LinkedIn Web Scraping with Bright Data MCP Server & Google Gemini

This workflow is designed to obtain real-time latitude, longitude, and timestamp data from the International Space Station and automatically store it in a Google BigQuery database. By using scheduled triggers and API calls, it eliminates the tediousness of manual queries and data entry, ensuring the timeliness and completeness of the data. It is suitable for fields such as aerospace research, educational platforms, and data analysis, facilitating real-time monitoring, analysis, and visualization of the space station's location.

This workflow automates the scraping of company data from the Indeed website, utilizing advanced technology to overcome anti-scraping restrictions. It combines data management and intelligent analysis tools to achieve efficient content extraction and summarization. Users can quickly access recruitment information and updates from target companies, addressing the complexities and inefficiencies of traditional data collection processes. It is applicable in various scenarios such as human resources, market research, and AI development, significantly enhancing data processing efficiency and decision-making capabilities.

This workflow automatically listens for diary reply messages in Telegram, identifies a specific format, and organizes and saves them into a Google Sheets spreadsheet. By automatically capturing and structuring the content of user replies, it addresses the cumbersome issue of manually organizing diaries, improving efficiency and accuracy, and preventing information loss and duplicate entries. It is suitable for both individuals and teams for unified management and backup.

This workflow automates the collection and updating of LinkedIn contact information. It is triggered on a schedule to read personal profile URLs from Google Sheets, utilizes the Prospeo.io API to query detailed information (such as name, email, position, etc.), and writes the data back to Google Sheets. This process effectively addresses the tediousness of manually searching for contact information, enhances data completeness and accuracy, and simplifies data maintenance. It is suitable for scenarios where sales, business development, and recruitment teams need to quickly obtain contact information.

This workflow automatically retrieves monthly time tracking reports from Clockify and backs up the data to a GitHub repository. It supports data backups for the last three months and can intelligently update existing files or create new ones, ensuring the integrity and accuracy of the data. By performing regular backups, it mitigates the risk of time tracking data being lost due to online changes, making it suitable for individuals and teams that prioritize data security and version control, thereby enhancing management efficiency and reliability.

This workflow provides personalized drinking reminders through scheduled alerts and intelligent message interactions, helping users develop good hydration habits. Users can quickly log their water intake via Slack, with the data automatically synced to Google Sheets for centralized management and analysis. By incorporating health content generated by OpenAI, the reminders are enhanced in professionalism and encouragement. Additionally, data linkage with health applications is achieved through iOS shortcuts, optimizing the user's health management experience.

This workflow automatically reads YouTube video links from Google Sheets, captures comment data in real-time, and uses an AI model to perform sentiment analysis on the comments, classifying them as positive, neutral, or negative. The analysis results are updated back to Google Sheets, ensuring consistency and timeliness in data management. By supporting pagination for comment retrieval and allowing flexible update frequencies, it greatly enhances the ability of content creators and brand teams to gain insights into audience feedback, helping to optimize content strategies and market responses.

This workflow automatically renames specified key names in a set of initial data through a manual trigger function, helping users quickly achieve data field conversion and standardization. It is suitable for use in scenarios such as development debugging and data preprocessing, effectively addressing the issue of inconsistent field naming. This reduces the tediousness of manual modifications, enhances the efficiency and accuracy of data organization, and facilitates the use of subsequent processes.