What this skill does
Every CSV export from a real system has the same problems — inconsistent date formats, stray whitespace, duplicate rows from a retried sync, phone numbers in four different formats — and cleaning it by hand in a spreadsheet is slow and easy to get subtly wrong. This skill takes a messy CSV and fixes the common issues automatically, then reports exactly what it changed so nothing is silently altered without a record.
How it works
- Issue detection — scans every column for inconsistent formatting (mixed date formats, inconsistent casing, stray whitespace, mixed phone/currency formats), exact and near-duplicate rows, and empty-vs-null inconsistencies.
- Normalization — standardizes each column to a single consistent format (choosing the most common existing format as the target unless you specify one), trims whitespace, and removes exact duplicate rows.
- Change report — outputs the cleaned CSV alongside a plain-English change log: how many rows were deduplicated, which columns were reformatted and how, and any rows flagged as ambiguous for manual review rather than auto-corrected.
Requirements
- Works on Claude AI (Claude Code, or claude.ai with code execution enabled) — cleaning runs via code execution (Python/pandas) so it can handle large files without loading everything into the model's context at once.
Setup
- Unzip the package into your Skills directory, keeping the folder structure intact.
- Attach or point it at your CSV file, specifying any target formats you want enforced (e.g. force all dates to
YYYY-MM-DD). - Review the change report before treating the cleaned file as final — ambiguous rows are flagged, not silently guessed.
What you get
- A cleaned CSV with inconsistent dates, casing, whitespace, and formats standardized automatically
- Duplicate and near-duplicate rows removed, with counts reported
- A plain-English change log of exactly what was altered, so nothing changes silently
- Ambiguous rows flagged for your manual review rather than auto-corrected
- A worked example cleaning a sample messy export
