AI Agent Data Visualization Workflow

Problem

A common failure mode in data visualization is not inability to render a chart, but an agent defaulting to generic templates: bar charts for trends, pie charts for relationships, gaps from missing values, too many colors, unlabeled axes, and long category labels. The harder task is to start from the analytical question and produce charts suitable for reports, slide decks, or dashboards.

How the skill works

The skill gives an agent a structured workflow: inspect variable types, time granularity, category counts, and the relationship the user wants to highlight, then map the goal to a chart form. Use bar charts for category comparisons, line charts for temporal trends, scatter plots for continuous variable relationships, histograms or KDE plots for distributions, box or violin plots for group distributions, heatmaps for correlation matrices, stacked bars or pie charts for composition, and plotly for interactive exploration.

Key capabilities include:
- Static charts: use matplotlib for precise control over axes, legends, annotations, and export details, and use seaborn for statistical plotting with sensible defaults.
- Interactive charts: use plotly or plotly.express to render HTML with hover, zoom, and pan.
- Production details: maintain colorblind-friendly palettes, human-readable number formats, unit labels, and plot dimensions; for print-oriented output, target 150+ DPI.
- Edge cases: aggregate long bar categories into “Other” or use a treemap; handle overlapping scatter points with opacity, jitter, or density plots; interpolate or break line-chart gaps; apply log scales to skewed data and label the transformation.

Boundaries

Best for structured-data exploration, report figures, dashboard prototypes, and cases where the chart type should be derived from the question. It is not a BI tool and does not define business metrics. If metric definitions are unclear, data quality is poor, or real-time high-interaction needs dominate, clean the data and define requirements first. For composition problems with many categories, prefer stacked bars, faceting, or small multiples over adding more colors.