xlsx

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XLSX Skill

Create, read, and edit Excel workbooks — formulas, formatting, charts, data cleaning, and format conversion. Every formula-bearing output must be recalculated and error-free before delivery.

When to Use

Use this skill any time a spreadsheet file is the primary input or output: opening, reading, editing, or fixing an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file; creating a new spreadsheet from scratch or from other data; converting between tabular formats; cleaning messy tabular data into a proper spreadsheet. Trigger whenever the user references a spreadsheet file by name or path — even casually. Do NOT trigger when the deliverable is a Word document (docx skill), HTML report, standalone script, or Google Sheets API integration. For finance-grade modeling conventions (DCF, LBO, three-statement), the optional excel-author skill adds stricter standards on top of this one.

Prerequisites

pip install openpyxl pandas "markitdown[xlsx]"
which soffice || sudo apt install -y libreoffice   # formula recalculation (scripts/recalc.py)

macOS: brew install libreoffice.

Quick Reference

Task Approach
Create or edit with formulas/formatting openpyxl — see gotchas below
Bulk data in or out pandas (read_excel, to_excel)
Quick look at a sheet markitdown file.xlsx## SheetName per sheet; reads .xlsm too. No cell coordinates, so don't plan edits from it. (read_file also auto-extracts .xlsx)
Read a model (formulas and values) two load_workbook passes — see gotchas

Script paths below are relative to this skill's directory.

Requirements for every output

Recalculate (mandatory whenever the file contains formulas)

openpyxl writes formulas as strings with no cached values. Until you recalculate, every formula cell reads back as None to anything reading cached values — pandas, load_workbook(data_only=True), and most previewers.

python scripts/recalc.py output.xlsx [timeout_seconds]   # default 30

LibreOffice computes every formula, the file is rewritten in place, and you get JSON: status (success | errors_found), total_formulas, total_errors, and an error_summary naming up to 100 cells per error type (locations_truncated says how many it withheld — trust total_errors, not the length of the list). Fix what it names and run it again. JSON with an error key instead of a status means nothing was recalculated, and only that case exits non-zero — errors_found exits 0, so never treat a clean exit as a clean workbook.

A green recalc proves your formulas evaluate, not that they are right. An off-by-one range or a reference to the wrong row yields a clean, error-free file with wrong numbers. Write 2–3 formulas first and check they pull the values you expect, before building out a grid.

A workbook that links to another file loses those links if you re-save it with openpyxl and then recalculate. Such a formula reads ='[1]Returns Analysis'!$B$2 — the [1] is an index into the workbook's external-reference list, naming a separate file on disk, not a sheet. That file is rarely present, so the cell's cached value is the only thing holding its data. openpyxl strips that value on save; LibreOffice then has to resolve the reference for real, fails, writes #NAME?, and deletes every link. recalc.py refuses to run in that state — copy those cells' values out of the original before you save over them (--force overrides, and accepts the loss).

Choosing formulas that survive verification

LibreOffice implements fewer functions than Excel, and one it cannot evaluate becomes a literal #NAME? baked into the file you deliver.

openpyxl gotchas

Financial models

Unless the user says otherwise, or the existing file already does something else.

Color: blue text (0,0,255) for hardcoded inputs and scenario levers · black for formulas · green (0,128,0) for links to another sheet · red (255,0,0) for links to another file · yellow fill (255,255,0) for key assumptions and cells the user should fill in.

Numbers: currency $#,##0, with the unit named in the header (Revenue ($mm)) · zeros render as -, including in percentages ($#,##0;($#,##0);-) · negatives in parentheses · percentages 0.0%, stored as fractions (0.15 renders 15.0%; storing 15 renders 1500.0%) · valuation multiples 0.0x · years as text ("2024", never 2,024).

Structure: every assumption in its own labeled cell, referenced by the formulas that use it (=B5*(1+$B$6), never =B5*1.05) · formulas consistent across every projection period, since a lone edited cell mid-row is the commonest silent error · guard denominators that can be zero.

For full investment-banking conventions (balance checks, sensitivity tables, named ranges), install the optional skill: hermes skills install official/finance/excel-author.

Verification

  1. python scripts/recalc.py output.xlsxstatus: success, total_errors: 0.
  2. Spot-check 2–3 computed cells against expected values (load_workbook(data_only=True) after recalc).
  3. markitdown output.xlsx — scan for missing sheets, misplaced headers, leftover placeholders.

docx (Word documents), pdf (PDF work), powerpoint (decks), optional excel-author (finance-grade modeling standards).