mcpbeat

Clean Data Xls

anthropics/clean-data-xls

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis. Triggers on "clean this data", "clean up this sheet", "normalize this data", "fix formatting", "dedupe", "standardize this column", "this data is messy".

715 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
33987
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/anthropics/financial-services --skill clean-data-xls

The instruction itself

7 sections, as written by the author

Clean Data

Clean messy data in the active sheet or a specified range.

Environment

  • If running inside Excel (Office Add-in / Office JS): Use Office JS directly (Excel.run(async (context) => {...})). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]]. The in-place vs helper-column decision still applies.
  • If operating on a standalone .xlsx file: Use Python/openpyxl.

Workflow

Step 1: Scope

  • If a range is given (e.g. A1:F200), use it
  • Otherwise use the full used range of the active sheet
  • Profile each column: detect its dominant type (text / number / date) and identify outliers

Step 2: Detect issues

| Issue | What to look for |

|---|---|

| Whitespace | leading/trailing spaces, double spaces |

| Casing | inconsistent casing in categorical columns (usa / USA / Usa) |

| Number-as-text | numeric values stored as text; stray $, ,, % in number cells |

| Dates | mixed formats in the same column (3/8/26, 2026-03-08, March 8 2026) |

| Duplicates | exact-duplicate rows and near-duplicates (case/whitespace differences) |

| Blanks | empty cells in otherwise-populated columns |

| Mixed types | a column that's 98% numbers but has 3 text entries |

| Encoding | mojibake (é, ’), non-printing characters |

| Errors | #REF!, #N/A, #VALUE!, #DIV/0! |

Step 3: Propose fixes

Show a summary table before changing anything:

| Column | Issue | Count | Proposed Fix |

|---|---|---|---|

Step 4: Apply

  • Prefer formulas over hardcoded cleaned values — where the cleaned output can be expressed as a formula (e.g. =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2)), write the formula in an adjacent helper column rather than computing the result in Python and overwriting the original. This keeps the transformation transparent and auditable.
  • Only overwrite in place with computed values when the user explicitly asks for it, or when no sensible formula equivalent exists (e.g. encoding/mojibake repair)
  • For destructive operations (removing duplicates, filling blanks, overwriting originals), confirm with the user first
  • After each category of fix (whitespace → casing → number conversion → dates → dedup), show the user a sample of what changed and get confirmation before moving to the next category
  • Report a before/after summary of what changed

How to use it

Copy the folder

Take anthropics/clean-data-xls from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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