Clean Data Xls
anthropics/financial-servicesOfficial
This handles the grunt work of cleaning messy spreadsheets before analysis. It profiles your columns, detects whitespace issues, inconsistent casing, numbers stored as text, mixed date formats, duplicates, and type mismatches, then shows you a summary table of proposed fixes before touching anything. The smart bit is that it writes formulas like TRIM() and VALUE() in helper columns instead of just overwriting your data, so you can see and audit the transformations. It walks through each category of fix and asks for confirmation on destructive operations. Works in Excel via Office JS or on standalone .xlsx files with Python. Good for when you inherit a dataset and need to normalize it without guessing what silent changes got made.
- installs
- 2.1K
- GitHub stars
- 40.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add anthropics/financial-services --skill clean-data-xls --agent claude-codeInstalls into .claude/skills of the current project.
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
Sponsors
Details
- Repository
- github.com/anthropics/financial-services
- Categories
- Debugging, Finance & Trading
- First seen
- Jun 3, 2026
