免费在 GitHub 公开

data-visualization

Create clear, accurate charts for dashboards, data exploration, and reports. Choose chart types that fit the message, refine labels and scales, use accessible colors, and add context and annotations to help readers understand the findings.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Create effective data visualizations using best practices for clarity, accuracy, and visual communication of insights

kyegomez/swarms

查看安装方法

适用场景

Designing a sales dashboard for executives
Exploring patterns in customer behavior
Creating charts for an annual report
Reviewing chart scales, labels, and colors

包含的文件（1）

SKILL.md3.2 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

Data Visualization Skill

When creating data visualizations, follow these principles to ensure clear and effective communication:

Core Principles

1. Choose the Right Chart Type

Line Charts: Trends over time, continuous data
Bar Charts: Comparing categories, discrete data
Scatter Plots: Relationships between variables, correlations
Pie Charts: Parts of a whole (use sparingly, max 5-6 segments)
Heatmaps: Patterns in large datasets, correlations
Box Plots: Distribution statistics, outlier detection

2. Design Guidelines

Clarity

Use clear, descriptive titles and labels
Include units of measurement
Add a legend when multiple series are present
Ensure adequate contrast and readability

Accuracy

Start y-axis at zero for bar charts (unless good reason)
Use consistent scales across related charts
Avoid distorting data through inappropriate scaling
Label data points when precision matters

Simplicity

Remove chart junk and unnecessary decorations
Use color purposefully, not decoratively
Limit the number of colors (5-7 max)
Ensure accessibility (colorblind-friendly palettes)

3. Color Best Practices

Sequential: Use for ordered data (light to dark)
Diverging: Use for data with a meaningful midpoint
Categorical: Use for unordered categories
Highlight: Use accent colors to draw attention
Test accessibility with colorblind simulators

4. Storytelling with Data

Lead with the insight, not the data
Use annotations to highlight key findings
Arrange charts in logical flow
Provide context and comparisons
Include data sources and timestamp

Visualization Workflow

Understand the Data
- Explore data structure and distributions
- Identify key variables and relationships
- Determine the message to communicate
Select Visualization Type
- Match chart type to data characteristics
- Consider audience and use case
- Plan for interactivity if needed
Design the Visualization
- Create initial draft
- Apply design principles
- Optimize for clarity and impact
Refine and Validate
- Get feedback from stakeholders
- Test on target audience
- Iterate based on feedback
- Verify accuracy

Common Mistakes to Avoid

Using 3D charts unnecessarily (adds confusion)
Too many colors or visual elements
Missing or unclear axis labels
Truncated y-axis to exaggerate differences
Using pie charts for more than 5-6 categories
Poor color choices (rainbow colors for sequential data)

Tools and Libraries

Recommend appropriate tools based on needs:

Python: matplotlib, seaborn, plotly, altair
R: ggplot2, plotly
JavaScript: D3.js, Chart.js, Highcharts
BI Tools: Tableau, Power BI, Looker

Example Use Cases

Dashboard Design: "Create an executive dashboard for sales metrics"
Exploratory Analysis: "Visualize patterns in customer behavior data"
Report Charts: "Generate publication-ready charts for annual report"

评价

还没有评价。欢迎分享你的使用感受。

无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

同一仓库的技能

code-review

免费

Review code for readability, security weaknesses, performance bottlenecks, test quality, and ease of maintenance. Receive findings grouped by importance, with explanations and concrete suggestions that reflect the project's context and constraints.

中文简介正在准备中。当前显示英语简介。

Reviewing code quality before merging changes
Checking security and sensitive data handling
Finding slow queries and unnecessary API calls