In daily life, there are many scenarios that people need to tackle data-related tasks, such as filling out forms, analyzing Excel files, and visualize data report. However, the tools available for these tasks often fragment, requiring users to switch between multiple applications and manually orchestrate steps like data processing, querying, and visualization. Moreover, these tools often assume a certain level of technical proficiency, creating barriers for non-technical users. To facilitate tacking daily data task, we present DataClaw, an autonomous data agent that integrates directly into familiar instant messaging (IM) platforms. By simply typing a natural language request in a chat interface, users enable DataClaw to autonomously plan and execute a complete analytical pipeline, delivering insights, charts, and reports directly back into the conversation. Under the hood, DataClaw is powered by a transparent ReAct reasoning engine, a multi-tiered memory system for cross session context preservation, and a pluggable skill architecture for on-the-fly extensibility. In this demonstration, attendees will interact with DataClaw via standard IM platforms to solve real-world data scenarios, experiencing how it serves as a highly capable personal data assistant.

翻译：日常生活中，人们常需处理各类数据相关任务，如填写表格、分析Excel文件及可视化数据报告。然而，现有工具往往碎片化严重，用户需在多个应用间切换并手动编排数据处理、查询与可视化等环节。更关键的是，这些工具通常预设用户具备一定的技术能力，为非技术用户设置了门槛。为简化日常数据任务处理，我们提出了DataClaw——一款直接集成于常见即时通讯平台的自主数据智能体。用户仅需在聊天界面输入自然语言请求，即可让DataClaw自主规划并执行完整分析流程，将洞察结论、图表及报告直接回传至对话中。在技术实现层面，DataClaw由透明化的ReAct推理引擎、支持跨会话上下文保留的多层级记忆系统，以及具备可插拔扩展能力的技能架构共同驱动。本次演示中，与会者将通过标准即时通讯平台与DataClaw交互，解决真实场景中的数据处理问题，体验其作为高能个人数据助手的价值。