| name | visual-vocabulary |
| description | Chart catalog organized by communication goal, CSAR evaluation loop for AI-generated charts, 5-visual rule, override decision framework, and living gallery references |
| lastReviewed | 2026-05-02T00:00:00.000Z |
Visual Vocabulary
Pick the right chart for the story you want to tell, evaluate whether an AI-generated chart choice is correct, and know when to override.
This skill is the selection and evaluation layer. It delegates chart design (color, annotation, decluttering) to data-visualization and narrative arc (three-act structure, audience framing) to data-storytelling. Use this skill first to decide WHAT to show; use those skills to decide HOW to show it.
When to Use
- Choosing chart types for a dashboard, report, or data story
- Evaluating an AI-generated visualization (Copilot, ChatGPT, or any tool that auto-selects chart types)
- Building an SVG dashboard and deciding what each panel should contain
- Reviewing someone else's chart choices for story-intent alignment
Module 1: Chart Catalog by Communication Goal
Comparison
Show differences between items or groups.
| Chart | Best When | Avoid When |
|---|
| Horizontal bar | Ranking items; long category labels | More than 15 items (paginate or filter) |
| Grouped bar | Comparing 2-3 series across categories | More than 3 series (use small multiples) |
| Dot plot | Precise value comparison; tight ranges | Audience expects bars |
| Slope chart | Before/after comparison of ranked items | More than 10 items (too many crossing lines) |
| Radar | Multi-dimensional profile comparison | More than 7 axes; general audiences |
| Bullet chart | Actual vs. target with qualitative ranges | No clear target or benchmark |
Change Over Time
Show trends, seasonality, or evolution.
| Chart | Best When | Avoid When |
|---|
| Line | Continuous data; up to 5 series | Categorical time (use bar) |
| Area | Emphasizing volume or magnitude of change | Multiple overlapping series (use stacked) |
| Stacked area | Part-to-whole composition changing over time | Need to compare individual series precisely |
| Sparkline | Inline trend context (KPI cards, tables) | Trend shape matters less than precise values |
| Step line | Discrete changes (pricing, policy, thresholds) | Continuous gradual change |
| Small multiples | Comparing trends across 6-20 categories | Fewer than 4 categories (use single line chart) |
Proportion
Show part-to-whole relationships.
| Chart | Best When | Avoid When |
|---|
| Donut | 2-5 segments; one hero segment to highlight | More than 6 segments; comparing across groups |
| Stacked bar | Comparing composition across categories | More than 5 segments per bar |
| Waffle | Communicating percentages to general audiences | Precision matters (use table) |
| Treemap | Hierarchical part-to-whole with many items | Need to show change over time |
| Sunburst | Multi-level hierarchy exploration | Print or static context (needs interaction) |
| Waterfall | Showing additive/subtractive contributions | Non-sequential contributions |
Distribution
Show spread, shape, or outliers.
| Chart | Best When | Avoid When |
|---|
| Histogram | Single variable distribution shape | Comparing distributions (use violin or ridgeline) |
| Box plot | Comparing distributions across groups | General audiences (unfamiliar format) |
| Violin | Distribution shape comparison across groups | Fewer than 3 groups (use histogram) |
| Ridgeline | Many distributions stacked for pattern scanning | Precision on individual values |
| Scatter | Two-variable distribution and outlier detection | Categorical data |
| Beeswarm | Small dataset; every point matters | More than 500 points (use density) |
Relationship
Show correlation, causation, or connection.
| Chart | Best When | Avoid When |
|---|
| Scatter | Two continuous variables; outlier identification | Categorical variables |
| Bubble | Three variables (x, y, size) | More than 50 bubbles (overplotting) |
| Heatmap | Dense matrix relationships (correlation, time x category) | Fewer than 4x4 cells |
| Parallel coordinates | Multi-dimensional comparison (5+ variables) | General audiences |
| Network graph | Entity relationships, social connections | Hierarchical data (use tree) |
| Chord diagram | Bidirectional flows between categories | More than 10 categories |
Flow and Process
Show movement, conversion, or paths.
| Chart | Best When | Avoid When |
|---|
| Sankey | Multi-stage flow with branching paths | Fewer than 3 stages (use stacked bar) |
| Funnel | Sequential drop-off (conversion, pipeline) | No sequential order |
| Gantt | Timeline with parallel activities | More than 30 tasks (filter or paginate) |
| Swimlane | Process flow with role/team assignments | Simple linear process |
Deviation
Show variance from a reference point.
| Chart | Best When | Avoid When |
|---|
| Diverging bar | Above/below target or median | No clear reference point |
| Lollipop | Deviation from baseline; cleaner than bars | Audience expects standard bars |
| Line + reference | Trend deviation from target over time | Multiple baselines |
| Gauge | Single KPI vs. target (dashboards, KPI cards) | More than 3 gauges on a page |
Module 2: The CSAR Evaluation Loop
When an AI tool generates a chart, evaluate before accepting. Adapted from the Copilot Design Automation framework:
| Step | Question | Action |
|---|
| Clarify | "What question am I answering?" | Write the question as a sentence before looking at the chart |
| Summarize | "The AI chose a [chart type]. Does it answer the question?" | Name the chart type and check it against Module 1 |
| Act | Accept, modify, or override | Accept if story-intent matches. Override if wrong goal group. Modify if right type but wrong emphasis |
| Reflect | "Why did I accept/override? What principle guided me?" | Document the rationale; builds judgment over time |
Override Decision Table
| AI Chose | But Your Goal Is | Override To | Rationale |
|---|
| Pie (8 slices) | Compare items | Horizontal bar, sorted | Pie with 8+ slices is unreadable |
| Clustered bar | Show trend | Line chart | Time series needs continuity |
| 3D column | Anything | 2D bar or line | 3D adds no information, distorts perception |
| Stacked bar | Compare individual series | Grouped bar or small multiples | Stacking hides individual values |
| Donut | Show precise values | Table with conditional formatting | Donut communicates rough proportion only |
| Line (20 series) | Compare trends | Small multiples or highlight 3 key series | Too many lines become spaghetti |
| Map | Compare values | Bar chart sorted by value | Maps encode position; bars encode length (more precise) |
Module 3: The 5-Visual Rule
Executive dashboards that work follow this constraint:
No more than 5 visuals per page. Each visual answers a different question. If you need more, you need a second page, not a denser layout.
