clean-data-xls Skill
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", Published by anthropics in financial-services.
What is clean-data-xls Skill?
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", Published by anthropics in financial-services. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Writing
- Data analysis use cases
- Writing use cases
Technical details
- Install or run with Copy skill directory
When to use clean-data-xls Skill
- Use it for data analysis.
- Use it for writing.
Built with
Editorial notes
Source
- Creator: anthropics
- Repository: anthropics/financial-services
- Skill file: plugins/vertical-plugins/financial-analysis/skills/clean-data-xls/SKILL.md
What it does
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",
Skill instructions
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-dup
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Frequently asked questions
What is clean-data-xls?
clean-data-xls is a open-source AI agent skill with Copy skill directory. Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns.
Who is clean-data-xls best for?
clean-data-xls is best for reusing agent instructions, scripts, and references, data analysis workflows, writing workflows.
How do I install clean-data-xls?
Install or run clean-data-xls using Copy skill directory. Check clean-data-xls for the latest setup command.
Is clean-data-xls actively maintained?
clean-data-xls may need a closer maintenance check before production use.
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