Data Visualization

Official

anthropics·vlatest·data·Apache-2.0

Transforms complex datasets into clear, interactive charts and graphs for enhanced understanding and insights.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Run this in your project

terminal

$ npx skills add anthropics/knowledge-work-plugins --skill data-visualization -a claude-code

Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned 28 Sept 2026

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No credential access: passed
Reads no keys, tokens, keychains or credential files.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned anthropics/knowledge-work-plugins at da38ec1ee8.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: da38ec1ee8
Manifest: data/skills/data-visualization/SKILL.md
Scanned: 1 file, 290 lines

This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.

What each check looks for →

About this skill

What it does

The data-visualization skill enables AI agents to generate visual representations of complex datasets, such as charts, graphs, and dashboards. It supports a wide range of visualization types including bar charts, line graphs, scatter plots, heatmaps, and more, allowing users to quickly interpret trends, patterns, and outliers in their data.

When to use it

To present analytical findings in an easily digestible format for stakeholders or clients.
When exploring large datasets to identify correlations or anomalies visually.
During presentations or reports where visual storytelling enhances understanding of the data.
For real-time monitoring of key performance indicators (KPIs) through dynamic dashboards.

Key capabilities

Generation of multiple chart types (bar, line, pie, scatter, etc.)
Interactive and static visualization options
Integration with common data formats like CSV, Excel, and SQL databases
Customization of colors, labels, and layouts

Example prompts

"Create a bar chart showing monthly sales figures for the last year."
"Generate an interactive heatmap to visualize customer engagement across regions."
"Make a line graph comparing stock prices over time for Apple and Microsoft."

Tips & gotchas

Ensure your data is clean and properly formatted before generating visualizations.
Some advanced features may require specific libraries or tools, so check compatibility with your environment.