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【广发金工】全面赋能日常办公与投研场景实例:OpenClaw多平台部署与投研应用(二)
Core Viewpoint - The article discusses the application of OpenClaw in investment research and office automation, highlighting its capabilities in financial data integration, stock selection, document management, and report generation through various skills and automation processes [1][2][3]. Group 1: OpenClaw Applications - OpenClaw enables multi-platform deployment for investment research, including local Windows, Mac, and cloud servers, facilitating financial data access and analysis [1]. - The system automates the extraction of unstructured data from PDFs, complex data cleaning in Excel, and the generation of structured reports in Word, significantly reducing manual errors and time costs [2][7]. - OpenClaw's intelligent agent framework automates the entire process from data collection to structured output, enhancing the efficiency of investment research [3]. Group 2: Automation of Office Document Processing - The article details how OpenClaw can automate workflows involving docx, pdf, pptx, xlsx, and canvas-design skills, addressing the challenges of cross-software collaboration and data transfer [6][7]. - Each skill serves specific functions: - docx for creating and editing Word documents, enhancing collaboration [8]. - pdf for extracting and processing data from PDF files, reducing manual data entry [8]. - pptx for generating and editing PowerPoint presentations, streamlining report presentations [8]. - xlsx for managing and analyzing Excel spreadsheets, improving data accuracy [8]. - canvas-design for creating high-quality visual designs, supporting various design needs [9]. Group 3: Practical Examples - An example illustrates how OpenClaw can read a PDF financial report, summarize the content, generate visual charts, and compile everything into a Word document, showcasing its end-to-end automation capabilities [26][33]. - The process involves extracting key information from complex documents and transforming it into structured outputs, demonstrating the tool's effectiveness in handling large volumes of data [33].