Excel Chart Generation

Problem it solves

When an .xlsx or .xls file already contains category, date, and numeric columns, manually setting axes, choosing a chart type, and cleaning column names can still introduce errors. Excel Chart Generation narrows the workflow to previewing the data, confirming parameters, and rendering a chart. It is useful for turning a spreadsheet into a static report figure or an interactive HTML chart.

How it works

The skill first runs the preview mode of scripts/generate_chart.py to read the workbook and report sheet names, the first 5 rows, column types, and numeric summaries. It then chooses the sheet, X axis, Y axis, chart type, and output format:
- Bar / horizontal bar: bar for comparing categories
- Line: line for time trends
- Pie / donut: pie for proportions
- Scatter: scatter for relationships between two numeric variables
- Box plot: box for distributions and outliers
- Heatmap: heatmap, requiring at least two numeric columns

The default output is html, producing a Plotly interactive file. The png option produces a static Matplotlib image. The script parses date strings, infers numeric and categorical columns, ignores fully empty columns, and prompts for batching or sampling when data exceeds 1000 rows. Missing values are marked rather than filled, so the data semantics should be confirmed before publishing the chart.

Boundaries

This is intended for visualizing structured Excel data, not for complex dashboard design or BI report layout. If field meanings are unclear, multiple sheets have inconsistent metrics, or the chart requires precise colors, legends, axis titles, and annotations, human confirmation is still needed.