Skill: shenjialin-cn/testskill1111 (version 1.0.2)

--- name: Excel / XLSX slug: excel-xlsx version: 1.0.2 homepage: https://clawic.com/skills/excel-xlsx description: "Create, inspect, and edit Microsoft Excel workbooks and XLSX files with reliable formulas, dates, types, formatting, recalculation, and template preservation. Use when (1) the task is about Excel, `.xlsx`, `.xlsm`, `.xls`, `.csv`, or `.tsv`; (2) formulas, formatting, workbook structure, or compatibility matter; (3) the file must stay reliable after edits." changelog: Tightened formula anchoring, recalculation, and model traceability after a stricter external spreadsheet audit. metadata: {"clawdbot":{"emoji":"📗","requires":{"bins":[]},"os":["linux","darwin","win32"]}} --- ## When to Use Use when the main artifact is a Microsoft Excel workbook or spreadsheet file, especially when formulas, dates, formatting, merged cells, workbook structure, or cross-platform behavior matter. ## Core Rules ### 1. Choose the workflow by job, not by habit - Use `pandas` for analysis, reshaping, and CSV-like tasks. - Use `openpyxl` when formulas, styles, sheets, comments, merged cells, or workbook preservation matter. - Treat CSV as plain data exchange, not as an Excel feature-complete format. - Reading values, preserving a live workbook, and building a model from scratch are different spreadsheet jobs. ### 2. Dates are serial numbers with legacy quirks - Excel stores dates as serial numbers, not real date objects. - The 1900 date system includes the false leap-day bug, and some workbooks use the 1904 system. - Time is fractional day data, so formatting and conversion both matter. - Date correctness is not enough if the number format still displays the wrong thing to the user. ### 3. Keep calculations in Excel when the workbook should stay live - Write formulas into cells instead of hardcoding derived results from Python. - Use references to assumption cells instead of magic numbers inside formulas. - Cached formula values can be stale, so do not trust them blindly after edits. - Check copied formulas for wrong ranges, wrong sheets, and silent off-by-one drift before delivery. - Absolute and relative references are part of the logic, so copied formulas can be wrong even when they still "work". - Test new formulas on a few representative cells before filling them across a whole block. - Verify denominators, named ranges, and precedent cells before shipping formulas that depend on them. - A workbook should ship with zero formula errors, not with known `#REF!`, `#DIV/0!`, `#VALUE!`, `#NAME?`, or circular-reference fallout left for the user to fix. - For model-style work, document non-obvious hardcodes, assumptions, or source inputs in comments or nearby notes. ### 4. Protect data types before Excel mangles them - Long identifiers, phone numbers, ZIP codes, and leading-zero values should usually be stored as text. - Excel silently truncates numeric precision past 15 digits. - Mixed text-number columns need explicit handling on read and on write. - Scientific notation, auto-parsed dates, and stripped leading zeros are common corruption, not cosmetic issues. ### 5. Preserve workbook structure before changing content - Existing templates override generic styling advice. - Only the top-left cell of a merged range stores the value. - Hidden rows, hidden columns, named ranges, and external references can still affect formulas and outputs. - Shared strings, defined names, and sheet-level conventions can matter even when the visible cells look simple. - Match styles for newly filled cells instead of quietly introducing a new visual system. - If the workbook is a template, preserve sheet order, widths, freezes, filters, print settings, validations, and visual conventions unless the task explicitly changes them. - Conditional formatting, filters, print areas, and data validation often carry business meaning even when users only mention the numbers. - If there is no existing style guide and the file is a model, keep editable inputs visually distinguishable from formulas, but never override an established template to force a generic house style. ### 6. Recalculate and review before delivery - Formula strings alone are not enough if the recipient needs current values. - `openpyxl` preserves formulas but does not calculate them. - Verify no `#REF!`, `#DIV/0!`, `#VALUE!`, `#NAME?`, or circular-reference fallout remains. - If layout matters, render or visually review the workbook before calling it finished. - Be careful with read modes: opening a workbook for values only and then saving can flatten formulas into static values. - If assumptions or hardcoded overrides must stay, make them obvious enough that the next editor can audit the workbook. ### 7. Scale the workflow to the file size - Large workbooks can fail for boring reasons: memory spikes, padded empty rows, and slow full-sheet reads. - Use streaming or chunked reads when the file is big enough that loading everything at once becomes fragile. - Large-file workflows also need narrower reads, explicit dtypes, and sheet targeting to avoid accidental damage. ## Common Traps - Type inference on read can leave numbers as text or convert IDs into damaged numeric values. - Column indexing varies across tools, so off-by-one mistakes are common in generated formulas. - Newlines in cells need wrapping to display correctly. - External references break easily when source files move. - Password protection in old Excel workflows is not serious security. - `.xlsm` can contain macros, and `.xls` remains a tighter legacy format. - Large files may need streaming reads or more careful memory handling. - Google Sheets and LibreOffice can reinterpret dates, formulas, or styling differently from Excel. - Dynamic array or newer Excel functions like `FILTER`, `XLOOKUP`, `SORT`, or `SEQUENCE` may fail or degrade in older viewers. - A workbook can look fine while still carrying stale cached values from a prior recalculation. - Saving the wrong workbook view can replace formulas with cached values and quietly destroy a live model. - Copying formulas without checking relative references can push one bad range across an entire block. - Hidden sheets, named ranges, validations, and merged areas often keep business logic that is invisible in a quick skim. - A workbook can appear numerically correct while still failing because filters, conditional formats, print settings, or data validation were stripped. - A workbook can be numerically correct and still fail visually because wrapped text, clipped labels, or narrow columns were never reviewed. ## Related Skills Install with `clawhub install <slug>` if user confirms: - `csv` — Plain-text tabular import and export workflows. - `data` — General data handling patterns before spreadsheet output. - `data-analysis` — Higher-level analysis that can feed workbook deliverables. ## Feedback - If useful: `clawhub star excel-xlsx` - Stay updated: `clawhub sync`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)