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Found 103 Skills
Auto-detect and fix common Excel formatting issues like merged cells, inconsistent types, duplicate headers, and encoding problems.
Handle messy CSVs with encoding detection, delimiter inference, and malformed row recovery.
Comprehensive Excel spreadsheet creation, editing, and analysis using openpyxl and xlwings supporting formulas, formatting, data analysis, charts, and financial model color coding. Use when asked to "create a spreadsheet", "edit this Excel file", "analyze spreadsheet data", "preserve Excel formulas", "create financial model", or "recalculate formulas". Implements industry-standard color conventions (blue=inputs, black=formulas, green=internal links, red=external links, yellow=key assumptions) and zero formula error requirements. Works with .xlsx, .xlsm, .csv, .tsv files for professional spreadsheet workflows.
Use when the user needs Excel file manipulation — reading, writing, formulas, charts, conditional formatting, data validation, pivot tables, or large file handling. Trigger conditions: create Excel reports programmatically, read spreadsheet data, add formulas or charts, apply conditional formatting, perform data validation, generate pivot tables, handle CSV import/export, process large datasets in Excel format.
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
全面的电子表格创建、编辑与分析工具，支持公式、格式设置、数据分析和可视化。当需要处理电子表格（如 .xlsx、.xlsm、.csv、.tsv 等）时使用，包括：(1) 创建包含公式和格式的新电子表格，(2) 读取或分析数据，(3) 在保留公式的情况下修改现有电子表格，(4) 在电子表格中进行数据分析和可视化，或 (5) 重新计算公式。
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, ....
Implement analytics, data analysis, and visualization best practices using Python, Jupyter, and modern data tools.
Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.
Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.
Polars fast DataFrame library. Use for fast data processing.
Use this skill when spreadsheet files are the primary input or output. This means the user wants to: open, read, edit, or repair existing .xlsx, .xlsm, .csv, or .tsv files (e.g., add columns, calculate formulas, format, create charts, clean messy data); create new spreadsheets from scratch or from other data sources; or convert between spreadsheet file formats. Trigger this especially when the user references a spreadsheet file by name or path—even casually (such as "the xlsx in my downloads")—and wants to process it or generate content from it. It's also used to clean or reorganize messy tabular data files (rows with incorrect formatting, misaligned headers, garbage data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do not trigger this when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.