Agent skill

data-visualization

Chart and visualization generation for DBX Studio. Use when a user wants to visualize data — bar charts, line graphs, pie charts, scatter plots, etc.

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Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dbxstudio/data-visualization

SKILL.md

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"

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