Spreadsheet Skill (Create, Edit, Analyze, Visualize)

When to use

• Build new workbooks with formulas, formatting, and structured layouts.
• Read or analyze tabular data (filter, aggregate, pivot, compute metrics).
• Modify existing workbooks without breaking formulas or references.
• Visualize data with charts/tables and sensible formatting.

Workflow

1. Confirm the file type and goals (create, edit, analyze, visualize).
2. Use

   openpyxl

   for

   .xlsx

   edits and

   pandas

   for analysis and CSV/TSV workflows.
3. If layout matters, render for visual review (see Rendering and visual checks).
4. Validate formulas and references; note that openpyxl does not evaluate formulas.
5. Save outputs and clean up intermediate files.

Temp and output conventions

• Use

  tmp/spreadsheets/

  for intermediate files; delete when done.
• Write final artifacts under

  output/spreadsheet/

  when working in this repo.
• Keep filenames stable and descriptive.

Primary tooling

• Use

  openpyxl

  for creating/editing

  .xlsx

  files and preserving formatting.
• Use

  pandas

  for analysis and CSV/TSV workflows, then write results back to

  .xlsx

  or

  .csv

  .
• If you need charts, prefer

  openpyxl.chart

  for native Excel charts.

Rendering and visual checks

• If LibreOffice (

  soffice

  ) and Poppler (

  pdftoppm

  ) are available, render sheets for visual review:

  • soffice --headless --convert-to pdf --outdir $OUTDIR $INPUT_XLSX

  • pdftoppm -png $OUTDIR/$BASENAME.pdf $OUTDIR/$BASENAME

• If rendering tools are unavailable, ask the user to review the output locally for layout accuracy.

Dependencies (install if missing)

uv

uv pip install openpyxl pandas

uv

python3 -m pip install openpyxl pandas

uv pip install matplotlib

uv

python3 -m pip install matplotlib

# macOS (Homebrew)
brew install libreoffice poppler

# Ubuntu/Debian
sudo apt-get install -y libreoffice poppler-utils

Environment

Examples

• Runnable Codex examples (openpyxl):

  references/examples/openpyxl/

Formula requirements

• Use formulas for derived values rather than hardcoding results.
• Keep formulas simple and legible; use helper cells for complex logic.
• Avoid volatile functions like INDIRECT and OFFSET unless required.
• Prefer cell references over magic numbers (e.g.,

  =H6*(1+$B$3)

  not

  =H6*1.04

  ).
• Guard against errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?) with validation and checks.
• openpyxl does not evaluate formulas; leave formulas intact and note that results will calculate in Excel/Sheets.

Citation requirements

• Cite sources inside the spreadsheet using plain text URLs.
• For financial models, cite sources of inputs in cell comments.
• For tabular data sourced from the web, include a Source column with URLs.

Formatting requirements (existing formatted spreadsheets)

• Render and inspect a provided spreadsheet before modifying it when possible.
• Preserve existing formatting and style exactly.
• Match styles for any newly filled cells that were previously blank.

Formatting requirements (new or unstyled spreadsheets)

• Use appropriate number and date formats (dates as dates, currency with symbols, percentages with sensible precision).
• Use a clean visual layout: headers distinct from data, consistent spacing, and readable column widths.
• Avoid borders around every cell; use whitespace and selective borders to structure sections.
• Ensure text does not spill into adjacent cells.

Color conventions (if no style guidance)

• Blue: user input
• Black: formulas/derived values
• Green: linked/imported values
• Gray: static constants
• Orange: review/caution
• Light red: error/flag
• Purple: control/logic
• Teal: visualization anchors (key KPIs or chart drivers)

Finance-specific requirements

• Format zeros as "-".
• Negative numbers should be red and in parentheses.
• Always specify units in headers (e.g., "Revenue ($mm)").
• Cite sources for all raw inputs in cell comments.

Investment banking layouts

• Totals should sum the range directly above.
• Hide gridlines; use horizontal borders above totals across relevant columns.
• Section headers should be merged cells with dark fill and white text.
• Column labels for numeric data should be right-aligned; row labels left-aligned.
• Indent submetrics under their parent line items.