# Generate Data Charts and Dashboards

Teams often need clear charts without choosing every visualization detail manually. This skill helps Claude, Codex, and Claude Code turn structured data into chart plans, code examples, exports, and dashboard layouts.

## Install

```bash
npx skillstore add curiouslearner/chart-generator
```

## Metadata

- - Status: approved
- - Slug: curiouslearner-chart-generator
- - Version: 1.0.0
- - Author: CuriousLearner
- - GitHub username: CuriousLearner
- - License: MIT
- - Repository: https://github.com/CuriousLearner/devkit/tree/main/skills/chart-generator
- - Ref: main
- - Supported tools: Claude, Codex, Claude Code
- - Risk level: low
- - Risk factors: filesystem
- - Quality score: 76
- - Public page: https://skillstore.pages.dev/skills/curiouslearner-chart-generator
- - Manifest: https://skillstore.pages.dev/api/skills/curiouslearner-chart-generator/manifest

## Capabilities

- Recommends chart types for comparisons, trends, distributions, relationships, and composition.
- Provides Python chart examples using Matplotlib, Seaborn, and Plotly.
- Provides JavaScript chart examples using Chart.js rendering patterns.
- Shows how to export charts as PNG, SVG, PDF, JPG, and interactive HTML.
- Includes chart styling guidance for labels, legends, themes, annotations, and accessibility.
- Outlines a pipeline for generating multiple charts from chart configuration data.

## Use Cases

- Prepare Business Review Charts: Turn sales, finance, or operations tables into clear comparison and trend charts for review meetings.
- Prototype Interactive Dashboards: Create Plotly or Chart.js dashboard drafts before building a production reporting workflow.
- Improve Report Visuals: Select readable chart types, labels, colors, and export formats for reports and publications.

## Prompt Templates

### Choose a Chart Type

```
Use chart-generator to review this dataset summary and recommend the best chart type. Explain the choice and list the required columns.
```

### Create a Static Chart

```
Use chart-generator to create a Matplotlib chart for this CSV. Include a title, axis labels, readable colors, and a PNG export.
```

### Build an Interactive View

```
Use chart-generator to design an interactive Plotly chart for these metrics. Include hover details, filters, and an HTML export plan.
```

### Generate a Chart Pipeline

```
Use chart-generator to design a reusable pipeline that creates bar, line, scatter, and heatmap charts from this configuration table.
```

## Limitations

- The skill is documentation and examples, not a packaged chart application.
- Several snippets are illustrative and may need cleanup before direct execution.
- It does not validate source data quality or statistical correctness automatically.
- It does not provide built-in access control for generated output paths.

## Best Practices

- Validate column names, units, and missing values before generating charts.
- Use chart types that match the question, not only the available data.
- Keep output files inside the project workspace and use clear file names.

## Anti Patterns

- Do not use pie charts for many categories or precise comparisons.
- Do not accept untrusted output paths without review.
- Do not rely on default colors when accessibility or brand consistency matters.

## Security Audit

- - Safe to publish: true
- - Audited at: 2026-06-29T03:18:07.292\+00:00
- - Summary: Static analysis reported many high-risk matches, but review found the command execution, weak cryptography, environment access, and reconnaissance matches are false positives from Markdown fences or ordinary chart text. The only confirmed risk is expected filesystem output behavior for generated chart files, so publication is acceptable with normal file path caution.

## Stats

- - Views: 252
- - Downloads: 11
- - Favorites: 0
- - Popularity score: 0