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name: xlsx description: Create, read, edit Excel .xlsx workbooks and CSVs. version: 1.1.0 author: Nous Research license: MIT platforms: [linux, macos, windows] metadata: hermes: tags: [excel, spreadsheet, xlsx, csv, openpyxl, productivity] category: productivity related_skills: [docx, pdf, powerpoint]

Xlsx Skill

Work with Excel .xlsx workbooks using Python and openpyxl: build styled multi-sheet workbooks with formulas and charts, inspect or dump existing files, edit cells and structure, and convert to/from CSV. All helper scripts are argparse CLIs that print JSON and use explicit UTF-8 I/O.

When to Use

• Creating .xlsx reports: multiple sheets, number formats, styling, merged cells, freeze panes, autofilter, conditional formatting, charts, data-validation dropdowns, native Excel tables, defined names, hyperlinks, cell notes, sheet protection.
• Reading a workbook: sheet inventory, dumping data as JSON or CSV, listing formulas vs cached values, notes, defined names, tables.
• Editing existing files: set cells, append rows, insert/delete rows/columns (reference-aware via xlsx_restructure.py), copy/rename sheets, tables, names, notes, protection.
• Recalculating formulas headlessly via LibreOffice (xlsx_recalc.py).
• CSV interop with type inference and non-UTF-8 encodings.
• Not for the legacy .xls binary format (use LibreOffice to convert first: soffice --headless --convert-to xlsx old.xls).

Prerequisites

• Python 3.10+ with openpyxl (pip install openpyxl). No other third-party packages are needed; everything else is stdlib.
• Optional: LibreOffice (soffice) for headless recalculation or format conversion.

How to Run

Run the helper scripts with the terminal tool from this skill's scripts/ directory (every script supports --help):

python scripts/xlsx_create.py spec.json report.xlsx   # build from JSON spec
python scripts/xlsx_read.py report.xlsx --sheets      # inventory
python scripts/xlsx_read.py report.xlsx --json --sheet Data
python scripts/xlsx_read.py report.xlsx --formulas
python scripts/xlsx_edit.py report.xlsx --sheet Data --set B2=42 --recalc
python scripts/xlsx_restructure.py report.xlsx --sheet Data --insert-rows 3:2
python scripts/xlsx_recalc.py report.xlsx
python scripts/csv_to_xlsx.py data.csv out.xlsx --encoding utf-8
python scripts/xlsx_to_csv.py report.xlsx out.csv --sheet Data

Author the JSON spec with write_file, inspect script JSON output with read_file or directly from stdout.

Quick Reference

Task	Command
Create workbook from spec	xlsx_create.py spec.json out.xlsx
Sheet names + dimensions	xlsx_read.py f.xlsx --sheets
Dump sheet as JSON	xlsx_read.py f.xlsx --json --sheet S
Dump sheet as CSV	xlsx_read.py f.xlsx --csv --out d.csv
List formulas + cached values	xlsx_read.py f.xlsx --formulas
Set a cell / formula	xlsx_edit.py f.xlsx --set "A1==SUM(B:B)"
Append a row	xlsx_edit.py f.xlsx --append '[1,"x",true]'
Insert 2 rows, refs NOT shifted	xlsx_edit.py f.xlsx --insert-rows 3:2
Insert 2 rows, refs shifted	xlsx_restructure.py f.xlsx --insert-rows 3:2
Delete a column, refs shifted	xlsx_restructure.py f.xlsx --delete-cols B
Create a native table	xlsx_edit.py f.xlsx --add-table Sales:A1:C9
Append inside a table	--table-append 'Sales=["West",5]'
List tables	xlsx_edit.py f.xlsx --list-tables
Defined names	--define-name "Rates='Data'!$B$2:$B$9" / --delete-name Rates / xlsx_read.py f.xlsx --names
Hyperlink	`--hyperlink "A1=https://example.com
Cell note	`--note "B2=Check this
Protect sheet (see Pitfalls)	--protect your-password --unlock B2:B9
Recalculate via LibreOffice	xlsx_recalc.py f.xlsx
Copy / rename sheet	--copy-sheet Src:New --rename-sheet Old:New
Force recalc on open	xlsx_edit.py f.xlsx --recalc
CSV -> styled xlsx	csv_to_xlsx.py in.csv out.xlsx
xlsx -> CSV	xlsx_to_csv.py f.xlsx out.csv --encoding utf-8

