mviz（mviz 搜索）

mviz v1.6.7

mviz

Generate clean, data-focused charts and dashboards from compact JSON specs or markdown. Maximizes data-ink ratio with minimal chartjunk, gridlines, and decorative elements. Uses a 16-column grid layout system.

Setup

No installation required. Use npx -y -q mviz which auto-downloads from npm. The -q flag reduces npm output while still showing lint errors.

For faster repeated use, install globally: npm install -g mviz

What This Skill Does

Converts minimal JSON specifications into standalone HTML visualizations using ECharts. Instead of writing 50-100 lines of chart code, write a compact spec that gets expanded into a full HTML artifact with professional styling.

Visual Style (mdsinabox theme)

• Font: Helvetica Neue, Arial (clean sans-serif)
• Signature: Orange accent line at top of dashboards
• Palette: Blue primary, orange secondary, semantic colors (green=positive, amber=warning, red=error)
• Background: Paper (#f8f8f8 light) / Dark (#231f20 dark)
• Principles: High data-ink ratio, no chartjunk, minimal gridlines, data speaks for itself

How to Use

Single Chart (JSON)

echo '<json_spec>' | npx -y -q mviz -o chart.html

Dashboard from Markdown

npx -y -q mviz dashboard.md -o dashboard.html

Dashboard from Folder

npx -y -q mviz my-dashboard/ -o dashboard.html

16-Column Grid System

Components are sized using size=[cols,rows] syntax:

{"value": 1250000, "label": "Revenue", "format": "currency0m"}

{"title": "Sales", "x": "month", "y": "sales", "file": "data/sales.json"}

• 16 columns total width (both portrait and landscape)
• Row height: ~32px per row unit (approximate - charts have padding)
• Page capacity: Portrait [16c × 30r], Landscape [16c × 22r]
• Components on same line share the row
• Empty line = new row

Height Guidelines:

For charts with many categories (10+ bars, 10+ rows in dumbbell), increase row units to prevent compression.

Side-by-Side Layout

Critical: To place components side-by-side, their code blocks must have NO blank lines between them:

{"title": "Chart A", ...}

{"title": "Chart B", ...}

This renders Chart A and Chart B on the same row. Adding a blank line between them would put them on separate rows.

Headings and Section Breaks

Heading Guidelines:

• Set the page title with either the frontmatter title: *or* a leading # H1, not both.
• Use ## H2 for section titles within a page (most common).
• Use ### H3 for lightweight subheadings that don't interrupt flow.

Section vs Page Breaks:

• Use --- to separate logical sections visually. Content flows naturally to the next page when needed.
• Use === only when you explicitly want to force a new page (e.g., separating chapters or major report sections for PDF output).
• Never use === by default. Only add page breaks when the user specifically requests them.

Default Sizes

Recommended Size Pairings

Example: Dense KPI Row

{"value": 1250000, "label": "Revenue", "format": "currency0m"}

{"value": 8450, "label": "Orders", "format": "num0k"}

{"value": 2400000000, "label": "Queries", "format": "num0b"}

{"value": 0.15, "label": "MoM", "format": "pct0"}

{"value": 0.08, "label": "vs Target", "format": "pct0"}

This creates a row with 5 KPIs (3+3+3+3+4 = 16 columns).

Example: Two Charts Side by Side

Supported Types

Charts: bar, line, area, pie, scatter, bubble, boxplot, histogram, waterfall, xmr, sankey, funnel, heatmap, calendar, sparkline, combo, dumbbell, mermaid

UI Components: big_value, delta, alert, note, text, textarea, empty_space, table

Table Formatting

Tables support column-level and cell-level formatting:

Column options: bold, italic, type ("sparkline" or "heatmap")

{
  "type": "table",
  "columns": [
    {"id": "product", "title": "Product", "bold": true},
    {"id": "category", "title": "Category", "italic": true},
    {"id": "sales", "title": "Sales", "fmt": "currency"},
    {"id": "margin", "title": "Margin", "type": "heatmap", "fmt": "pct"},
    {"id": "trend", "title": "Trend", "type": "sparkline", "sparkType": "line"}
  ],
  "data": [
    {"product": "Widget", "category": "Electronics", "sales": 125000, "margin": 0.85, "trend": [85, 92, 88, 95, 102, 125]}
  ]
}

Cell-level overrides: Use {"value": "text", "bold": true} to override column defaults.

Heatmap: Applies color gradient from low to high values. Text auto-switches to white on dark backgrounds.

