Skill

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".

Install

npx claudepluginhub rodaquino-omni/crowtech-healthcare-finance --plugin financial-analysis

Tool Access

This skill uses the workspace's default tool permissions.

Preview

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

SKILL.md

Similar Skills

kotlin-ktor-patterns

Provides Ktor server patterns for routing DSL, plugins (auth, CORS, serialization), Koin DI, WebSockets, services, and testApplication testing.

everything-claude-code

163.2k

deep-research

Conducts multi-source web research with firecrawl and exa MCPs: searches, scrapes pages, synthesizes cited reports. For deep dives, competitive analysis, tech evaluations, or due diligence.

everything-claude-code

163.2k

inventory-demand-planning

Provides demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation for multi-location retailers managing 300-800 SKUs.

everything-claude-code

163.2k

Stats

Parent Repo Stars0

Parent Repo Forks0

Last CommitApr 21, 2026

Actions

View Source View Plugin View on GitHub View README

clean-data-xls

Clean Data

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

Environment

Office JS: Use Excel.run(async (context) => {...}). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]].

Standalone .xlsx: 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: dominant type (text / number / date) and 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

Duplicates

exact and near-duplicate rows

Blanks

empty cells in otherwise-populated columns

Mixed types

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

Encoding

mojibake, 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 — write =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2) in a helper column rather than overwriting the original.

Only overwrite in place when user requests, or when no formula equivalent exists (encoding repair).

For destructive operations (dedup, fill blanks, overwrite), confirm first.

After each category (whitespace -> casing -> number conversion -> dates -> dedup), show a sample and get confirmation before moving on.

Report a before/after summary.

Clean Data

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

Environment

Office JS: Use Excel.run(async (context) => {...}). Read via range.values, write helper-column formulas via range.formulas = [["=TRIM(A2)"]].
Standalone .xlsx: 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: dominant type (text / number / date) and 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
Duplicates	exact and near-duplicate rows
Blanks	empty cells in otherwise-populated columns
Mixed types	a column that's 98% numbers but has 3 text entries
Encoding	mojibake, 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 — write =TRIM(A2), =VALUE(SUBSTITUTE(B2,"$","")), =UPPER(C2), =DATEVALUE(D2) in a helper column rather than overwriting the original.
Only overwrite in place when user requests, or when no formula equivalent exists (encoding repair).
For destructive operations (dedup, fill blanks, overwrite), confirm first.
After each category (whitespace -> casing -> number conversion -> dates -> dedup), show a sample and get confirmation before moving on.
Report a before/after summary.