Multidimensional Table Fast Analysis Assistant

The Problem

When business data lives in Excel or CSV files, the bottleneck is often repetitive work: removing duplicates, normalizing dates, selecting a summary column, building pivots, and taking screenshots. The Multidimensional Table Fast Analysis Assistant condenses that workflow into a Python pipeline for turning raw spreadsheets into shareable analysis artifacts.

How It Works

The skill centers on scripts/data_pipeline.py and accepts .xlsx, .xls, and .csv inputs, with automatic encoding detection. It reads the first populated sheet, performs column type detection, and identifies date, categorical, numeric, text, and ID columns. It then removes duplicates, standardizes dates, fills nulls, and produces group summaries, monthly trends, and cross-analysis.

Typical outputs include:
- _summary.xlsx: multi-sheet summary workbook with native formulas and bar, line, and pie charts
- _dashboard.html: interactive HTML dashboard with KPI cards, charts, and data tables
- _analysis.json: structured results for downstream development

Useful parameters include --group-by, --value-col, --fill-null, and --no-dedup. If the user has a clear analytic spec, dimensions and summary columns should be set explicitly; if they only need a quick read of the data, the default pipeline is sufficient.

Boundaries

The skill works best on medium-sized, reasonably clean tabular data—especially sales, operations, inventory, or cost tables with date, category, and amount fields. It is not a full BI modeling tool and does not define complex metrics. If the request evolves into “add profit margin” or “compare custom regions,” it is better to extend the output with pandas / openpyxl. Also, the HTML dashboard loads Chart.js from a CDN, so network access is required in offline or sandboxed environments.