What problem does it solve? Working with spreadsheet files programmatically often leads to hardcoded values, broken formulas, and inconsistent formatting. This Skill provides a structured workflow for creating, editing, and analyzing .xlsx, .xlsm, .csv, and .tsv files so the deliverable is a dynamic, error-free spreadsheet rather than a static data dump. ## Core Features & Use Cases - Formula-first spreadsheet generation: Builds Excel files where calculations live in real Excel formulas (SUM, AVERAGE, growth rates) instead of Python-computed hardcoded values, keeping workbooks dynamic and updateable. - Mandatory formula recalculation and error scanning: Uses the bundled recalc.py script with LibreOffice to recalculate all formulas and report #REF!, #DIV/0!, #VALUE!, and other errors 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 assumptions. - Use Case: A user asks to build a revenue projection model from raw sales data. The Skill creates the workbook with openpyxl, writes growth formulas referencing assumption cells, applies currency and percentage formats, then runs recalc.py to verify zero formula errors before delivery. ## Quick Start Use the xlsx skill to create a formatted Excel financial model from my sales data with live formulas and verify it has no formula errors.
