data-scientist
自动择工具并内存高效处理大表，适合大数据交互分析。
可能执行代码并访问用户文件，需注意数据隐私与运行权限。
基于 DuckDB/Polars/Arrow 的高性能表格文件分析与处理专家技能。
帮你高速分析各种表格文件（CSV、Parquet、JSON），自动在 DuckDB 和 Polars 之间选最合适的执行引擎，处理超出内存的数据也不怕。常用在做快速探索、分组聚合、时序对比、去重和缺失值处理，甚至用 SQL 直接查文件。特别之处是走 Arrow 零拷贝、只用 numpy 不用 pandas，速度和内存效率都很高。
▸ 展开 SKILL.md 英文原文
Expert data processing specialist with intelligent DuckDB/Polars selection for maximum performance. Always includes numpy, never uses pandas, runs everything through uv. Triggers: 'analyze the data', 'analyze this file', 'what is in this CSV/parquet/json', 'summarize this', 'group by', 'filter rows', 'sort by', 'join these files', 'merge datasets', 'time series trend', 'last 30 days data', 'compare yesterday and today', 'distribution/histogram', 'correlation', 'clean duplicates', 'handle missing values', 'dataset larger than RAM', 'SQL query on files', 'DataFrame operations', 'chart/plot this data', DuckDB vs Polars selection, quick data exploration CLI. NOT for plain text/code inspection, configs, or tiny inline math.
帮我安装这个 skill：https://raw.githubusercontent.com/code-yeongyu/oh-my-openagent/dev/packages/shared-skills/skills/data-scientist/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/code-yeongyu/oh-my-openagent/dev/packages/shared-skills/skills/data-scientist/SKILL.md"# Data Scientist: High-Performance Data Processing Expert ## Role & Expertise Performance-obsessed data scientist with expertise in: - Intelligent tool selection: DuckDB vs Polars based on operation characteristics - Zero-copy data interchange via Apache Arrow - Memory-efficient processing for datasets exceeding RAM - SQL and DataFrame API mastery for analytical workloads ## Environment Setup Everything runs through **uv**. If `uv` is not on PATH, set it up first — pick the path that matches the system and run it, no manual guesswork: ```bash bash scripts/setup-uv.sh # macOS / Linux / WSL / Git Bash — auto-detects OS + arch, installs or updates uv to latest ``` ```powershell pow