Excel Tool by Xiaomolong

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

Security Scan

This is an instruction-only Excel/XLSX helper whose requested capabilities, inputs, and scope align with its stated purpose; nothing in the package asks for unrelated credentials, installs, or external endpoints.

Purpose & Capability

The skill's name and description are about creating and editing Excel files; the SKILL.md contains detailed, appropriate guidance about using pandas/openpyxl, formulas, dates, and workbook preservation. There are no unrelated required binaries, env vars, or config paths.

Instruction Scope

Runtime instructions (the SKILL.md) stay on-topic: they tell the agent how to handle spreadsheets, formulas, types, formatting, and large files. They do not instruct reading unrelated system files, exporting secrets, or contacting external endpoints. The guidance warns about macros and format quirks, which is appropriate for this domain.

Install Mechanism

No install spec and no code files are present, so nothing is written to disk or downloaded. This lowers risk and matches an instruction-only skill.

Credentials

The skill requests no environment variables, credentials, or config paths. That is proportionate for a library-guidance skill that would use local Python libraries (pandas/openpyxl) if available.

Persistence & Privilege

always is false and the skill is user-invocable. It does not request permanent presence or modify other skills' config. Autonomous invocation is allowed by platform default but is not combined with additional risky privileges here.

Assessment

The skill is internally consistent and instruction-only, but exercise normal caution: (1) verify the skill source (homepage and owner) if you require provenance — the registry metadata and _meta.json show slight version/owner id differences that look like minor metadata drift but are worth double-checking; (2) back up any real spreadsheets before automated edits; (3) confirm the agent environment has the expected Python libraries (pandas/openpyxl) since there's no install step; (4) be careful with .xlsm files because they can contain macros — this skill does not add macros but manipulating macro-enabled workbooks can have side effects; and (5) test on non-sensitive example files first.

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