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/visualization-builder

@fcb0454

Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.

Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/visualization-builder

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referencesvisual_design_principles.md

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Visual Design Principles for Data Visualisation

Signal-to-noise ratio

Every element in a chart is either signal (it communicates data) or noise (it takes up space without adding meaning). Maximise signal; eliminate noise.

Noise examples:

  • Gridlines that are dark or numerous
  • Borders around chart areas
  • Backgrounds that are not plain white
  • Tick marks when labels suffice
  • Legends when direct labels are possible
  • Decorative icons or clip art

The test: Cover an element. Does the chart still make sense? If yes, remove it.


Pre-attentive attributes

Pre-attentive attributes are visual properties perceived instantly, before conscious thought. Use them to guide the eye to the most important element.

Attribute Use for
Colour (hue) Categorisation; highlighting
Colour (saturation/brightness) Magnitude within a category
Size Magnitude in scatter/bubble charts
Position All quantitative comparisons
Shape Categorical distinction in scatter plots
Motion Changes over time (animated only)

Key rule: Use only one or two pre-attentive attributes in a single chart. More creates competing focal points.


Colour principles

Encode meaning, not decoration. Colour should communicate something: category membership, direction (positive/negative), or relative magnitude.

Limit the palette:

  • Sequential data: single colour, varying lightness (e.g., light blue to dark blue)
  • Diverging data: two colours from a neutral midpoint (e.g., red–white–blue)
  • Categorical data: ≤ 6 distinct colours; grey out non-highlighted categories

Accessibility:

  • 8% of men and 0.5% of women are red-green colour blind
  • Never use red and green alone to encode "good" and "bad"
  • Use blue–orange or blue–red as safe contrasting pairs
  • Test charts with a colour-blind simulator before publishing

Typography

  • Title: Bold, 14–16pt, states the finding not the description
  • Axis labels: Regular, 10–12pt, horizontal where possible
  • Data labels: Regular or medium, 10pt, placed to avoid overlap
  • Annotation: Italic or medium, 10pt, only for the key callout

Limit to two fonts: one for headings, one for all other text.


Layout and alignment

  • Align chart elements to an invisible grid
  • Charts in a grid layout should share axes where possible (enables direct comparison)
  • Group related charts with proximity or a shared background panel
  • Leave breathing room — charts should not touch text or each other

The five-second test

Show the chart to someone for five seconds, then ask: "What is the main takeaway?" If they can't answer, the chart is not communicating its message. Revise.

Common causes of failure:

  • No clear focal point (too many equally-weighted elements)
  • The title describes the data, not the finding
  • The most important data is not visually prominent
  • Too much information competing for attention

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    The visualization-builder skill is a secure toolset designed to help users create professional data visualizations. It includes a Python script for chart generation and several markdown guides for design and selection. The skill does not perform any network operations, access sensitive files, or execute untrusted code. It uses standard libraries for plotting and provides a safe fallback to text-based charts.

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    Risk: LOW · No issues

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    Score: 93/100 · 2 sections analyzed

Signed by skilld at fcb0454. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 5 days ago.

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