data-visualization-tool
Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.
Best use case
data-visualization-tool is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.
Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "data-visualization-tool" skill to help with this workflow task. Context: Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-visualization-tool/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-visualization-tool Compares
| Feature / Agent | data-visualization-tool | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Visualization — DBX Studio
## Chart Types Available
The `generate_chart` tool supports these types:
| Type | Best For |
|------|----------|
| `bar` | Comparisons between categories |
| `line` | Trends over time |
| `pie` | Part-to-whole relationships (< 7 slices) |
| `scatter` | Correlation between two numeric values |
| `area` | Cumulative trends over time |
| `histogram` | Distribution of a numeric column |
## Workflow
1. Understand what the user wants to visualize
2. Write the SQL query to get the data (`data_query`)
3. Call `generate_chart` with the config
4. Confirm chart title and axes are meaningful
## generate_chart Parameters
```json
{
"chart_type": "bar",
"title": "Monthly Revenue by Product Category",
"x_axis": "category",
"y_axis": "revenue",
"data_query": "SELECT category, SUM(amount) AS revenue FROM orders GROUP BY 1 ORDER BY 2 DESC",
"group_by": "category"
}
```
## Chart Selection Guide
**User says "trend" or "over time"** → `line` chart, x_axis = date column
**User says "compare" or "by category"** → `bar` chart
**User says "breakdown" or "share"** → `pie` chart (only if ≤ 7 categories)
**User says "distribution" or "spread"** → `histogram`
**User says "relationship" or "correlation"** → `scatter`
## Data Query Patterns
### Bar: Top N categories
```sql
SELECT category, COUNT(*) AS count
FROM orders
GROUP BY category
ORDER BY count DESC
LIMIT 10
```
### Line: Time series
```sql
SELECT DATE_TRUNC('day', created_at) AS date, SUM(amount) AS revenue
FROM orders
WHERE created_at >= NOW() - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1
```
### Pie: Proportion breakdown
```sql
SELECT status, COUNT(*) AS count
FROM orders
GROUP BY status
```
## Design Principles
- Always give the chart a descriptive title including the time period if relevant
- Keep x_axis and y_axis names human-readable (not raw column names)
- For large result sets, aggregate before charting (avoid raw row-level data)
- Pie charts: max 7 slices, group remainder as "Other"Related Skills
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