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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add anthropics/skills --skill xlsx -a cursor -y
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
| 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 a model (formulas and values) | two load_workbook passes — see gotchas |
openpyxl,pandas, andmarkitdownare preinstalled — do not runpip installfirst; write the script and import directly. Only if an import fails (or themarkitdowncommand is missing):pip installthe missing package.
Script paths below are relative to this skill's directory.
recalc.py reports errors_found. If you think an error predates you, prove it: load the original with data_only=True and look at that cell. An error you introduced looks exactly like one you inherited.sheet['B10'] = '=SUM(B2:B9)', not the Python-computed total. The sheet must recalculate when its inputs change.Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]); when the number came from the user, say so plainly.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$2 — the [1] is an index
into the workbook's external-reference list, naming a separate file on disk, not a sheet.
That file is rarely present here, so the cell's cached value is the only thing holding its
data. openpyxl strips that value on save; LibreOffice then has to resolve the reference for
real, fails, writes #NAME?, and deletes every link. recalc.py refuses to run in that state
— copy those cells' values out of the original before you save over them (--force overrides,
and accepts the loss).
LibreOffice implements fewer functions than Excel, and one it cannot evaluate becomes a
literal #NAME? baked into the file you deliver.
SUMIFS, INDEX, MATCH, IFERROR, SUMPRODUCT — which need no prefix._xlfn. prefix, because openpyxl writes your formula into the XML verbatim and Excel stores post-2007 names prefixed (its UI hides the prefix): _xlfn.TEXTJOIN, _xlfn.CONCAT, _xlfn.IFS, _xlfn.SWITCH, _xlfn.MAXIFS, _xlfn.MINIFS. Written bare, each yields #NAME?.XLOOKUP, XMATCH, SORT, FILTER, UNIQUE, or SEQUENCE. The runtime's LibreOffice cannot evaluate them under any prefix. Newer builds do evaluate them, but they are spilling array functions and an openpyxl-written file has no spill metadata, so only the top-left cell of the range gets a value — and recalc.py reports total_errors: 0 on the truncated result. Use INDEX/MATCH for lookups, and sort, filter, and de-duplicate in Python before writing the cells.#NAME?.data_only=True yields cached values with the formulas gone; the default yields formula strings with no values. One pass cannot give you both.data_only=True is destructive if you save. That workbook has no formulas left, so saving replaces every one with a literal — permanently.data_only=True on a file openpyxl just wrote returns None everywhere — run recalc.py first. (A formula whose result is "" also reads back as None.)MergedCell whose .value is read-only..xlsm loses its macros unless you pass keep_vba=True to load_workbook.='Assumptions Inputs'!$B$5. Unquoted, it evaluates to #VALUE!.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.
openpyxl, pandas, markitdown (pip, preinstalled — install only if an import fails or the command is missing) · LibreOffice (soffice, auto-configured for sandboxed environments via scripts/office/soffice.py)