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frontend-data-viz

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Mis à jour4 avril 2026 à 12:14

Map analytical questions to appropriate visual encodings, interactions, and dashboard behavior for vacancy-market data.

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ivanshamaev

ivanshamaev/ai-agent-codex

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Développeurs webProfessions informatiques et mathématiques·SOC 15-1254

SKILL.md

readonly

name	frontend-data-viz
description	Map analytical questions to appropriate visual encodings, interactions, and dashboard behavior for vacancy-market data.
license	MIT
compatibility	opencode
metadata	{"audience":"frontend-engineers-and-analysts","domain":"data-visualization"}

What I do

Choose chart types that fit the question being answered.
Define interactions such as hover, drill-down, compare, and filter sync.
Reduce misleading visualizations for sparse or incomplete salary data.
Keep time-series, distribution, and categorical views interpretable.

Typical mappings

trend over time -> line or area chart
category comparison -> bar chart
salary distribution -> histogram or box plot
vacancy mix by dimension -> stacked bar or small multiples

Guardrails

Show missing-data effects when they materially change interpretation.
Avoid dual-axis charts unless the comparison is clearly justified.

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name	frontend-data-viz
description	Map analytical questions to appropriate visual encodings, interactions, and dashboard behavior for vacancy-market data.
license	MIT
compatibility	opencode
metadata	{"audience":"frontend-engineers-and-analysts","domain":"data-visualization"}

What I do

Choose chart types that fit the question being answered.
Define interactions such as hover, drill-down, compare, and filter sync.
Reduce misleading visualizations for sparse or incomplete salary data.
Keep time-series, distribution, and categorical views interpretable.

Typical mappings

trend over time -> line or area chart
category comparison -> bar chart
salary distribution -> histogram or box plot
vacancy mix by dimension -> stacked bar or small multiples

Guardrails

Show missing-data effects when they materially change interpretation.
Avoid dual-axis charts unless the comparison is clearly justified.