Use when you need to choose the right visualization for your data and question, then create a narrated report that highlights insights and recommends actions. Invoke when analyzing data for patterns (trends, comparisons, distributions, relationships, compositions), building dashboards or reports, presenting metrics to stakeholders, monitoring KPIs, exploring datasets for insights, communicating findings from analysis, or when user mentions "visualize this", "what chart should I use", "create a dashboard", "analyze this data", "show trends", "compare these metrics", "report on", "what does this data tell us", or needs to turn data into actionable insights. Apply to business analytics (revenue, growth, churn, funnel, cohort, segmentation), product metrics (usage, adoption, retention, feature performance, A/B tests), marketing analytics (campaign ROI, attribution, funnel, customer acquisition), financial reporting (P&L, budget, forecast, variance), operational metrics (uptime, performance, capacity, SLA), sales analytics (pipeline, forecast, territory, quota attainment), HR metrics (headcount, turnover, engagement, DEI), and any scenario where data needs to become a clear, actionable story with the right visual form.
Selects optimal visualizations for your data and question, then creates narrated reports that highlight key insights and recommend specific actions. Triggers when analyzing data for patterns, building dashboards, presenting metrics, or when you ask "what chart should I use" or "visualize this.
/plugin marketplace add lyndonkl/claude/plugin install lyndonkl-thinking-frameworks-skills@lyndonkl/claudeThis skill inherits all available tools. When active, it can use any tool Claude has access to.
resources/evaluators/rubric_visualization_choice_reporting.jsonresources/methodology.mdresources/template.mdVisualization choice & reporting matches visualization types to questions and data, then creates narrated dashboards that highlight signal and recommend actions.
Three core components:
1. Chart selection: Match chart type to question type and data structure (comparison → bar chart, trend → line chart, distribution → histogram, relationship → scatter, composition → treemap, geographic → map, hierarchy → tree diagram, flow → sankey)
2. Visualization best practices: Apply perceptual principles (position > length > angle > area > color for accuracy), reduce chart junk, use pre-attentive attributes (color, size, position) to highlight signal, respect accessibility (colorblind-safe palettes, alt text), choose appropriate scales (linear, log, normalized)
3. Narrative reporting: Lead with insight headline, annotate key patterns, provide context (vs benchmark, vs target, vs previous period), interpret what it means, recommend next actions
When to use: Data analysis, dashboards, reports, presentations, monitoring, exploration, stakeholder communication
Copy this checklist and track your progress:
Visualization Choice & Reporting Progress:
- [ ] Step 1: Clarify question and profile data
- [ ] Step 2: Select visualization type
- [ ] Step 3: Design effective chart
- [ ] Step 4: Narrate insights and actions
- [ ] Step 5: Validate and deliver
Step 1: Clarify question and profile data
Define the question you're answering (What's the trend? How do X and Y compare? What's the distribution? What drives Z? What's the composition?). Profile your data: type (categorical, numerical, temporal, geospatial), granularity (daily, user-level, aggregated), size (10 rows, 10K, 10M), dimensions (1D, 2D, multivariate). See Question-Data Profiling.
Step 2: Select visualization type
Match question type to chart family using Chart Selection Guide. Consider data size (small → tables, medium → standard charts, large → heatmaps/binned), number of series (1-3 → standard, 4-10 → small multiples, 10+ → interactive/aggregated), and audience expertise (executives → simple with insights, analysts → detailed exploration).
Step 3: Design effective chart
For simple cases → Apply Design Checklist (clear title, labeled axes, legend if needed, annotations, accessible colors). For complex cases (multivariate, dashboards, interactive) → Study resources/methodology.md for advanced techniques (small multiples, layered charts, dashboard layout, interaction patterns).
Step 4: Narrate insights and actions
Lead with insight headline ("Revenue up 30% YoY driven by Enterprise segment"), annotate key patterns (arrows, labels, shading), provide context (vs benchmark, target, previous), interpret meaning ("Suggests product-market fit in Enterprise"), recommend actions ("Double down on Enterprise sales hiring"). See Narrative Framework.
Step 5: Validate and deliver
Self-assess using resources/evaluators/rubric_visualization_choice_reporting.json. Check: Does chart answer the question clearly? Are insights obvious at a glance? Are next actions clear? Create visualization-choice-reporting.md with question, data summary, visualization spec, narrative, and actions. See Delivery Format.
