Agent skill · data analytics · 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".
Why this skill is useful
Adds specific Python and Excel workflows for cleaning and normalizing spreadsheet data that the AI wouldn't reliably generate on its own.
What it needs
Requires openpyxl, python installed locally. About 2k tokens when loaded. Last updated 2026-08-04. 34,059 stars on the source repository.
What this skill does
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,"
quot;,"")), =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. …How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/clean-data-xls