Excel Intelligent Data Cleaning and Analysis Master

Problem

Messy Excel, CSV, and WPS spreadsheets rarely fail in one obvious way. Duplicate customers, blank fields, garbled text, and abnormal values are usually spread across multiple files, so merging, splitting by department or month, summing, calculating period-over-period changes, and matching with VLOOKUP-style lookups can easily drop fields or copy the wrong header. For finance, operations, HR, and sales teams, the hard part is not computing one metric; it is turning scattered workbooks into reviewable, consistently formatted reports.

How It Works

The skill works around a spreadsheet processing workflow: it first parses a natural-language instruction to identify file scope, processing actions, statistical dimensions, and output settings; then it reads and validates files, skips corrupted or misencoded files, and uses chunked reads for large tables.

Core capabilities include:
- Dirty-data cleaning: deduplicate by duplicate_key, fill missing values, remove abnormal values, and clean special symbols.
- Merging and splitting: align headers, merge same-structure files, or split by department, month, category, and other conditions.
- Formula and statistics: generate logic for totals, conditional checks, matching, and period comparisons, then build pivot-style aggregations.
- Charts and reporting: generate bar, line, or pie charts, normalize headers, fonts, borders, and colors, and output cleaning logs plus a processing summary.

Boundaries

It is best for structured two-dimensional tables such as sales summaries, finance reconciliation, attendance statistics, and customer data cleaning. Performance and accuracy may be limited when workbooks contain many complex merged cells, hand-drawn layouts, or non-standard content. Very large tables are processed in chunks, but lower-memory machines may run more slowly. Use it only with data you are authorized to process and avoid bulk exporting sensitive customer information.