Clean Data Xls
- 1k installs
- 33.8k repo stars
- Updated July 22, 2026
- anthropics/financial-services-plugins
clean-data-xls cleans messy spreadsheet data for reliable financial analysis.
About
The clean-data-xls skill sanitizes messy spreadsheet inputs by trimming whitespace, fixing inconsistent casing, converting numbers stored as text, standardizing dates, removing duplicates, and flagging mixed-type columns. It prepares financial and operational datasets before modeling, comps, or audit workflows run on unreliable exports. Agents scope cleanup to selected ranges, sheets, or entire workbooks based on user intent. The skill prevents garbage-in failures in downstream Excel analytics without altering source systems. Trims whitespace and fixes inconsistent text casing. Converts numbers-stored-as-text and standardizes dates. Removes duplicates and flags mixed-type columns. Scoped cleanup for ranges, sheets, or full workbooks. Prepares messy exports for financial modeling workflows. Clean messy spreadsheet data: trim whitespace, fix casing, dates, duplicates, and mixed types.
- Trims whitespace and fixes inconsistent text casing.
- Converts numbers-stored-as-text and standardizes dates.
- Removes duplicates and flags mixed-type columns.
- Scoped cleanup for ranges, sheets, or full workbooks.
- Prepares messy exports for financial modeling workflows.
Clean Data Xls by the numbers
- 1,037 all-time installs (skills.sh)
- +40 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #135 of 1,136 Finance & Trading skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
What clean-data-xls says it does
Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates
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| Installs | 1k |
|---|---|
| repo stars | ★ 33.8k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 22, 2026 |
| Repository | anthropics/financial-services-plugins ↗ |
How do I fix messy Excel data before modeling or comps?
Clean messy spreadsheet data: trim whitespace, fix casing, dates, duplicates, and mixed types.
Who is it for?
Analysts receiving messy CSV or Excel exports for finance workflows.
Skip if: Skip when source data is already validated in a warehouse pipeline.
When should I use this skill?
User cleans messy spreadsheet data, fixes dates, or removes Excel duplicates.
What you get
Cleaned sheet with standardized dates, text, numbers, and duplicate removal.
- Cleaned cell ranges
- Helper columns
- Mixed-type column flags
Files
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 viarange.values, write helper-column formulas viarange.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
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What problems does it fix?
Whitespace, casing, numbers-as-text, dates, duplicates, and mixed types.
Can cleanup be scoped?
Yes; to a selected range, single sheet, or entire workbook.
Why clean before modeling?
Prevents garbage-in failures in comps, audits, and financial models.
Is Clean Data Xls safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.