cgpt-data-viz-auditor

name	cgpt-data-viz-auditor
description	Audit statistical figures, charts, maps, and plotting code for accessibility, labels, color, export quality, and academic journal readiness. Use when reviewing ggplot2, matplotlib, rendered figures, manuscript graphics, maps, or multi-figure submissions before delivery or journal submission.

CGPT Data Viz Auditor

Use this skill to review figures that already exist as code or rendered artifacts. The goal is a concrete audit with specific fixes, not a general design critique.

Scope

Audit:

• ggplot2, matplotlib, seaborn, base R, and static exported figures.
• Manuscript figures, tables-as-figures, multi-panel plots, diagrams, and maps.
• Accessibility and color reliability, including color vision deficiency and contrast.
• Journal readiness for marketing, management, social science, and policy-style submissions.
• UW-branded charts when the project calls for UW styling.

Do not use this skill to invent new statistical analyses. If the final artifact is DOCX, PPTX, PDF, HTML, or a rendered report, hand off final structural/output validation to cgpt-render-check.

Audit Workflow

1. Identify target venue, medium, and final size when known: journal, slide, report, web, single-column, double-column, print, or screen.
2. Inspect the source code and/or rendered image.
3. Check labels, accessibility, color, layout, journal/export requirements, and figure numbering/captions.
4. Provide PASS/WARN/FAIL with prioritized issues and exact code or artifact fixes.
5. If code was changed, render the figure again when feasible.
6. Run or recommend cgpt-render-check on the final containing artifact.

Core Checklist

Labels And Text

• Axis titles are present and include units or scale transformations.
• Tick labels are readable at final size and do not overlap.
• Legends have descriptive titles and human-readable entries.
• Panel labels are consistent and referenced correctly in the caption.
• Captions belong in the manuscript or report unless the target format requires embedded text.
• Minimum print readability: 8 pt for minor labels, 10 pt or larger for axis titles and legends.

Color And Accessibility

• Palette is colorblind-safe; prefer viridis, Okabe-Ito, or vetted Brewer palettes.
• Color is not the only encoding for categories; add shape, linetype, direct labels, pattern, or faceting.
• Data marks, labels, and map boundaries have sufficient contrast against the background.
• Avoid rainbow/jet palettes, red-green-only comparisons, and default hue scales for many groups.
• For UW work, follow uw-accessibility: exact UW colors, Spirit Gold decorative only, and no color-only meaning.

Layout And Interpretation

• Plot has a clear visual hierarchy and enough margins; labels are not clipped.
• Aspect ratio is appropriate for the venue.
• Gridlines are lighter than data marks.
• Multi-panel figures use consistent scales, labels, themes, and legend treatment unless differences are intentional.
• Direct labels are preferred when they reduce legend lookup.
• The figure supports one clear takeaway; remove decoration that competes with data.

Journal And Export Standards

• Export is at least 300 DPI for print; use 600 DPI for line art when required.
• Vector PDF/SVG/EPS is preferred for line charts and diagrams when accepted.
• Raster PNG/TIFF dimensions match target column width and file-size limits.
• Fonts are embedded or standard enough for the submission pipeline.
• Figure numbering, captions, and in-text references match the manuscript.
• Do not rely on low-resolution screenshots for submission graphics.

Maps And Spatial Figures

• Include a legend, scale bar or clear scale context, projection/CRS when relevant, and data source note.
• Choropleth bins are meaningful, labeled, and not misleading.
• Missing data is explicitly encoded and labeled.
• Avoid map projections or color ramps that distort the intended comparison.
• Boundary lines, labels, and overlays remain legible at final size.

Common Fixes

For ggplot2:

scale_color_viridis_d(option = "D")
scale_fill_viridis_d(option = "D")
labs(x = "Year", y = "Revenue (USD millions)", color = "Segment")
theme_minimal(base_size = 11) +
  theme(panel.grid.minor = element_blank(),
        legend.position = "bottom")
ggsave("figure-1.tiff", width = 7, height = 5, dpi = 300, compression = "lzw")

For matplotlib:

import matplotlib as mpl

mpl.rcParams.update({
    "font.size": 11,
    "axes.labelsize": 11,
    "xtick.labelsize": 9,
    "ytick.labelsize": 9,
    "legend.fontsize": 9,
})
fig.savefig("figure-1.pdf", bbox_inches="tight")
fig.savefig("figure-1.png", dpi=300, bbox_inches="tight")

Report Format

Use a compact result:

Status: PASS/WARN/FAIL
Figure(s): path or identifier
Critical issues: count and short list
Recommended fixes: exact changes
Verification: rendered/inspected, plus final cgpt-render-check status or handoff

Rules

• Ground findings in visible evidence or source code.
• Separate accessibility failures from style preferences.
• Do not silently change the statistical meaning of a plot.
• When a chart is embedded in a larger deliverable, finish with cgpt-render-check on that deliverable.