Sparkline types: line, bar, area, pct_bar (progress bar), dumbbell (before/after comparison)

Sort & filter:

Example: {"type": "table", "filter": true, "columns": [...], "data": [...]}

Note Types

Notes support three severity levels via noteType:

Notes also support an optional label for bold prefix text:

{"type": "note", "label": "Pro Tip:", "content": "Use keyboard shortcuts for faster navigation.", "noteType": "tip"}

Specialized Chart Examples

big_value - Hero metrics with large display:

{"type": "big_value", "value": 1250000, "label": "Revenue", "format": "currency0m"}

• Optional comparison object: {"value": 10300, "format": "currency", "label": "vs last month"} shows change with arrow

dumbbell - Before/after comparisons with directional coloring:

{
  "type": "dumbbell",
  "title": "ELO Changes",
  "category": "team",
  "start": "before",
  "end": "after",
  "startLabel": "Week 1",
  "endLabel": "Week 2",
  "higherIsBetter": true,
  "data": [
    {"team": "Chiefs", "before": 1650, "after": 1720},
    {"team": "Bills", "before": 1600, "after": 1550}
  ]
}

• Green = improvement, Red = decline, Grey = no change
• higherIsBetter: false for rankings (lower = better)
• Labels auto-abbreviate large numbers (7450 → "7k")

delta - Change metrics with directional coloring:

{"type": "delta", "value": 0.15, "label": "MoM Growth", "format": "pct0"}

• Positive values show green with ▲, negative show red with ▼
• Optional comparison object: {"value": 0.05, "label": "vs Target"}

area - Filled line chart for cumulative/volume data:

{
  "type": "area",
  "title": "Daily Active Users",
  "x": "date",
  "y": "users",
  "data": [{"date": "Mon", "users": 1200}, {"date": "Tue", "users": 1450}]
}

combo - Bar + line with dual Y-axis:

{
  "type": "combo",
  "title": "Revenue vs Growth Rate",
  "x": "quarter",
  "y": ["revenue", "growth_rate"],
  "data": [
    {"quarter": "Q1", "revenue": 1000000, "growth_rate": 0.15},
    {"quarter": "Q2", "revenue": 1200000, "growth_rate": 0.20}
  ]
}

• First y-field renders as bars, second as line
• Dual Y-axes with independent scales

heatmap - 2D matrix visualization:

{
  "type": "heatmap",
  "title": "Activity by Hour",
  "xCategories": ["Mon", "Tue", "Wed", "Thu", "Fri"],
  "yCategories": ["9am", "12pm", "3pm", "6pm"],
  "format": "num0",
  "data": [[0, 0, 85], [1, 0, 90], [2, 0, 72]]
}

• format option applies to cell labels (e.g., num0k, currency0k, pct)

funnel - Conversion or elimination flows:

{
  "type": "funnel",
  "title": "Sales Pipeline",
  "format": "num0",
  "data": [
    {"stage": "Leads", "value": 1000},
    {"stage": "Qualified", "value": 600},
    {"stage": "Proposal", "value": 300},
    {"stage": "Closed", "value": 100}
  ]
}

• format option applies to labels/tooltips (e.g., currency_auto, pct, num0)

waterfall - Cumulative change visualization:

{
  "type": "waterfall",
  "title": "Revenue Bridge",
  "x": "item",
  "y": "value",
  "data": [
    {"item": "Start", "value": 1000, "isTotal": true},
    {"item": "Growth", "value": 200},
    {"item": "Churn", "value": -50},
    {"item": "End", "value": 1150, "isTotal": true}
  ]
}

bubble - Scatter with size dimension. Supports series for color grouping and showLabels for persistent labels:

{
  "type": "bubble",
  "title": "Market Analysis",
  "x": "growth",
  "y": "profit",
  "size": "revenue",
  "series": "region",
  "label": "company",
  "data": [
    {"growth": 5, "profit": 20, "revenue": 100, "region": "US", "company": "Acme"},
    {"growth": 10, "profit": 15, "revenue": 200, "region": "EU", "company": "Beta"}
  ]
}

sankey - Flow diagrams showing relationships:

{
  "type": "sankey",
  "title": "Traffic Sources",
  "data": [
    {"source": "Organic", "target": "Landing", "value": 500},
    {"source": "Paid", "target": "Landing", "value": 300},
    {"source": "Landing", "target": "Signup", "value": 400}
  ]
}

mermaid - Diagrams from Mermaid syntax (flowcharts, sequence, state, class, ER). Use array for multi-line code:

