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csv-clean

Detect and fix CSV data quality issues including subtotal rows, formatted numbers, and mixed date formats.

Updated Jul 7, 2026
Search Tags:#data quality#pandas#etl#csv#data cleaning#unpivot#date normalization
One-click install
npx skills add https://github.com/hivgb1-ai/do-better-workspace-v2 --skill csv-clean-hivgb1-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: csv-clean
Source: https://github.com/hivgb1-ai/do-better-workspace-v2/tree/main/.claude/skills/csv-clean
Command: npx skills add https://github.com/hivgb1-ai/do-better-workspace-v2 --skill csv-clean-hivgb1-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes scripts (resource) components.

What problem does it solve? CSV exports from banks, Excel, and APIs often contain subtotal rows, comma-formatted numbers, inconsistent date formats, and crosstab layouts that break downstream analysis. This Skill analyzes and cleans these issues automatically with pandas. ## Core Features & Use Cases - Quality Analysis: Run an info-only scan that detects subtotal rows, text-formatted numbers, mixed date formats, and crosstab structures, then suggests the right fix options. - Automated Cleaning: Remove subtotal/total rows, strip currency symbols and commas from numbers, normalize dates to YYYY-MM-DD (including Korean date formats), and unpivot crosstab tables into tidy data. - Use Case: You exported a bank statement CSV where amounts look like "1,234,500" and dates mix "2024.01.15" with "2024년 1월 20일". Run the cleaner to normalize everything into analysis-ready rows. ## Quick Start Ask the AI to clean the attached CSV file by removing subtotal rows and normalizing the numbers and dates.

Frequently Asked Questions about csv-clean

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I remove subtotal and total rows from a CSV file?▼

Run the script with the --remove-subtotals flag to detect and drop rows containing keywords like subtotal, total, 소계, or 합계. Use --info first to preview which rows will be removed before applying changes.

How to convert comma-formatted numbers in CSV to numeric values?▼

Use the --clean-numbers flag to auto-detect columns with commas, currency symbols, or percent signs and convert them to numeric values. To target specific columns only, pass them with --clean-numbers-cols.

Can I normalize mixed date formats in a CSV column?▼

Yes, the --normalize-dates flag parses formats like YYYY-MM-DD, YYYY.MM.DD, YYYYMMDD, and Korean YYYY년 M월 D일, then rewrites them to a uniform format. Set the target with --date-format, defaulting to %Y-%m-%d.

Does the CSV cleaner overwrite my original file?▼

By default it writes a new file named with a _cleaned suffix, leaving the original untouched. Use --inplace to overwrite the original or --output to specify a custom path.

What Python dependencies does the CSV cleaning script need?▼

The script requires pandas version 2.0.0 or higher, installed via pip install pandas or the included requirements.txt. All other functionality uses Python standard library modules.

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