minimax-xlsx
Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use when the user asks to create, build, modify, analyze, read, validate, or format any Excel spreadsheet, financial model, pivot table, or tabular data file. Covers: creating new xlsx from scratch, reading and analyzing existing files, editing existing xlsx with zero format loss, formula recalculation and validation, and applying professional financial formatting standards. Triggers on 'spreadsheet', 'Excel', '.xlsx', '.csv', 'pivot table', 'financial model', 'formula', or any request to produce
npx skills add vibeeval/vibecosystem --skill minimax-xlsx --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MiniMax XLSX Skill Handle the request directly. Do NOT spawn sub-agents. Always write the output file the user requests. ## Task Routing | Task | Method | Guide | |------|--------|-------| | **READ** — analyze existing data | `xlsx_reader.py` + pandas | `references/read-analyze.md` | | **CREATE** — new xlsx from scratch | XML template | `references/create.md` + `references/format.md` | | **EDIT** — modify existing xlsx | XML unpack→edit→pack | `references/edit.md` (+ `format.md` if styling needed) | | **FIX** — repair broken formulas in existing xlsx | XML unpack→fix `<f>` nodes→pack | `references/fix.md` | | **VALIDATE** — check formulas | `formula_check.py` | `references/validate.md` | ## READ — Analyze data (read `references/read-analyze.md` first) Start with `xlsx_reader.py` for structure discovery, then pandas for custom analysis. Never modify the source file. **Formatting rule**: When the user specifies decimal places (e.g. "2 decimal places"), apply that format to ALL numeric values — use `f'{v:.2f}'` on every number. Never output `12875` when `12875.00` is required. **Aggregation rule**: Always compute sums/means/counts directly from the DataFrame column — e.g. `df['Reven
- Task Routing
- READ — Analyze data (read references/read-analyze.md first)
- CREATE — XML template (read references/create.md + references/format.md)
- EDIT — XML direct-edit (read references/edit.md first)
- FIX — Repair broken formulas (read references/fix.md first)
- VALIDATE — Check formulas (read references/validate.md first)
- Financial Color Standard
- Key Rules
- Utility Scripts
python3 SKILL_DIR/scripts/xlsx_unpack.py input.xlsx /tmp/xlsx_work/ Find the target sheet's XML via xl/workbook.xml → xl/_rels/workbook.xml.rels Then use the Edit tool to add <f> inside the target <c> element: python3 SKILL_DIR/scripts/xlsx_pack.py /tmp/xlsx_work/ output.xlsx python3 SKILL_DIR/scripts/xlsx_add_column.py /tmp/xlsx_work/ --col G \ in the worksheet XML, NOT by using the row number from the prompt. The prompt may say "row 5 (Office Rent)" but Office Rent might actually be at row 4. Always locate the row by its text label first. python3 SKILL_DIR/scripts/xlsx_insert_row.py /tmp/xlsx_work/ --at 5 \ python3 SKILL_DIR/scripts/xlsx_reader.py input.xlsx # structure discovery
What does the minimax-xlsx skill do?
Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use when the user asks to create, build, modify, analyze, read, validate, or format any Excel spreadsheet, financial model, pivot table, or tabular data file. Covers: creating new xlsx from scratch, reading and analyzing existing files, editing existing xlsx with zero format loss, formula recalculation and validation, and applying professional financial formatting standards. Triggers on 'spreadsheet', 'Excel', '.xlsx', '.csv', 'pivot table', 'financial model', 'formula', or any request to produce
How do I install it?
Run `npx skills add vibeeval/vibecosystem --skill minimax-xlsx --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From vibeeval/vibecosystem, a repository with 521 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.