Data Visualization

CommunityPopular

kyegomez

data-visualization

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

Overview

Publisher	kyegomez
Repository	`swarms`
Skill name	`data-visualization`
Stars	7.2K
Forks	1K
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by kyegomez on GitHub. Read the source before you install it.

Installation

Install the Data Visualization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/kyegomez/swarms/tree/master/examples/single_agent/capabilities/skills/data-visualization

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/kyegomez/swarms.git /tmp/swarms
mkdir -p .claude/skills
cp -r /tmp/swarms/examples/single_agent/capabilities/skills/data-visualization .claude/skills/data-visualization

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Visualization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Visualization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Visualization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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"

Frequently asked questions

What does the Data Visualization AI skill do?

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

Why use Data Visualization on TypingMind?

Because you install it once and use it with any model. Data Visualization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Visualization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kyegomez/swarms/tree/master/examples/single_agent/capabilities/skills/data-visualization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Data Visualization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Visualization?

As many as you like. As long as a model supports skills, you can use Data Visualization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Visualization AI skill free?

Yes. It is published on GitHub by kyegomez under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Visualization Skill

Core Principles

1. Choose the Right Chart Type

2. Design Guidelines

3. Color Best Practices

4. Storytelling with Data

Visualization Workflow

Common Mistakes to Avoid

Tools and Libraries

Example Use Cases

Frequently asked questions

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