{
  "type": "mermaid",
  "title": "User Flow",
  "code": [
    "graph TD",
    "  A[Start] --> B{Decision}",
    "  B -->|Yes| C[Action]",
    "  B -->|No| D[End]"
  ]
}

mermaid (ASCII) - ASCII/Unicode text-based diagrams (set ascii: true):

{
  "type": "mermaid",
  "title": "Process Flow",
  "code": ["graph LR", "  A[Input] --> B[Process] --> C[Output]"],
  "ascii": true
}

Mermaid lint rules (errors that will fail validation):

• No <br/> tags in labels (render as literal text, not line breaks)
• No quoted labels like A["text"] in flowcharts (quotes appear in output)

Number Format Options

Percentage formats: pct, pct0, pct1 always multiply by 100 for scalar components (big_value, delta) — pass 0.15 for 15%. Charts and tables are series-aware: they inspect the column/series and skip the multiply when any value exceeds 1, so [15, 22, 18] renders as 15.0%, 22.0%, 18.0% (not 1500%+) while [0.15, 0.22, 0.18] still renders as 15.0%, 22.0%, 18.0%. Pass [0.8, 1.2] for a growth-rate series and it'll render as 80%, 120%. If big_value / delta get a pct value > 1, the linter warns "did you mean value/100?".

Smart formatting (auto/currency_auto) is recommended. The format option applies to both axis labels and data labels on bar charts. It automatically picks the right suffix (k, m, b) based on magnitude and always shows 4 significant digits. Negative values are wrapped in parentheses: (1.000m).

When no format is specified, smart formatting is used by default.

Auto-Detected Axis Formatting

Chart axes automatically detect the appropriate format based on field names:

Override with an explicit format field in the chart spec.

Columnar Data Format

The chart generator auto-detects columnar query results. Instead of manually converting columns/rows to data, pass the result directly:

{
  "type": "bar",
  "title": "Sales by Region",
  "x": "region",
  "y": "sales",
  "columns": ["region", "sales"],
  "rows": [["North", 45000], ["South", 32000], ["East", 28000]]
}

This is automatically converted internally. No manual JSON reconstruction needed.

Axis Bounds (yMin/yMax)

For line, area, bar, and combo charts, control y-axis range with yMin and yMax:

{
  "type": "line",
  "title": "Elo Rating Trend",
  "x": "date",
  "y": "elo",
  "yMin": 1400,
  "data": [{"date": "Oct", "elo": 1511}, {"date": "Jan", "elo": 1636}]
}

Use yMin when:

• Data doesn't start at 0 (ratings, stock prices, temperatures)
• You want to emphasize relative changes over absolute values

Use yMax when:

• Labels are being cut off at the top of the chart
• You need headroom above the highest data point

Validation & Lint Rules

The CLI validates specs automatically using built-in lint rules. Use --lint flag for validation-only mode:

npx -y -q mviz --lint dashboard.md  # Validate without generating HTML

Lint Rules

Errors exit with code 1. Warnings log to stderr but don't fail.

Common Fixes

Time series error: Sort your data by date before passing to the chart.

Sankey wrong keys: Use source, target, value in your data:

{"source": "A", "target": "B", "value": 100}

big_value string: Pass numeric value with format option:

{"type": "big_value", "value": 0.625, "format": "pct0", "label": "Rate"}

Troubleshooting

Warning Messages

The generator outputs helpful warnings to stderr when issues are detected:

Warnings include context like content previews, similar type suggestions, and section/row info.

Labels Cut Off at Chart Edges

If data labels on bar, line, or area charts are being cut off at the top:

1. Find the maximum value in your data
2. Set yMax to ~10-15% higher than that value

Example: If max value is 200, set "yMax": 220

{
  "type": "bar",
  "title": "Sales",
  "x": "month",
  "y": "sales",
  "yMax": 250,
  "data": [{"month": "Jan", "sales": 180}, {"month": "Feb", "sales": 220}]
}

This provides headroom for the label text above the bars.

