Excel2Insights Pro

Analyze and visualize Excel/CSV/TSV data with automated stats, quality checks, charts, and generate structured insight reports in Markdown or HTML.

ChengQian@cqdev-ai

Excel2Insights

Overview

Excel2Insights is a data analysis and visualization tool designed for structured data (CSV, XLSX, XLS, TSV). It helps you quickly understand the contents of any tabular dataset through automated statistical analysis, data quality checks, visualization generation, and structured reporting.

The skill works in two modes:

Quick Scan — Load a file, preview shape/dtypes, descriptive stats, and missing values
Deep Analysis — Full pipeline: profile + clean + analyze + visualize + report

When to Use

You receive a CSV/XLSX and need to understand what's in it fast
You need a structured data quality report before further processing
You want to generate charts and summaries from tabular data
You need to compare two datasets or check for anomalies
You want to transform raw data into a readable insight report

Workflow

Step 1: Load & Inspect

Use excel-reader.py to load the file. The script returns:

File metadata (rows, columns, size)
Column names and dtypes
First/last 5 rows preview
Missing value count per column
Basic data quality flags

python

# Agent internally calls:
python3 scripts/excel-reader.py --file path/to/data.csv
# Output: structured JSON with metadata + preview

Step 2: Statistical Analysis

Use data-analyzer.py to compute:

Numeric columns: mean, median, std, min, max, quartiles, skewness
Categorical columns: value counts, unique count, mode, frequency distribution
Date columns: date range, gaps, frequency
Cross-column: correlation matrix (numeric), cross-tabulation (categorical)

Output is structured JSON grouped by column type.

Step 3: Data Quality Check

Built into the analysis phase:

Missing value ratio per column
Duplicate rows
Outlier detection (IQR method)
Data type inconsistencies
Value range violations

Flagged issues are included in the final report.

Step 4: Generate Visualizations

Use chart-generator.py to create charts:

histogram — distribution of numeric columns
boxplot — outliers and spread for numeric columns
bar — frequency counts for categorical columns
line — trends for date-indexed data
pie — proportion breakdown for categorical data
correlation heatmap — pairwise numeric correlation
scatter — relationship between two numeric columns
pairplot — pairwise scatter matrix (small datasets)

All charts are saved as PNG files and embedded into the final report.

Step 5: Generate Report

Use report-generator.py to produce a comprehensive Markdown report containing:

Executive summary
File overview (metadata + quality)
Column-by-column analysis
Charts gallery
Key insights and anomalies
Recommendations

The report is written to output/ directory.

Available Scripts

Script	Description	Usage
`scripts/excel-reader.py`	Load file, display metadata and preview	`python3 scripts/excel-reader.py --file DATA`
`scripts/data-analyzer.py`	Full statistical analysis	`python3 scripts/data-analyzer.py --file DATA [--output JSON_OUTPUT]`
`scripts/chart-generator.py`	Generate visualization charts	`python3 scripts/chart-generator.py --file DATA [--charts CHART_TYPES] [--output DIR]`
`scripts/report-generator.py`	Generate markdown insight report	`python3 scripts/report-generator.py --file DATA [--charts DIR] [--output REPORT_PATH]`
`scripts/auto-pipeline.py`	Run full pipeline (analyze + chart + report)	`python3 scripts/auto-pipeline.py --file DATA [--charts CHART_TYPES] [--output DIR]`

Script Options

excel-reader.py

--file PATH — Path to Excel/CSV/TSV file
--sheet NAME — Sheet name (XLSX only, default: first sheet)
--encoding ENC — File encoding (default: utf-8)
--preview N — Preview rows count (default: 5)

data-analyzer.py

--file PATH — Path to data file
--output PATH — Output JSON path (optional)
--correlation — Include correlation matrix
--outliers — Enable outlier detection
--categorical-top N — Top N value counts (default: 20)

chart-generator.py

--file PATH — Path to data file
--charts CHART_TYPES — Comma-separated: histogram,boxplot,bar,line,pie,heatmap,scatter,pairplot
--columns COLS — Specific columns to visualize
--output DIR — Output directory (default: output/charts)
--theme THEME — Chart theme (default: clean)

report-generator.py

--file PATH — Path to data file
--analysis PATH — Pre-computed analysis JSON (optional)
--charts DIR — Charts directory (optional)
--output PATH — Output report path (default: output/report.md)
--format FORMAT — Output format: markdown, html, pdf

auto-pipeline.py

--file PATH — Path to data file (required)
--charts CHART_TYPES — Chart types (default: histogram,boxplot,bar,correlation)
--output DIR — Output directory (default: output/)
--format FORMAT — Report format (default: markdown)

Examples

Example 1: Quick scan a CSV file

Agent reads a CSV and runs the reader:

text

📄 File: sales_data.csv
   Rows: 10,000 | Columns: 15 | Size: 1.2 MB

📊 Column Overview:
   date        datetime64  non-null  10000
   product     object      non-null  10000
   category    object      non-null  9985    (0.15% missing)
   revenue     float64     non-null  10000
   quantity    int64       non-null  9990    (0.10% missing)

⚠  Data Quality Flags:
   - 2 columns with missing values (<1%) — acceptable
   - 12 duplicate rows detected (0.12%)

Example 2: Full deep analysis pipeline

Agent runs the auto-pipeline on a sales dataset:

bash

python3 scripts/auto-pipeline.py --file data/sales_2024.csv --charts histogram,bar,line,heatmap

Output: