visualization-expert_skill

Overview

This skill provides expert guidance for selecting chart types and designing visualizations that communicate data clearly and accurately. It helps you choose the right visual form, apply proven design principles, and produce actionable code examples for common plotting libraries. Use it to improve clarity, accessibility, and interpretability of your charts and dashboards.

How this skill works

I analyze the analytical goal, data structure, and target audience to recommend chart types and layout strategies. Recommendations include rationale, concrete design rules, and code snippets for matplotlib or Plotly to implement the visualization. I also flag common pitfalls like misleading scales, excessive chart junk, and accessibility issues, and offer interpretation tips for readers.

When to use it

• Choosing a chart type for a specific analytic question (comparison, trend, distribution, relationship, composition)
• Designing dashboards or multi-chart layouts with clear hierarchy
• Improving readability and accessibility of existing charts
• Preparing visuals for presentations or reports to non-technical audiences
• Translating complex data tables into effective graphics

Best practices

• Match chart type to the question: use bar for categorical comparison, line for trends, histogram/box for distributions, scatter for relationships
• Prioritize clarity: label axes, use meaningful scales, remove unnecessary gridlines and decorations
• Ensure honesty: start axes appropriately and avoid distorted ratios or truncated scales
• Support accessibility: use colorblind-friendly palettes, add direct labels and sufficient contrast
• Keep visuals simple: focus on one main message per chart and use annotations to guide interpretation

Example use cases

• Comparing sales across regions: recommend clustered bar chart with percentage labels and sorted categories
• Showing customer age distribution: suggest histogram with KDE or box plot for outlier insight
• Exploring correlation between advertising spend and conversions: propose scatter plot with trend line and marginal histograms
• Dashboard overview: outline tile layout with summary KPI cards, a trend line for time series, and a bar chart for top contributors
• Presenting composition over time: advise stacked area or small multiples instead of a single 100% stacked chart for clarity