Data Generation Best Practice

Use SQL to generate data files instead of manually authoring JSON. This reduces errors and ensures data accuracy:

-- Generate chart data file
COPY (
  SELECT month, SUM(sales) as sales, SUM(revenue) as revenue
  FROM orders
  GROUP BY month
  ORDER BY month
) TO 'data/monthly-sales.json' (FORMAT JSON, ARRAY true);

Then reference the generated file:

{"title": "Monthly Sales", "x": "month", "y": "sales"}

This approach:

• Ensures data accuracy (no manual transcription errors)
• Keeps data in sync with source systems
• Reduces token usage (SQL is more compact than JSON arrays)
• Makes updates easy (re-run query to refresh)

File References (JSON and CSV)

Reference external data files to save tokens and enable data/visualization separation:

JSON Files

CSV Files (DuckDB Workflow)

CSV files work great with DuckDB for data exploration:

# Export query results to CSV
duckdb -csv -c "SELECT quarter, revenue FROM sales" > data/quarterly.csv

{"title": "Quarterly Revenue", "x": "quarter", "y": "revenue"}

• CSV provides data, inline JSON provides chart options (title, x, y, format)
• Auto-detection: If no inline options, first column = x, second column = y
• Type conversion: Numeric strings auto-convert to int/float

Benefits of File References

Dashboard Markdown Format

---
theme: light
title: My Dashboard
---

# Page Title

## Section Name

{"value": 125000, "label": "Revenue", "format": "currency0k"}

Rules:

• # Title sets the page title (first occurrence only)
• ## Section creates a new section with divider (border, spacing)
• ### Header creates a soft header within the current section (no divider)
• --- creates a section break (untitled, visual divider only)
• === creates a page break (forces new page when printing to PDF)
• size=[cols,rows] controls layout (16-column grid)
• size=auto auto-calculates size from data
• file=path references external JSON
• Empty lines = new rows

Theme Toggle

Dashboards include a theme toggle button (top right) that switches between light and dark modes. All charts dynamically update when the theme changes.

Set the default theme in frontmatter:

---
title: My Dashboard
theme: dark
orientation: landscape
print: true
---

Page capacity: Portrait fits 30 row units, landscape fits 22 row units (Letter paper, 0.5" margins).

The theme toggle affects all charts globally - individual chart theme settings are ignored in favor of the global toggle.

Custom Themes

Load custom brand colors and fonts from a YAML file:

npx -y -q mviz --theme my_theme.yaml dashboard.md -o dashboard.html

Example theme file:

name: brand-colors
extends: light

colors:
  primary: "#1a73e8"
  secondary: "#ea4335"

palette:
  - "#1a73e8"
  - "#ea4335"
  - "#fbbc04"

fonts:
  family: "'Roboto', sans-serif"
  import: "https://fonts.googleapis.com/css2?family=Roboto&display=swap"

Custom themes merge with defaults - only specify what you want to override.

Print and PDF Support

Charts are optimized for printing to PDF:

• High-Quality Rendering: Uses SVG renderer for crisp vector graphics at any zoom level
• No Page Breaks: CSS prevents charts and tables from being split across pages
• All Labels Visible: Category labels always shown with 45° rotation to fit

When printing dashboards to PDF, all content stays intact without being cut off mid-chart.

JSON Formatting for Editability

Use formatted (multi-line) JSON when data may need editing. This enables smaller, more precise edits:

{ "title": "Monthly Sales", "x": "month", "y": "sales", "data": [ {"month": "Jan", "sales": 120}, {"month": "Feb", "sales": 150}, {"month": "Mar", "sales": 180} ] }

Benefits:

• Each data point on its own line enables targeted edits
• Changing one value: ~30 chars vs ~200+ chars with compact JSON
• Easier to review diffs in version control

When to use compact JSON:

• Very small specs (< 100 chars)
• Data that won't change
• Single-line values like {"value": 1250000, "label": "Revenue"}

JSON Schema

mviz specs can be validated using the JSON Schema at:

https://raw.githubusercontent.com/matsonj/mviz/main/schema/mviz.schema.json

Add $schema to enable editor autocomplete and validation:

{
  "$schema": "https://raw.githubusercontent.com/matsonj/mviz/main/schema/mviz.schema.json",
  "type": "bar",
  "title": "Sales",
  ...
}

Color Palette (mdsinabox theme)

See reference/chart-types.md for complete documentation.

Your Role

You are an analytics assistant helping a human who has decision-making context that you lack. Your job is to present data clearly and surface patterns worth investigating—not to draw conclusions or make recommendations.

Key principles:

• Use a matter-of-fact tone. State what the data shows, not what it means.
• Design analysis that invites further questions, not analysis that closes them.
• Surface anomalies and patterns without assuming their cause or significance.
• Let the human add context and make decisions.

For additional guidance on creating effective data visualizations—including Tufte-inspired principles, anti-patterns to avoid, and layout examples—see Best_practices.md.

Feedback

Having issues with mviz? Ask Claude to create a friction log documenting the problem, then open it as an issue at https://github.com/matsonj/mviz/issues