Claude-skill-registry ggterm-plot

Create terminal data visualizations using Grammar of Graphics. Use when plotting data, creating charts, graphing, visualizing distributions, or when the user mentions plot, chart, graph, histogram, scatter, boxplot, or visualize.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/ggterm-plot" ~/.claude/skills/majiayu000-claude-skill-registry-ggterm-plot && rm -rf "$T"

manifest: skills/data/ggterm-plot/SKILL.md

source content

Terminal Plotting with ggterm

Create plots using the CLI tool. Start by inspecting the data, then plot.

Step 1: Inspect Data (Recommended)

bun packages/core/src/cli-plot.ts inspect <data.csv>

Shows column names, types (numeric/categorical/date), unique counts, and sample values.

Step 2: Get Suggestions (Optional)

bun packages/core/src/cli-plot.ts suggest <data.csv>

Returns ready-to-run plot commands based on column types.

Step 3: Create Plot

bun packages/core/src/cli-plot.ts <data.csv> <x> <y> [color] [title] [geom]

Arguments:

```
data.csv
```
- Path to CSV file (use absolute path or relative to ggterm dir)
```
x
```
- Column name for x-axis
```
y
```
- Column name for y-axis (use
```
-
```
for histogram)
```
color
```
- Column name for color (optional, use
```
-
```
to skip)
```
title
```
- Plot title (optional, use
```
-
```
to skip)

geom

- Geometry type:

point

(default),

line

path

step

bar

col

histogram

freqpoly

density

boxplot

violin

ridgeline

joy

beeswarm

quasirandom

dumbbell

lollipop

waffle

sparkline

bullet

braille

calendar

flame

icicle

corrmat

sankey

treemap

area

ribbon

rug

errorbar

errorbarh

crossbar

linerange

pointrange

smooth

segment

curve

rect

tile

raster

bin2d

text

label

contour

contour_filled

density_2d

qq

qq_line

hline

vline

abline

Examples

Scatter plot:

bun packages/core/src/cli-plot.ts data/iris.csv sepal_length sepal_width species "Iris Dataset" point

Line chart:

bun packages/core/src/cli-plot.ts data/stocks.csv date price symbol "Stock Prices" line

Histogram:

bun packages/core/src/cli-plot.ts data/iris.csv sepal_width - - "Sepal Width Distribution" histogram

Box plot:

bun packages/core/src/cli-plot.ts data/experiment.csv treatment response_time - "Response by Treatment" boxplot

Workflow

Identify the data file from $ARGUMENTS or ask user
Run
```
inspect
```
to see column names and types
Run
```
suggest
```
to get recommended visualizations (or choose based on user request)
Run the plot command
Briefly describe what the plot shows

$ARGUMENTS

Geom Selection Guide

Data Question	Geom	Example
Relationship between 2 variables	`geom_point()`	Scatter plot
Trend over time	`geom_line()`	Time series
Distribution of 1 variable	`geom_histogram()`	Frequency distribution
Smoothed distribution	`geom_density()`	Kernel density estimate
Distribution by group	`geom_boxplot()`	Compare medians
Density shape	`geom_violin()`	Distribution shape
Stacked distributions	`geom_ridgeline()`	Joy plot / ridgeline
Individual points	`geom_beeswarm()`	Avoid overlap in groups
Before/after comparison	`geom_dumbbell()`	Two connected points
Sparse rankings	`geom_lollipop()`	Clean bar alternative
Part-of-whole	`geom_waffle()`	Grid-based pie alternative
Inline trends	`geom_sparkline()`	Word-sized charts
KPI progress	`geom_bullet()`	Progress with target
High resolution	`geom_braille()`	8x detail using braille
Activity over time	`geom_calendar()`	GitHub-style heatmap
Performance profiling	`geom_flame()`	Call stack visualization
Variable correlations	`geom_corrmat()`	Correlation matrix
Flow between categories	`geom_sankey()`	Source to target flows
Hierarchical proportions	`geom_treemap()`	Nested rectangles by value
Category comparison	`geom_bar()`	Counts per category
Known values per category	`geom_col()`	Bar heights from data
Trend with uncertainty	`geom_smooth()`	Fitted line
2D density	`geom_density_2d()`	Contour density
Filled region	`geom_area()`	Cumulative or stacked
Error ranges	`geom_errorbar()`	Confidence intervals
Normality check	`geom_qq()`	Q-Q plot
Multi-distribution comparison	`geom_freqpoly()`	Overlaid frequency lines

Common Plot Types

Scatter Plot

gg(data)
  .aes({ x: 'weight', y: 'height', color: 'species' })
  .geom(geom_point({ size: 2 }))

Line Chart

gg(data)
  .aes({ x: 'date', y: 'value', color: 'category' })
  .geom(geom_line())

Histogram

import { geom_histogram } from '@ggterm/core'

gg(data)
  .aes({ x: 'value' })
  .geom(geom_histogram({ bins: 20 }))

