely applicable in scenarios such as market research, user feedback analysis, and brand reputation management, significantly enhancing data application efficiency and user experience.

This workflow is designed to provide language learners with new words daily by automatically scraping popular articles from Hacker News, extracting and translating English words from them, and ultimately storing the selected bilingual vocabulary in a database to be sent to users via SMS. It addresses the challenges of vocabulary acquisition, timely content updates, and insufficient learning reminders, helping users efficiently accumulate new words and improve their language skills. It is suitable for various types of language learners and educational institutions.

This workflow allows users to manually trigger it to read the latest content from specified RSS feeds in real-time, enabling quick access to updates from websites or blogs. It resolves the cumbersome issue of manually visiting multiple web pages, streamlining the information retrieval process. It is suitable for content editors, social media managers, and individual users, enhancing the efficiency of information monitoring and providing a foundation for subsequent data processing.

This workflow is designed to automate the extraction and updating of business information. By reading business domain names from Google Sheets, it sequentially visits the corresponding websites and extracts HTML content. After intelligent cleaning, it utilizes artificial intelligence to generate the company's value proposition, industry classification, and market positioning. Ultimately, the structured data will be written back to Google Sheets, achieving real-time information updates. This process significantly enhances the efficiency and accuracy of data organization, helping users better conduct market analysis and customer management.

This workflow establishes clustering representative points and thresholds for crop image datasets using two methods, providing a foundation for anomaly detection. It utilizes vector database APIs and Python libraries for sparse matrix calculations, ensuring the efficient and accurate determination of cluster centers and thresholds. This approach is applicable in various scenarios such as agricultural smart monitoring and preprocessing for machine learning models, significantly enhancing the accuracy and reliability of anomaly detection while simplifying the complex clustering analysis process.

This workflow automates the generation of weekly Google Analytics data reports, focusing on comparing key performance indicators from the last 7 days with the same period last year. Utilizing AI technology for intelligent analysis and formatting, the reports can be pushed through multiple channels, including email and Telegram, helping users save time, gain insights into trends, and enhance report quality. It is suitable for website operations teams, data analysts, and management, supporting informed decision-making and efficient communication.

This workflow automatically fetches all comments for specified stories from Hacker News and stores the comment text vectors in a vector database. It clusters the comments using the K-means algorithm and utilizes the GPT-4 model to generate content summaries and sentiment analysis. Finally, the analysis results are exported to Google Sheets. This process efficiently handles a large volume of comments, helping users identify community hot topics and extract valuable feedback, making it suitable for various scenarios such as community management, product optimization, and data analysis.

This workflow automatically retrieves keyword ranking data through scheduled or manual triggers and uses custom code for trend analysis. The analyzed data is then sent to an artificial intelligence model for in-depth analysis, and the final results are stored in a low-code database for easier management and viewing. It integrates data collection, intelligent analysis, and result storage, enhancing the efficiency of SEO monitoring and optimization, making it suitable for SEO teams, digital marketers, and website administrators.

This workflow implements an intelligent chat agent by integrating the OpenAI GPT-4o language model with a Postgres database, allowing users to interact with Google Search Console data using natural language. It automatically parses user requests, generates corresponding API queries, and returns data in the form of Markdown tables. This tool simplifies the data access process and enhances user experience, making it suitable for website operators, SEO experts, and data analysts, enabling them to quickly obtain and analyze website performance data.

This workflow automates the collection and analysis of active data from top community creators and their workflows, generating detailed statistical reports. Utilizing advanced AI language models in conjunction with GitHub data, it provides clear reports in Markdown format, supporting various distribution methods such as email and cloud storage. This systematic process helps community managers efficiently identify outstanding creators, promotes knowledge sharing, enhances user experience, and drives the promotion and application of automated processes.

This workflow integrates AI technology with Google Sheets to enable intelligent customer data querying and analysis. Users can ask questions in natural language, and the AI agent will interpret the intent and invoke the appropriate tools to accurately return customer information, avoiding the inefficiencies and errors of traditional manual queries. The platform supports quick retrieval of column names, specified column values, and complete customer data, enhancing response speed and accuracy. It is suitable for various scenarios such as customer service, sales, and data analysis, simplifying data operations and lowering the usage threshold.

This workflow receives files in Parquet, Avro, ORC, or Feather format via Webhook and uses an online API to convert them into JSON format. It automates the processing of complex binary big data files, simplifies data preprocessing, lowers the technical barrier, and is suitable for data analysis, ETL processes, and development teams, enhancing data utilization efficiency. Users can easily upload files and quickly obtain the parsed JSON data, supporting various application scenarios and facilitating data-driven decision-making and system integration.

This workflow automates the processing of user research data by importing survey responses from Google Sheets, generating text vectors using OpenAI, and storing them in the Qdrant database. It identifies major groups through the K-means clustering algorithm and utilizes large language models to perform intelligent summarization and sentiment analysis on the group responses. Finally, the insights are automatically exported back to Google Sheets, creating a structured research report. This process enhances analysis efficiency and helps decision-makers quickly gain deep insights.

This workflow is manually triggered to automatically extract all order data with a "Completed" status from the Unleashed Software system, helping users efficiently filter and centrally manage order information. It is suitable for finance, sales, or operations teams, effectively reducing the time spent on manual queries, improving the accuracy and efficiency of order management, and facilitating subsequent data analysis and report generation.

The main function of this workflow is to automate the extraction of content from specified web pages. Users only need to provide the webpage URL, and the system will use the FireCrawl API to retrieve the webpage data and convert it into Markdown format for return. This process lowers the technical barrier and enhances extraction efficiency, making it suitable for various scenarios such as AI intelligent agents, office automation, data collection, and content monitoring, thereby facilitating quick integration of web scraping functionality for both developers and non-technical users.

This workflow automates the collection of customer reviews from Trustpilot and utilizes AI technology to extract key information from the reviews and perform sentiment analysis. By structuring the review data and analyzing sentiment trends, businesses can quickly gain insights into customer feedback, monitor brand reputation, and simultaneously update the results in real-time to Google Sheets. This enhances the efficiency of data collection and analysis, supporting market research, customer service improvement, and decision-making.

This workflow enables real-time monitoring of new or updated data in Google Sheets. When relevant rows are updated, the system automatically extracts key fields such as "Security Code," "Price," and "Quantity," and converts them into a neatly formatted ASCII table, which is then sent to a designated channel via Discord's Webhook. This process significantly enhances the timeliness and accuracy of data synchronization, making it suitable for teams that require quick sharing and collaboration, especially in the fields of finance and project management.