This workflow automates the export and organization of notes from Google Keep. By automatically filtering and parsing content, it intelligently extracts important information and stores it in a structured format in Google Sheets. Utilizing AI technology, it efficiently identifies key information in the notes, such as amounts, significantly enhancing the accuracy and efficiency of data analysis. It is suitable for both individual and team note management, particularly in the fields of financial analysis and decision support, helping users save time and optimize information utilization.

This workflow automatically calls the Shopify API to retrieve the previous day's orders and customer UTM parameters daily, synchronizing the structured data to the Baserow database. This process not only addresses the cumbersome issue of manually organizing data but also achieves seamless integration of order and marketing data, helping e-commerce operators to analyze advertising effectiveness in depth, optimize marketing strategies, and enhance decision-making efficiency. It is suitable for e-commerce teams, marketing personnel, and data analysts.

The List Builder workflow helps users efficiently create detailed lists of specific groups through automated web searches and data extraction. It can scrape relevant web pages from Google search results, extract information about target individuals, deduplicate and organize the data, and finally import the cleaned data into Google Sheets. This workflow addresses the tediousness of manual searches and information organization, improving the efficiency and accuracy of list building, and is suitable for various scenarios such as marketing, recruitment, community management, and data analysis.

This workflow implements the bulk import of agricultural crop image datasets into the Qdrant vector database, covering data preprocessing, image vector generation, and efficient uploading. By automatically creating collections, generating unique UUIDs, and calling the multimodal embedding API, it ensures that the data structure is standardized and the upload process is efficient, supporting subsequent similarity searches and anomaly detection. It is suitable for data preparation in the agricultural field and machine learning applications, optimizing the process of managing large-scale image data.

This workflow triggers automatic searches and subtitle extraction for YouTube videos through the MCP server. It utilizes Apify's services to bypass official restrictions, ensuring efficient and stable data collection. It supports video searching, subtitle downloading, and usage reporting, simplifying data processing for subsequent analysis and presentation. Additionally, the built-in quota monitoring feature provides real-time feedback on usage, helping users manage resources effectively. This workflow is suitable for various scenarios, including researchers, content creators, and data engineers.

This automated workflow utilizes the Google Custom Search API to obtain street view photos, then employs AWS Rekognition for content label recognition. The image names, links, and recognized labels are organized and saved to Google Sheets. It effectively addresses the inefficiencies and errors associated with traditional manual classification, automating the processes of image acquisition, intelligent analysis, and structured storage. This enhances information management efficiency and is applicable in various fields such as media, advertising, and e-commerce, helping users save time and costs.

This workflow can automatically extract subtitle text from YouTube videos, clean it up, and optimize the formatting to generate a readable transcript. By calling a third-party API, users only need to input the video link to quickly obtain the organized subtitles, eliminating tedious manual operations. It is suitable for content creators, educational institutions, and market analysts, enhancing the efficiency and accuracy of video transcription and greatly simplifying the content processing workflow.

This workflow provides users with a convenient real-time weather inquiry service through a Telegram bot, supporting weather information retrieval for multiple European capitals. Users can receive text and professional visualized weather data with simple chat commands. The bot intelligently recognizes commands, offers friendly prompts for invalid inputs, and provides timely feedback in case of errors, enhancing the interactive experience. Whether for personal inquiries, travel planning, or business reminders, this tool effectively meets various needs.

This workflow automatically fetches user information by calling a random user API and implements multi-format data conversion and storage. It appends user data in real-time to Google Sheets, generates CSV files, and converts them to JSON format, which is then sent via email. This process enhances the efficiency of data collection and sharing, reduces the risk of manual operations, and is suitable for scenarios such as market research, data processing, and team collaboration, significantly improving work efficiency.

This workflow automates the collection and storage of trajectory data from the International Space Station. It periodically calls an API to obtain real-time information on latitude, longitude, and timestamps, efficiently storing this data in a TimescaleDB database to ensure its timeliness and accuracy. This solution addresses the inefficiencies of manual recording and is suitable for various scenarios such as aerospace research, educational demonstrations, and data analysis, providing reliable time-series data support for relevant personnel and enhancing the value of data applications.

This workflow can automatically extract key information from receipt images, such as the merchant, amount, and date. It retrieves receipt images through HTTP requests and calls an intelligent document recognition API to achieve accurate recognition and parsing, significantly improving the efficiency and accuracy of manual data entry. It is suitable for scenarios such as financial reimbursement, expense management, and digital archiving of receipts, helping users quickly obtain structured information, reduce errors, and enhance data management and analysis capabilities.

This workflow implements an automated ETL data pipeline that regularly scrapes tweets on specific topics from Twitter, performs sentiment analysis, and stores the data in MongoDB and Postgres databases. The analysis results are filtered and pushed to a Slack channel, allowing the team to receive important information in real time. This process effectively avoids the tedious task of manually monitoring social media, improves data processing efficiency, and supports quick responses to market dynamics and brand reputation management.

