Data visualization
Skill yigityildiz0/universal-ai-skill-library/skills/common/data-visualization
Design, implement, critique, or validate charts and quantitative visualizations with correct encodings, annotations, accessibility, and source context. Use for chart, graph, data visualization, visualizing metrics, dashboard chart, or figure review.From its SKILL.md
npx -y skills add yigityildiz0/universal-ai-skill-library --skill data-visualizationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.0 KB, 145 tokens by cl100k_base, as published. Nobody here has run it
Data Visualization
Choose the chart that makes the decision-relevant comparison easiest to see.
- Identify the audience, decision, measure, comparison, uncertainty, and source date.
- Choose a simple encoding: position/length before color/area; avoid charts that exaggerate tiny differences.
- Label units, denominators, time window, filters, sample size where relevant, and uncertainty or data gaps.
- Use accessible contrast, non-color cues, readable annotations, and a text alternative or concise finding.
- Validate the data transformation and visually inspect the rendered output.
Do not use misleading truncated axes, decorative 3D, unlabelled dual axes, or color-only meaning without a reasoned exception.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.