Data storytelling
Skill Dragoon0x/everything-design-taste/skills/data-storytelling
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.
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Data narrative design, chart annotation, and turning numbers into compelling stories.
SKILL.md
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Data Storytelling
Data Narrative Principles
- Lead with the insight, not the data. "Sales doubled in Q3" before showing the chart.
- One story per chart. If a chart tells two stories, make two charts.
- Annotate the interesting parts. Labels on peaks, dips, and inflection points.
- Provide context. "42% increase" compared to what? Industry average? Last year? Goal?
- Design for the audience. Executives want conclusions. Analysts want data.
Chart Annotation
- Callout labels on data points that matter.
- Reference lines for targets/goals/averages.
- Shaded regions for time periods of interest.
- Brief text annotations explaining anomalies.
Presentation Order
- Headline: The conclusion in one sentence.
- Chart: Visual evidence.
- Context: Comparison, trend, benchmark.
- Implication: What this means for the business.
- Action: What to do about it.