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Map visualization

Skill Dragoon0x/everything-design-taste/skills/map-visualization

A taste system for AI agents. 288 skills, 80 agents, 12 rules, and 8 hooks that turn generic AI output into work with genuine design quality. Anti-slop detection, typography craft, color systems, brand voice, product strategy, and 50+ specialized reviewers across UI, industry, platform, and content domains.

Install
npx -y skills add Dragoon0x/everything-design-taste --skill map-visualization

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Choropleth maps, point maps, heat maps, and geographic data visualization.

SKILL.md

1.0 KB, as published. Nobody here has run it

Map Visualization

Types

TypeDataExample
ChoroplethValues per regionElection results by state
Point/DotLocationsStore locations
Heat mapDensityCrime hotspots
FlowMovementMigration patterns
BubbleLocation + magnitudeCity population

Rules

  1. Use sequential color scales for quantitative data (light to dark)
  2. Use diverging scales for data with a meaningful midpoint
  3. Provide a legend with clear labels
  4. Zoom and pan for interactive maps
  5. Tooltips on hover showing exact values
  6. Don't use rainbow color scales (perceptually misleading)

Accessibility

  • Don't rely on color alone (add patterns or labels)
  • Provide data table alternative
  • Keyboard navigable map markers
  • Screen reader announcements for key data points

Gives 0 of the 12 instructions most data analysis skills give

Counted across 286 of the 286 authors here whose files we hold, read 2026-08-06

  • use excel formulas instead of hardcoded calculated valuesin 35 of 286, across 7 files
  • match existing template conventions when modifying filesin 35 of 286, across 7 files
  • document sources for all hardcoded valuesin 35 of 286, across 7 files
  • write minimal concise python codein 35 of 286, across 7 files
  • place all assumptions in separate assumption cellsin 32 of 286, across 5 files
  • apply industry-standard color coding to financial modelsin 31 of 286, across 5 files
  • format years as text stringsin 30 of 286, across 3 files
  • recalculate formulas using recalc.py after modificationsin 30 of 286, across 3 files
  • format negative numbers using parenthesesin 30 of 286, across 3 files
  • fix all identified formula errors before finishingin 27 of 286, across 1 file
  • use colorblind-safe palettesin 19 of 286, across 12 files
  • Name tests after the prevented bugin 13 of 286, across 8 files

Said here and by no other author read

  • use sequential scales for quantitative data
  • use diverging scales for data with a midpoint
  • add zoom and pan for interactive maps
  • show tooltips on hover with exact values
  • add patterns or labels alongside color
  • provide a data table alternative

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.