karthik-data-visualization

name	karthik-data-visualization
description	Use when creating, editing, reviewing, or critiquing charts, dashboards, statistical graphics, data visualizations, R/ggplot plots, matplotlib plots, SVG chart output, chart copy, annotations, legends, labels, color palettes, or visual analysis in any project. Applies Karthik's chart style and Tufte-inspired principles: data-ink ratio, chartjunk removal, graphical integrity, comparison, small multiples, sparklines, layering, and high-density displays. Use the embedded workflow before finalizing chart design or chart-related code.
metadata	{"short-description":"Karthik chart style","claude-description":"Create or review charts, dashboards, and data visualizations in Karthik's style. Use for plots, labels, palettes, annotations, and visual analysis."}

Karthik Data Visualization

Use this skill for any chart, graph, dashboard, or data visualization work, including chart-generating code, visual analysis pages, annotations, captions, labels, palettes, and chart review.

Apply the workflow below before finalizing design decisions or chart code. Private local references may add nuance, but this public skill is self-contained.

Workflow:

1. Clarify the analytical job: what comparison matters, what the viewer should learn, and what evidence supports it.
2. Choose the structure: line/point for time, range plot for intervals, small multiples for category comparison, table/sparkline for dense metric scans, bar/table for part-to-whole.
3. Build from data outward: data first, direct labels second, annotations third, grids/axes last.
4. Check graphical integrity: scales, baselines, proportional encoding, missing context, and any visual effect that exaggerates or understates the data effect.
5. Apply the eraser test: remove any ink that does not carry data, labels, or necessary context.
6. Render and inspect the exported image; adjust labels, spacing, hierarchy, and source notes from the actual output.

Core operating rules:

• Follow low-chartjunk, high data-ink, direct-labeling principles.
• Use white backgrounds by default; reserve warm beige only for Bangalore weather lineage or explicit continuation of that family.
• Prefer static PNG/SVG exports unless interactivity is explicitly needed.
• Label data directly when possible instead of relying on legends.
• Use domain-specific palettes where meaningful; avoid copying one chart family's colors blindly.
• Tune labels and spacing after rendering, not just from code inspection.
• Favor visual forms that make comparison and change easy to read.
• Make visual hierarchy match information hierarchy: data, labels, annotations, grids, borders.
• Show comparison and context explicitly; a chart should answer "compared to what?"
• Use color sparingly: gray for context, color for emphasis or true encoding.
• Let complexity come from the data, not decoration.

When writing or changing chart code:

• Keep the visual design deliberate, not library-default.
• Check that text is legible and non-overlapping at the intended output size.
• Make the chart stand alone without caveats doing all the work.
• Save public chart outputs with stable, descriptive filenames when the project expects exported artifacts.
• Prefer small multiples to crowded multi-series panels when comparison across groups is the task.
• Consider sparklines or compact tables when many series need shape plus current value.
• Consider range frames, rug marks, or labeled data points when axes or ticks can carry more information.
• Include enough source, scale, timeframe, and transformation notes for a stranger to evaluate the evidence.