Excel Model

Excel Model Skill

A model is only useful if it's live — change an assumption and everything recalculates. A markdown table can't do that; a real .xlsx with cell formulas can. This skill builds an actual Excel workbook by writing and running an openpyxl script: a clean inputs sheet, calculation sheets that reference those inputs with real = formulas, and sensible formatting — so the user gets a file they can drive, not a snapshot.

Environment: this produces a binary file, so it needs a place to run code — Claude Code, the Anthropic API code-execution tool, or Claude.ai (with the analysis/code tool). In the browser playground (no code execution), use the markdown output as the spec instead.

Required Inputs

Ask for these only if they aren't already provided:

• What the model is — financial model, budget, forecast, pricing model, scenario planner, etc.
• The inputs/assumptions — the driver variables (and rough values) the user will change.
• The outputs — what it should compute (revenue, burn, margins, totals, a P&L, etc.).
• Structure — periods (months/years), tiers/segments, and any required layout.

Process

1. Design before coding — lay out the sheets (Inputs · Calculations · Output/Summary), and which cells are inputs vs. formulas. Confirm the calculation logic with the user if non-trivial.
2. Write an openpyxl script that:
   • Puts all driver assumptions on an Inputs sheet (one source of truth), labelled and formatted.
   • Builds calculation cells as real formulas referencing the input cells (e.g. =Inputs!B2*Inputs!B3), never hard-coded results — so the model is live.
   • Adds formatting: headers, number/currency/percent formats, column widths, and light cell styling for readability.
   • Saves to a clearly named .xlsx.
3. Run it, then state the formulas used and tell the user which cells to change to flex the model.

Output Format

• The generated .xlsx file (the deliverable).
• A short README of the model: the sheets, the input cells to change, the key formulas in plain English, and any assumptions.

Quality Checks

• Calculations are live cell formulas, not pasted static values
• All driver assumptions live on one Inputs sheet and are referenced, not duplicated
• Numbers are formatted (currency/percent/thousands) and sheets are readable
• The script runs cleanly and the file opens in Excel/Sheets/Numbers
• The user is told exactly which cells to change to drive the model

Anti-Patterns

• Do not write computed results as static numbers — the whole point is that inputs recalculate
• Do not hard-code an assumption inside a formula — put it on the Inputs sheet and reference it
• Do not scatter inputs across sheets — one assumptions sheet, single source of truth
• Do not skip formatting — an unformatted grid of numbers is hard to trust or use
• Do not claim a file was produced if there was no code execution — fall back to a clear spec instead

Based On

Financial-modelling best practice (separate inputs from calculations, formula-driven, no hard-codes) implemented with openpyxl.

Programmatic Helper

This skill ships scripts/xlsx_tool.py — a zero-dependency (stdlib zip+XML) tool that produces real .xlsx files, so the model you design can be delivered as a working workbook, not a markdown table:

# Build a workbook from JSON (numbers stay numbers, "=B2*C2" becomes a live formula)
python3 scripts/xlsx_tool.py create model.xlsx --data '{"Model": [["Item","Qty","Price","Total"],["Widget",4,9.5,"=B2*C2"]]}'

# Fill {{placeholders}} in an existing template workbook
python3 scripts/xlsx_tool.py fill template.xlsx out.xlsx --values '{"month":"July","revenue":21000}'

Design the model first (per this skill), then emit the JSON and run create. Honest limits: default styling only, no charts — for formatted finals, open the generated file and style it, or use the playground's Excel export.