chart-builder

name	chart-builder
description	Use this skill whenever the user asks to visualize, chart, plot, or graph data (bar, line, scatter, histogram, pie) from a CSV, table, or DataFrame. It gives the agent prebuilt, parameterized matplotlib functions so charts are produced faster and more reliably — with consistent styling and sane defaults — instead of hand-writing matplotlib each time.

Generate charts from tabular data via the bundled scripts/charts.py toolkit. Each function takes a DataFrame or a file path plus the columns to plot, and saves a PNG (returning the path). It handles theming, figure sizing, label rotation, NaN dropping, legend placement, and saving.

Instructions

1. Provide the data source: a pandas DataFrame, or a path to a .csv / .tsv / .json file.
2. Call the function for the chart you need, passing the column names:
   • bar(data, x, y) — one value per category (horizontal=True for barh)
   • grouped_bar(data, x, y, group) — value per category split by group (stacked=True to stack)
   • line(data, x, y, group=None) — value over an ordered axis, one line per group
   • scatter(data, x, y, group=None, size=None) — two numeric columns
   • histogram(data, y, bins=20, group=None) — distribution of one column
   • pie(data, x, y, donut=False) — share of y per category x
3. Optional keyword args on every chart: title, xlabel, ylabel (derived from column names if omitted), out (default "chart.png"; pass None to skip saving), dpi (default 150), figsize, palette.
4. The function returns the saved image path. See references/cheatsheet.md for full signatures and a chart-selection table.

Usage

Import:

from charts import bar, line, pie
bar("sales.csv", x="region", y="revenue", title="Revenue by region",
    out="revenue.png")

CLI:

python scripts/charts.py grouped_bar sales.csv \
    --x region --y revenue --group quarter --stacked --out by_quarter.png

Bundled files

• scripts/charts.py — the toolkit: load_data, apply_theme, save_fig, and six chart functions (bar, grouped_bar, line, scatter, histogram, pie), plus a CLI. Depends on matplotlib and pandas; runs headless.
• references/cheatsheet.md — chart-selection table, full signatures, and copy-paste CLI examples.
• assets/sample_sales.csv — a small demo dataset that exercises every chart.

Defaults

• Colorblind-friendly palette and a shared theme across charts.
• Figure size, x-label rotation, and legend placement adapt to the data.
• Rows with missing values in the plotted columns are dropped before drawing.
• Output is a saved PNG at 150 DPI by default (override out and dpi).