Procedure

1. Create: write a JSON spec (schema documented in xlsx_create.py --help and its docstring). Each sheet supports rows (scalars or styled cell objects), sparse cells overrides, column_widths, row_heights, merges, freeze_panes, autofilter, conditional_formats (cell_is rules and color scales), charts (bar/line/pie from cell ranges), validations (list dropdowns), tables (native Excel tables with a style name), and protection. Workbook-level defined_names maps names to refs. Cell objects also take hyperlink and note. Typed values: JSON numbers/bools pass through; dates use {"value": "2026-01-31", "type": "date"}. Number formats are Excel format strings: currency "$#,##0.00", percent "0.0%", date "yyyy-mm-dd".
2. Formulas: set with "formula": "SUM(B2:B9)" in the spec or --set "C1==SUM(A:A)" in the editor. When writing formulas, add "full_calc_on_load": true (spec) or --recalc (editor); this sets the workbook's fullCalcOnLoad flag so Excel/LibreOffice recompute everything on open. openpyxl itself NEVER evaluates formulas.
3. Read: --sheets for inventory (names, dimensions, merged ranges, chart count, tables, protection, defined names), --json/--csv for data, --formulas to pair each formula string with its cached result, --notes for cell comments, --names for defined names. Cached results exist only if the file was last saved by a real spreadsheet app; files fresh from openpyxl return null there. To materialize results headlessly run xlsx_recalc.py file.xlsx (uses LibreOffice; prints {"recalculated": false, ...} and exits 0 when soffice is absent), then reload with --data-only.
4. Edit: xlsx_edit.py applies renames/copies first, then structural row/column changes, then --set/--append. It edits in place unless --out is given — copy the file first if you need the original.
5. Restructure: for insert/delete on sheets that have formulas, merges, tables, or filters, use xlsx_restructure.py instead of xlsx_edit.py. It rewrites formula references on ALL sheets (absolute $ refs, ranges, cross-sheet refs), shifts merges, autofilter, freeze panes, validation and conditional-format ranges, table refs, defined names, and row/column dimensions, then prints a JSON report including a not_shifted list. Rules and limits: references/restructuring.md.
6. CSV interop: csv_to_xlsx.py infers int/float/bool/ISO-date per cell and styles the header row; xlsx_to_csv.py writes ISO dates and blank strings for empty cells. Both default to UTF-8 and accept --encoding (e.g. utf-8-sig for Excel-friendly BOM, cp1252 for legacy Windows exports).

Converting to PDF

LibreOffice converts headlessly (also works for CSV export of a single sheet):

soffice --headless --convert-to pdf report.xlsx --outdir out/
soffice --headless --convert-to csv report.xlsx --outdir out/  # 1st sheet only

Only the first sheet lands in a CSV; for other sheets use xlsx_to_csv.py --sheet NAME. If soffice is missing, install LibreOffice or hand the file to the user unconverted.

Pitfalls

• openpyxl does not calculate. Formula results are available only via load_workbook(path, data_only=True) and only when the file was previously saved by Excel/LibreOffice. Otherwise you get None.
• xlsx_edit.py insert/delete does not shift references (raw openpyxl behavior). Use xlsx_restructure.py, which does — but even it cannot move chart anchors, images, or conditional-format RULE formulas; read its JSON report's not_shifted list and references/restructuring.md.
• Sheet protection is NOT security. --protect sets the standard xlsx sheet-protection hash: it signals "don't edit this" to well-behaved apps and nothing more. Anyone can strip it by editing the zip's XML or unchecking it in LibreOffice. Never rely on it for confidentiality or integrity; it does not encrypt anything.
• data_only=True then save silently discards all formulas (cached values replace them). Never save a workbook loaded that way unless that is the goal.
• Loading strips charts/images: openpyxl does not round-trip charts, so editing a charted workbook and saving drops the charts. Re-add charts after editing, or avoid re-saving charted files.
• CSV locale traps: always pass explicit encodings (the scripts already do) and remember European CSVs often use ; delimiters and decimal commas — use --delimiter ';' and expect strings like "12,5" to stay strings.
• Dates are datetimes: Excel stores dates as serial numbers; openpyxl returns datetime/date objects. Dumps here emit ISO strings.
• Sheet names are capped at 31 chars and reject [ ] : * ? / \.

Verification

• After creating: xlsx_read.py out.xlsx --sheets and confirm sheet names, dimensions, merged ranges, and chart counts match intent.
• Dump data with --json and compare against the source values.
• After edits: re-dump the touched range; if formulas were written, confirm --formulas lists them and that --recalc was applied.
• After xlsx_restructure.py: read its JSON report, then re-run --formulas and --sheets to confirm references and ranges landed where expected.
• For a full visual check, open in LibreOffice: soffice --headless --convert-to pdf out.xlsx and inspect the PDF.