Create, edit, analyze, and validate Excel spreadsheet files with pandas and openpyxl.
npx skills add https://github.com/langbyyi/CyberStrikeAI-SRC --skill xlsx-langbyyi
Please help me install this Agent Skill. Skill: xlsx Source: https://github.com/langbyyi/CyberStrikeAI-SRC/tree/main/skills/xlsx Command: npx skills add https://github.com/langbyyi/CyberStrikeAI-SRC --skill xlsx-langbyyi
Working with Excel and spreadsheet files often requires specialized knowledge of formatting standards, formula syntax, and data cleaning techniques. This Skill provides an AI-powered assistant that handles the full lifecycle of spreadsheet operations, from creating professional workbooks to analyzing messy data and validating complex financial models.
Use the xlsx skill to analyze the attached 'sales_data.xlsx' file and generate a formatted summary report with pivot tables.
High-intent search queries and answers about installing and using this skill.
To automate Excel file creation from messy CSV data, this tool uses pandas to clean misplaced headers and malformed rows, then applies openpyxl to generate a formatted .xlsx workbook with structured data, pivot tables, and charts.
Yes, you can validate Excel formulas and check for errors like #REF! automatically by running mandatory LibreOffice recalculation, which scans the workbook to detect #DIV/0!, #VALUE!, and other formula errors deterministically.
Yes, openpyxl supports applying financial modeling standards to spreadsheets by enforcing color-coded inputs (blue), formulas (black), cross-sheet links (green), and external links (red) alongside proper number formatting and assumption documentation.
The best way to convert between .xlsx, .csv, and .tsv formats is to use pandas for data manipulation and openpyxl for spreadsheet control, enabling seamless conversion between tabular formats for business and administrative workflows.
Yes, you need LibreOffice to recalculate formulas in an xlsx workflow because it provides deterministic formula recalculation and comprehensive error detection that ensures zero formula errors before finalizing the spreadsheet.
Pandas may drop rows when reading a malformed spreadsheet due to inconsistent column counts or misplaced headers, but this tool handles junk data and malformed rows to produce clean, structured spreadsheets without losing valid records.
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