XLSX Official
Spreadsheet toolkit.
Install
npx skills add XiaomiMiMo/MiMo-Code --skill xlsx-official -g -a claude-code -y- Repository
- XiaomiMiMo/MiMo-Code13.7k
- Category
- Documents
- Updated
- 3d ago
- License
- MIT
- Context cost
- Mediumabout 2,706 tokens
- Runs scripts
- Yes, review before use
What it does
Spreadsheet toolkit. Reach for it whenever the artifact on either side of the conversation is a workbook file, .xlsx, .xlsm, .xltx, .csv, .tsv, and the user wants that artifact produced, changed, cleaned, or read. Typical triggers: 'build me a model', 'update this sheet', 'add a column', 'compute the totals as formulas', 'sanity-check this xlsx', 'export sheet 2 to CSV', 'render the workbook as PDF', 'the spreadsheet in ~/Downloads is a mess, fix it'. Applies equally to financial models, ops reports, data cleanups, and template fills. Skip it when the workbook is only source material and the real output is a Word doc, an HTML page, a Python script that runs standalone, a Google Sheets integration, or an ingestion pipeline into a database, in those cases the spreadsheet is a means, not the deliverable.
XLSX Skill
An Apache-2.0 toolkit for producing, editing, and reading Microsoft Excel
(.xlsx) files. Written from scratch against the public
ECMA-376 / ISO/IEC 29500 (SpreadsheetML)
specification and built on permissively-licensed tooling
(openpyxl MIT, pandas BSD-3-Clause, lxml BSD-3-Clause,
optional xlsxwriter BSD-2-Clause, optional external binary soffice
MPL 2.0) so it can be reused in commercial projects without restriction.
Decision matrix
| Situation | Path | Read first |
|---|---|---|
| Build a workbook from a prompt / dataframe / raw values | Author with openpyxl (formulas + formatting) or pandas (bulk data) | create.md |
Edit an existing .xlsx, add rows, patch cells, refresh formulas | Load with openpyxl, preserve formulas & styles | edit.md |
| Only need to read the data out (analysis, ETL, quick QA) | pandas.read_excel + openpyxl for structural inspection | read.md |
| Clean, aggregate, or transform tabular data before writing back | pandas pipeline, then hand back to openpyxl for final polish | analyze.md |
| Deep structural edits (custom XML parts, defined names, VBA-free surgery) | Unpack → edit XML → repack | edit.md → Raw XML workflow |
| Recompute formula values before shipping | scripts/bake.py via LibreOffice | see QA below |
If the task mixes several of these, do them in this order: read → plan → edit/create → recalc → validate.
