Skill v1.0.0

version: "1.0.0" 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.

1. 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

1. 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.

1. 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).