Google Analytics: Weekly Report

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.