This workflow automatically retrieves the latest product information published on the Product Hunt platform every day, including the name, tagline, description, and official website link. It intelligently handles redirects and unnecessary parameters in the official website links to ensure data accuracy and conciseness. Ultimately, the organized product details are appended or updated in a designated Google Sheets document, making it convenient for users to manage and analyze the information, thereby enhancing the efficiency of information acquisition. It is suitable for entrepreneurs, investors, and content creators who need to track the latest product trends.

This workflow focuses on the formatting and validation of US phone numbers. It can automatically clean non-numeric characters, verify the length of the number and the validity of the country code, and output in various standard formats, such as E.164 format and international dialing format. Its core features include support for handling numbers with extensions and automatic clearing of invalid numbers, ensuring that the input and output phone numbers are consistent and standardized. It is suitable for scenarios such as CRM systems, marketing platforms, and customer service systems, enhancing data quality and the level of automation in business processes.

This workflow is designed to automatically listen for completed Stripe payment events, capturing and synchronizing customer payment order details in real-time, including customer information and purchased product content. Through this automated process, key order data can be efficiently obtained, enhancing the accuracy of data processing while reducing manual intervention and delays. It is suitable for e-commerce platforms, SaaS products, and order management systems, helping relevant teams save time and improve response speed.

This workflow achieves fully automated processing from automatically capturing images from the web to text content recognition and result storage. Utilizing a powerful image text detection service, it accurately extracts text from images, and after formatting, automatically saves the recognition results to Google Sheets for easy management and analysis. This process significantly enhances the efficiency and accuracy of image text processing, making it suitable for businesses and individuals that need to handle large volumes of image text information. It is widely used in fields such as market research and customer service operations.

This workflow is designed to automate the collection and analysis of website traffic data. It retrieves key traffic metrics by calling the Umami tool and uses artificial intelligence to generate easily readable SEO optimization suggestions. The final analysis results are saved to the Baserow database. This process supports scheduled triggers and manual testing, helping website administrators, SEO experts, and data analysts efficiently gain data insights, reduce manual workload, and enhance decision-making efficiency. It is suitable for users looking to achieve intelligent data processing.

This workflow is an efficient tool for detecting anomalies in agricultural crops, capable of automatically identifying whether crop images are abnormal or unknown. Users only need to provide the URL of the crop image, and the system converts the image into a vector using multimodal embedding technology, comparing it with predefined crop category centers to determine the image category. This tool is suitable for scenarios such as agricultural monitoring, research data cleaning, and quality control, significantly improving the efficiency and accuracy of crop monitoring.

This workflow can automatically read and convert data from local JSON files, and then append it to a specified Google Sheets spreadsheet. Through secure OAuth2 authentication, it ensures the safety of data operations, greatly simplifying the data import process, avoiding cumbersome manual tasks, and enhancing the efficiency and accuracy of data processing. It is suitable for businesses and individuals who need to regularly organize and analyze data, helping to achieve efficient data management and decision-making.

This workflow automates the crawling of social media links from specified company websites and outputs the data in a standardized JSON format. By integrating text and URL scraping tools, along with the OpenAI GPT-4 model, it ensures the accuracy and completeness of the data. It supports multi-page crawling and deduplication features, significantly enhancing the efficiency of data collection and addressing the complexities and information fragmentation issues of traditional manual collection processes. This workflow is suitable for professionals in fields such as marketing, data analysis, and recruitment.

The main function of this workflow is to automatically convert customer data from the Squarespace platform into a Shopify-compatible data format and update it in real-time to Google Sheets. It receives data through Webhooks, supports batch processing and manual triggering, ensuring data integrity and timeliness. This effectively reduces errors caused by manual operations and improves the efficiency of e-commerce businesses in managing customer information and marketing activities, making it suitable for users who need cross-platform data integration.

This workflow receives Webhook events from external systems and sends the event information to PostHog in real-time for user behavior analysis. It supports dynamic parsing of event names, ensuring flexibility and accuracy of the data. This process effectively addresses the complexities and data loss issues in cross-system event data transmission, making it suitable for scenarios that require real-time monitoring of user behavior. It helps teams achieve automated data collection and integration, quickly obtain behavioral insights, and promote data-driven decision-making and product optimization.

This workflow combines visual AI intelligent agents, web scraping services, and multimodal large language models to achieve efficient structured data extraction from web content. By using webpage screenshots and HTML scraping, it automatically extracts information such as product titles and prices, formatting the data into JSON for easier subsequent processing and storage. It integrates with Google Sheets, supporting automatic reading and writing of data, making it suitable for e-commerce product information collection, market research, and complex web data extraction, providing users with accurate and comprehensive data acquisition solutions.

This workflow receives external requests in real-time through a Webhook interface and reads data from specified tables in Google Sheets to quickly return query results. It simplifies the traditional data query process, ensuring instant access to data and automated responses, thereby enhancing efficiency and convenience. It is suitable for scenarios that require quick data retrieval, such as customer service systems, internal data integration, and the development of custom API interfaces.