Data Visualization
Data & Analyticsby anthropics
3/3 audits passApache-2.0
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles…
Data Visualization gives the agent a chart-selection guide organised by what the data shows: line charts for trends, horizontal bars for rankings, histograms for distributions, scatter plots for correlation, Sankey diagrams for flows and bullet charts for performance against target. It spells out when not to use pie, donut, 3D and dual-axis charts, then supplies Python patterns for matplotlib, seaborn and plotly: a professional style setup, colourblind-friendly palettes, line, bar, histogram, heatmap and small-multiples templates, and number-formatting helpers for axes.
The second half covers design principles for colour, typography, layout and accuracy, plus an accessibility checklist: the chart works without colour, the title states the insight, axes are labelled with units, and the data source and date range are noted, with alt text and a data-table alternative for screen readers. It suits analysts and anyone producing figures for reports. It is background guidance Claude applies on its own rather than a slash command, and it does not connect to data sources or assemble dashboards.
Imported into BusinessMCP it becomes a playbook the AI business analyst can follow when charting your workspace analytics, Stripe revenue or CRM numbers, and it is served to Claude, Cursor or ChatGPT over your hosted MCP endpoint. The Python snippets travel as instructions; BusinessMCP does not execute them.
What you can do with it
- Pick a chart type that matches the relationship in a dataset
- Produce a publication-quality matplotlib figure with labelled axes
- Replace a misleading pie or dual-axis chart with a clearer one
- Check a chart against an accessibility list before sharing
Run it on your business data
Imported into BusinessMCP, Data Visualization becomes a playbook your AI business analyst applies to your web analytics, funnels and connected databases.
Use Data Visualization in BusinessMCPInstall it in a coding agent
One command adds Data Visualization to your project.
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill data-visualizationHow we vetted it
- Source
- anthropics/knowledge-work-plugins at da38ec1
- Licence
- Apache-2.0
- Security audits (skills.sh)
- Gen Agent Trust Hub: Pass · Socket: Pass · Snyk: Pass
- Bundled scripts
- None, instructions only
Checked 2026-09-25 against its skills.sh listing. How we vet skills
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Frequently asked questions
Which libraries does Data Visualization cover?
It includes code patterns for matplotlib, seaborn and plotly, with a shared style setup and colourblind-friendly categorical, sequential and diverging palettes.
Does it build interactive dashboards?
No. It covers chart choice, figure code and design principles. For a self-contained HTML dashboard with filters, the same publisher's Build Dashboard skill is the fit.
How do I install Data Visualization?
Run `npx skills add anthropics/knowledge-work-plugins --skill data-visualization`, or import it from the BusinessMCP dashboard to use it as a playbook in your workspace.
Keep exploring
Run Data Visualization against your whole business
Free plan, no credit card.
Use Data Visualization in BusinessMCP