Excel / XLSX

Create, inspect, and edit Microsoft Excel workbooks and XLSX files with reliable formulas, dates, types, formatting, recalculation, and template preservation...

Security Scan

The skill is an instruction-only Excel/XLSX guidance pack whose claimed purpose (editing and preserving workbooks) matches the instructions and it asks for no extra credentials or installs — nothing appears disproportionate.

Purpose & Capability

The name/description and the SKILL.md consistently focus on spreadsheet work (formulas, dates, formatting, workbook preservation). The instructions reference using pandas/openpyxl for appropriate tasks — these are expected for an Excel-oriented skill and there are no unrelated env vars, binaries, or config paths requested.

Instruction Scope

SKILL.md provides detailed runtime guidance (when to use pandas vs openpyxl, handling dates, preserving templates, recalculation, streaming large files). It does assume access to user spreadsheet files (read/write) and common Python libraries but does not instruct the agent to access unrelated system files or secrets. Minor note: the skill names specific libraries but does not declare dependencies or installation steps.

Install Mechanism

No install spec and no code files (instruction-only). This is the lowest-risk install profile — nothing will be downloaded or written by an installer.

Credentials

requires.env / primary credential are empty and no secrets or config paths are requested. The set of required permissions (file read/write on spreadsheets you provide) is proportionate to the stated purpose.

Persistence & Privilege

always is false and the skill does not request persistent system modification or access to other skills. The skill can be invoked autonomously (platform default), which is normal and not by itself suspicious.

Assessment

This skill is internally consistent and appears low-risk: it provides best-practice instructions for editing Excel files and asks for nothing extra. Before installing, confirm your agent environment has the spreadsheet libraries you expect (openpyxl, pandas, and any readers/writers you need) from trusted package sources — the SKILL.md references these but does not install them. Be aware the skill will need permission to read/write any spreadsheets you hand it (expected for this purpose). Exercise normal caution with .xlsm (macros) and any spreadsheets that contain sensitive data; avoid sending highly confidential files unless you trust the runtime and its storage/transport. If you need stronger assurance, request a version that declares exact runtime dependencies or supplies code that you can review.

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

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