What problem does it solve? Building polished spreadsheets programmatically requires knowing which Python library fits each task, how to write formulas, charts, and formatting correctly, and how to handle large files without exhausting memory. This Skill provides tested recipes for creating .xlsx files, Google Sheets, and PDF exports so you avoid common pitfalls like unreadable formulas, broken chart references, and RAM blowups. ## Core Features & Use Cases - Spreadsheet Generation: Create .xlsx files with openpyxl or xlsxwriter including formulas, number formats, conditional formatting, data validation, freeze panes, and charts. - Financial Models: Ready-made recipes for 3-statement models, DCF, LBO, comparable company analysis, and M&A accretion/dilution models with input/output cell conventions. - Pivot-Style Summaries & Sensitivity Grids: Build SUMIFS-based pivot equivalents and one-way or two-way data tables with color-scale formatting. - Google Sheets & PDF Export: Create and format Google Sheets via gspread, upload .xlsx to Drive, and convert spreadsheets to PDF with LibreOffice or print-layout settings. - Use Case: Ask for a DCF valuation model for a public company; the Skill gathers real financials via web search, then produces a formatted multi-sheet workbook with assumptions, projections, and a WACC sensitivity grid. ## Quick Start Create an Excel workbook with a formatted sales table, revenue formulas, a bar chart, and a summary sheet from my data.