| Slot | Role | Typical Chart |
|---|
| 1-3 | KPI cards | Card, gauge, or sparkline |
| 4 | Hero chart | The main visual that carries the story |
| 5 | Supporting chart | A second angle on the same story |
A sixth visual is a table for drill-down, placed below the fold or on demand.
Composition by Audience
| Audience | Visuals | Time Budget | Design Priority |
|---|
| Executive | 3-5 | 30 seconds | KPIs first, hero chart, one action item |
| Manager | 5-8 | 2 minutes | Filters, comparison charts, trend lines |
| Analyst | 8-15 | Unlimited | Detail tables, drill-through, cross-filters |
| General | 3-4 | 1 minute | Annotated hero chart, simple narrative |
Module 4: SVG Dashboard Composition Patterns
Lessons from building production SVG dashboards:
Panel Primitive
Every card in an SVG dashboard uses one function:
panel({ x, y, w, h, title, subtitle, color })
Constants: radius 16, fill #0f172a (dark slate), stroke #334155, title at y+32. One primitive, every card looks the same. Visual uniformity builds trust.
Pie Chart Sizing
- Radius must fit within card boundaries with 40px margin on all sides
- Donut hole (inner radius) = 33% of outer radius for center labels
- Never more than 6 slices; group the rest as "Other"
- Center label for total count or primary metric
Bar Chart Spacing
- Row height: 32px per bar (readable text + bar + gap)
- Label width: 40% of card width; bar width: 50%; value: 10%
- Longest bar fills the available width; others scale proportionally
- Sort by value descending (unless time-ordered)
Color Palette (Dark Slate Theme)
| Use | Hex | Name |
|---|
| Card background | #0f172a | Slate 950 |
| Card stroke | #334155 | Slate 700 |
| Primary text | #ffffff | White |
| Secondary text | #cbd5e1 | Slate 300 |
| Muted text | #94a3b8 | Slate 400 |
| Tertiary text | #64748b | Slate 500 |
| Positive/complete | #22c55e | Green 500 |
| Warning/pending | #f59e0b | Amber 500 |
| Error/critical | #ef4444 | Red 500 |
| Info/accent | #3b82f6 | Blue 500 |
| Purple accent | #8b5cf6 | Violet 500 |
Anti-Patterns
| Anti-pattern | Fix |
|---|
| Pie arcs computed at wrong center coordinates | Calculate center from card y + card height, not absolute y |
| Incremental y-shifts corrupting multi-row layouts | Define each row's y from scratch (row N y = header + sum of previous row heights + gaps) |
| Labels bleeding outside card boundaries | Max y of any element = card y + card height - margin |
| Different font sizes per card | One title size (20px), one body size (13px), one muted size (11px) across all cards |
| Inline coordinate math | Define constants at the top: ROW_Y[], CARD_W, CARD_H, GAP |
Module 5: Living Gallery References
Use these galleries to browse real examples when Module 1's table isn't enough. The galleries are the living source of truth; this skill is the decision framework.
When to Consult a Gallery
- Module 1 suggests 2+ chart types and you cannot decide
- The story intent doesn't fit any standard goal group
- You need to see a real example of a chart type before committing
- Client or stakeholder needs visual evidence that the chart type works for their data
Module 6: Chart Selection Decision Tree
Run this algorithm when choosing charts for a dashboard or report:
1. Write the Big Idea sentence
(audience + action + evidence)
If you can't write it, the analysis isn't done.
2. List the 3-5 questions the dashboard must answer
Each question becomes one visual.
3. For each question:
a. Identify the communication goal (Module 1 header)
b. Check the chart catalog table
c. Filter by data shape and audience
d. If AI generated a chart, run CSAR (Module 2)
e. If still unsure, consult a gallery (Module 5)
4. Apply the 5-Visual Rule (Module 3)
Cut the weakest visual if over budget.
5. Arrange by narrative flow:
KPIs -> Hero chart -> Supporting -> Detail
(delegate to dashboard-design for layout patterns)
6. Design each chart:
(delegate to data-visualization for color,
annotation, decluttering)
Cross-References
data-visualization -- color theory, annotation patterns, decluttering rules. Use after this skill selects chart types.data-storytelling -- three-act narrative structure, audience-first framing, Big Idea worksheet. Use before this skill to define the story.dashboard-design -- layout patterns, KPI card design, filter architecture. Use after this skill to arrange the visuals.svg-dashboard-composition -- SVG composition mechanics, panel primitives, coordinate systems. Use when the output format is SVG.chart-interpretation -- reading and evaluating existing charts. The inverse of this skill.
