What problem does it solve? Working with spreadsheet files programmatically often leads to broken formulas, hardcoded values that cannot update, and Excel errors like #REF! or #DIV/0! that only surface after delivery. This Skill provides a disciplined workflow for creating, editing, and analyzing .xlsx, .xlsm, .csv, and .tsv files so every deliverable arrives with working formulas, professional formatting, and zero formula errors. ## Core Features & Use Cases - Formula-first spreadsheet creation: Builds Excel files where all calculations use native Excel formulas instead of hardcoded Python-computed values, keeping workbooks dynamic and updateable. - Mandatory recalculation and error scanning: Uses scripts/recalc.py with LibreOffice to recalculate all formulas and report every #REF!, #DIV/0!, #VALUE!, #NAME?, and #N/A error with exact cell locations. - Financial modeling conventions: Enforces industry-standard color coding (blue inputs, black formulas, green cross-sheet links), number formatting rules, and documented sources for hardcoded values. - Use Case: A user asks to build a revenue projection model from a messy CSV export. The Skill cleans the data with pandas, constructs the model in openpyxl with assumption cells and formulas, recalculates via LibreOffice, fixes any reported errors, and delivers a polished .xlsx. ## Quick Start Use the xlsx skill to turn the attached sales-export.csv into a formatted Excel workbook with a summary sheet that totals revenue by region using live formulas.