Question Types → Chart Families
| Question Type | Example | Primary Chart Families |
|---|---|---|
| Trend | How has X changed over time? | Line, area, sparkline, horizon |
| Comparison | How do categories compare? | Bar (horizontal for names), column, dot plot, slope chart |
| Distribution | What's the spread/frequency? | Histogram, box plot, violin, density plot |
| Relationship | How do X and Y relate? | Scatter, bubble, connected scatter, hexbin |
| Composition | What are the parts? | Treemap, pie/donut, stacked bar, waterfall, sankey |
| Geographic | Where is it happening? | Choropleth, bubble map, flow map, dot map |
| Hierarchical | What's the structure? | Tree, dendrogram, sunburst, circle packing |
| Multivariate | How do many variables interact? | Small multiples, parallel coordinates, heatmap, SPLOM |
Data Type → Encoding Considerations
| Question Type | Chart Types | When to Use |
|---|---|---|
| Comparison | Bar (horizontal), Column, Grouped bar, Dot plot, Slope chart | Categorical → Numerical. Horizontal bar for long names/ranking. Grouped for 2-3 metrics. Slope for before/after. |
| Trend | Line, Area, Sparkline, Step, Candlestick | Time → Numerical. Line for continuous trends. Area for cumulative/part-to-whole. Sparkline for inline. Step for discrete changes. |
| Distribution | Histogram, Box plot, Violin, Density plot | Numerical → Frequency. Histogram for shape/outliers. Box for quartiles across groups. Violin for full density. |
| Relationship | Scatter, Bubble, Hexbin, Connected scatter | Numerical X → Numerical Y. Scatter for correlation. Bubble for 3rd/4th variable (size/color). Hexbin for dense data. |
| Composition | Treemap, Pie/Donut, Stacked bar (100%), Waterfall, Sankey | Parts of whole. Treemap for hierarchy. Pie for 2-5 categories (part-to-whole key). Waterfall for cumulative. Sankey for flow. |
| Geographic | Choropleth, Bubble map, Flow map | Spatial patterns. Choropleth for regions. Bubble for precise locations. Flow for origin-destination. |
| Multivariate | Small multiples, Heatmap, Parallel coordinates | Many variables. Small multiples for consistent comparison. Heatmap for matrix (time×day). Parallel for dimensions. |
Essential Elements
Perceptual Best Practices
Declutter
Accessibility
Structure: Headline → Pattern → Context → Meaning → Action
1. Headline (one sentence, insight-first):
2. Pattern (what do you see?):
3. Context (compared to what?):
4. Meaning (why does it matter?):
5. Action (what should we do?):
Example Full Narrative:
Headline: Enterprise revenue up 120% YoY while SMB declined 10%, resulting in overall 30% growth.
Pattern: Revenue grew from $2M/month (Q1) to $2.6M (Q4). Enterprise segment contributed $1.5M in Q4 (up from $680K in Q1), while SMB dropped from $1.3M to $1.1M.
Context: Total revenue 15% above plan. Enterprise growth (120%) far exceeds industry average (25%). SMB churn rate doubled from 5% to 10% in Q3-Q4.
Meaning: Strong product-market fit in Enterprise; SMB pricing or feature set may be misaligned. Enterprise is now 58% of revenue vs 34% in Q1, reducing diversification.
Actions:
- Prioritize: Hire 2 Enterprise AEs for Q1, double down on Enterprise playbook
- Fix: Launch SMB annual plans (Q1) to reduce churn; interview churned SMB customers to identify gaps
- Monitor: Enterprise win rate, SMB churn by plan type, revenue concentration risk
Create visualization-choice-reporting.md with these sections:
1. Question: The question you're answering with data (e.g., "How has revenue trended over the past year?")
2. Data Summary: Source, time period, granularity, dimensions, size (e.g., "Analytics DB, Jan-Dec 2024, monthly, revenue by segment, 24 rows")
3. Visualization:
4. Narrative: (Headline → Pattern → Context → Meaning → Action structure from above)
5. Validation: Self-check with rubric (Clarity ✓, Accuracy ✓, Insight ✓, Actionability ✓, Accessibility ✓)
6. Appendix (optional): Raw data, alternatives considered, statistical tests, assumptions
See resources/template.md for full template with examples.
Chart Selection Errors
❌ Pie chart for >5 categories: Hard to compare angles accurately ✓ Use horizontal bar chart: Position on common scale is more accurate
❌ Line chart for categorical data: Implies continuity that doesn't exist (e.g., revenue by product) ✓ Use bar chart: Discrete categories
❌ 3D charts: Perspective distorts values, adds no information ✓ Use 2D with color/size: Clearer, more accurate
Design Mistakes
❌ Y-axis doesn't start at zero (bar chart): Exaggerates differences ✓ Start at zero for bar/column: Accurate visual proportion
❌ Dual Y-axes with different scales: Misleading correlations ✓ Use small multiples or index to 100: Compare shapes, not scales
❌ Rainbow color scheme: Not colorblind-safe, no perceptual ordering ✓ Sequential (light→dark) or diverging (blue→white→red) palette
Narrative Failures
❌ Title: "Revenue by Month": Descriptive, not insightful ✓ "Revenue up 30% YoY, driven by Enterprise": Insight-first
❌ No context: "Revenue is $2.6M" (vs what?) ✓ Add benchmark: "Revenue $2.6M, 15% above $2.25M target"
❌ Pattern without meaning: "Revenue increased" (so what?) ✓ Interpret: "Revenue up 30%, suggests Enterprise product-market fit, informs 2025 hiring plan"
❌ No actions: Ends with "interesting pattern" ✓ Recommend: "Hire 2 Enterprise AEs, investigate SMB churn"
Further reading:
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.