Box Plot

import { geom_boxplot } from '@ggterm/core'

gg(data)
  .aes({ x: 'group', y: 'value' })
  .geom(geom_boxplot())

Bar Chart

import { geom_bar } from '@ggterm/core'

gg(data)
  .aes({ x: 'category', fill: 'category' })
  .geom(geom_bar())  // Counts occurrences

Color and Styling

Color Scales

import { scale_color_viridis, scale_color_brewer } from '@ggterm/core'

// Viridis (perceptually uniform)
gg(data)
  .aes({ x: 'x', y: 'y', color: 'value' })
  .geom(geom_point())
  .scale(scale_color_viridis())

// ColorBrewer palettes
.scale(scale_color_brewer({ palette: 'Set1' }))  // Categorical
.scale(scale_color_brewer({ palette: 'Blues' })) // Sequential

Themes

import { themeDark, themeMinimal, themeClassic } from '@ggterm/core'

gg(data)
  .aes({ x: 'x', y: 'y' })
  .geom(geom_point())
  .theme(themeDark())      // Dark background
  // or .theme(themeMinimal())  // Clean, minimal
  // or .theme(themeClassic())  // Traditional

Faceting (Small Multiples)

import { facet_wrap, facet_grid } from '@ggterm/core'

// Wrap into grid
gg(data)
  .aes({ x: 'x', y: 'y' })
  .geom(geom_point())
  .facet(facet_wrap({ vars: 'category', ncol: 3 }))

// Grid by two variables
.facet(facet_grid({ rows: 'year', cols: 'region' }))

Scale Transformations

import { scale_x_log10, scale_y_sqrt } from '@ggterm/core'

gg(data)
  .aes({ x: 'population', y: 'gdp' })
  .geom(geom_point())
  .scale(scale_x_log10())
  .scale(scale_y_sqrt())

Layering Multiple Geoms

gg(data)
  .aes({ x: 'time', y: 'value' })
  .geom(geom_point({ alpha: 0.5 }))  // Points first
  .geom(geom_line())                  // Line on top
  .geom(geom_smooth({ method: 'loess' }))  // Trend line

Annotations

import { annotate_text, annotate_hline, annotate_rect } from '@ggterm/core'

gg(data)
  .aes({ x: 'x', y: 'y' })
  .geom(geom_point())
  .annotate(annotate_hline({ yintercept: 0, linetype: 'dashed' }))
  .annotate(annotate_text({ x: 10, y: 5, label: 'Important point' }))

Saving Plot Specifications

For reproducibility, save the PlotSpec alongside output:

import { writeFileSync } from 'fs'

const plot = gg(data).aes({ x: 'x', y: 'y' }).geom(geom_point())

// Get JSON-serializable specification
const spec = plot.spec()
writeFileSync('plot-spec.json', JSON.stringify(spec, null, 2))

// Render to terminal
console.log(plot.render({ width: 80, height: 24 }))

Render Options

plot.render({
  width: 80,           // Characters wide
  height: 24,          // Lines tall
  renderer: 'auto',    // 'braille' | 'block' | 'sixel' | 'auto'
  colorMode: 'truecolor'  // Use 'truecolor' for full color support
})

Quick Reference

For detailed examples, see examples/basic-plots.md.

All Available Geoms (52 total)

Point/line:

geom_point

geom_line

geom_path

geom_step

Bar:

geom_bar

geom_col

geom_histogram

geom_freqpoly

geom_density

Distribution:

geom_boxplot

geom_violin

geom_ridgeline

geom_joy

geom_beeswarm

geom_quasirandom

geom_density_2d

geom_qq

geom_qq_line

Comparison:

geom_dumbbell

geom_lollipop

Terminal-native:

geom_waffle

geom_sparkline

geom_bullet

geom_braille

Specialized:

geom_calendar

geom_flame

geom_icicle

geom_corrmat

geom_sankey

geom_treemap

Area:

geom_area

geom_ribbon

Reference:

geom_hline

geom_vline

geom_abline

geom_segment

geom_curve

Text:

geom_text

geom_label

Error bars:

geom_errorbar

geom_errorbarh

geom_crossbar

geom_linerange

geom_pointrange

2D/Tile:

geom_tile

geom_raster

geom_bin2d

geom_rect

geom_contour

geom_contour_filled

Other:

geom_rug

geom_smooth

All Available Scales

Position:

scale_x_continuous

scale_y_continuous

scale_x_log10

scale_y_log10

scale_x_sqrt

scale_y_sqrt

scale_x_reverse

scale_y_reverse

scale_x_discrete

scale_y_discrete

Color:

scale_color_continuous

scale_color_discrete

scale_color_viridis

scale_color_brewer

scale_color_gradient

scale_color_gradient2

scale_fill_*

(same variants)

Size:

scale_size_continuous

scale_size_area

scale_size_radius

DateTime:

scale_x_datetime

scale_y_datetime

scale_x_date

scale_y_date