About this skill

The Problem

When operations, finance, or admin teams process spreadsheets, data is often spread across multiple .xlsx, .csv, and .tsv files: mixed date formats, duplicate rows, concatenated fields, unmarked missing values, and invalid entries that break downstream analysis. This skill is for deterministic cleaning when headers are on the first row and files in the same directory follow consistent naming, not for semantic analysis or visualization.

How It Works

The skill follows a fixed input/output flow:
- Batch reading: Supports .xlsx, .xls, .csv (UTF-8/GBK), and .tsv, with single or multi-file batch processing.
- Field extraction: Splits target fields from combined columns using delimiters or rules, such as extracting a birth date from “name + ID number.”
- Format normalization: Standardizes date and numeric fields, for example converting 2024/1/5 to 2024-01-05.
- Deduplication and validation: Removes fully duplicated rows; flags missing values and identifies anomalies such as negative age or numeric fields stored as text.
- Result output: Produces a cleaned file and a cleaning log with per-row results and failure details.

Boundaries

It does not understand natural language semantics, so it cannot determine whether “Zhang San” and “Zhang San” are the same person unless rules are configured. It does not support tables in images or PDFs, encrypted or corrupted files, or headers not in the first row without manual specification. It does not provide visualization, statistical analysis, or automated business decisions.

Screenshots

Use Cases

Operations receives multiple monthly sales .xlsx files and needs date columns normalized to YYYY-MM-DD.
Finance receives a customer CSV where name and phone number are combined in one column and need splitting.
Admin processes an event registration TSV, removing fully duplicated rows and flagging missing or invalid values.
A data analyst merges monthly order XLSX files and needs numeric formats unified before downstream processing.

Best For

Operations staff who consolidate consistently named monthly reports and need auditable cleaning logs
Data analysts who split customer CSV fields, remove duplicates, and flag invalid values
Finance staff who clean Excel reimbursement, ledger, or payment files monthly
Admin staff who maintain registration forms and other administrative spreadsheets