/data-viz-renderer
Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts,
$ npx -y skills add zebbern/claude-code-guide --skill data-viz-renderer --agent claude-codeHow it fires
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/data-viz-renderer
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Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts,
SKILL.md
data-viz-renderer.SKILL.mdname: data-viz-renderer
description: "Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts, or dashboards."
license: MIT
type: tool
tags: ["infographic", "visualization", "svg", "html", "chart", "dashboard"]
Data Viz Renderer
Generate self-contained HTML/SVG infographics from JSON data. Four supported types:
1. **Stats Cards** — KPI big numbers + trend arrows + icons 2. **Comparison Chart** — Grouped bar chart with multiple series 3. **Flow Diagram** — Step-by-step process with numbering, icons, and connecting arrows 4. **Dashboard** — Mixed layout: stat cards + bar chart + donut chart + flow
Output is a **fully self-contained** HTML file (all CSS/SVG inline, no external dependencies), ready to open directly in a browser.
Usage
Basic Usage
python3 scripts/build_infographic.py config.json
Also supports reading from stdin:
cat config.json | python3 scripts/build_infographic.py
The script outputs a JSON status to stdout and writes the generated HTML to the path specified in the `output` field.
JSON Configuration Format
Common fields:
| Field | Type | Required | Description | |-------|------|----------|-------------| | `title` | string | No | Infographic title | | `subtitle` | string | No | Subtitle | | `type` | string | Yes | `stats` / `comparison` / `flow` / `dashboard` | | `palette` | string | No | Color palette (default: `auto`) | | `data` | object/array | Yes | Data content (format depends on type) | | `output` | string | No | Output file path (default: `infographic.html`) | | `footer` | string | No | Footer text |
Color Palettes
Available values: `auto` (automatically chosen based on data), `ocean`, `sunset`, `forest`, `berry`, `vibrant`, `corporate`, `pastel`, `earth`
Data Format by Type
1. stats — Stat Cards
{
"type": "stats",
"data": [
{
"label": "Total Revenue",
"value": "$1.2M",
"icon": "money",
"trend": "+12.5%",
"trend_dir": "up"
},
{
"label": "Users",
"value": "45,230",
"icon": "users",
"trend": "+8.2%",
"trend_dir": "up"
}
]
}**icon** options: `users`, `user`, `money`, `percent`, `globe`, `clock`, `check`, `star`, `target`, `zap`, `chart-bar`, `chart-pie`, `database`, `rocket`, `shield`, `heart`, `light`, `search`, `mail`, `settings`, `flag`, `trending-up`, `trending-down`
**trend_dir**: `up` (green upward arrow) or `down` (red downward arrow)
2. comparison — Bar Chart Comparison
{
"type": "comparison",
"data": {
"chart_title": "Quarterly Revenue Comparison",
"categories": ["Q1", "Q2", "Q3", "Q4"],
"series": [
{"name": "2024", "values": [320, 410, 380, 520]},
{"name": "2025", "values": [380, 490, 450, 610]}
]
}
}3. flow — Flow Diagram
{
"type": "flow",
"data": [
{"step": 1, "title": "Requirements", "description": "Gather user needs", "icon": "search"},
{"step": 2, "title": "Design", "description": "Create technical plan", "icon": "light"},
{"step": 3, "title": "Development", "description": "Code and test", "icon": "settings"},
{"step": 4, "title": "Launch", "description": "Deploy to production", "icon": "rocket"}
]
}4. dashboard — Mixed Dashboard
{
"type": "dashboard",
"data": {
"stats": [
{"label": "DAU", "value": "12.3K", "icon": "users", "trend": "+5%", "trend_dir": "up"},
{"label": "Conversion Rate", "value": "3.8%", "icon": "target", "trend": "-0.2%", "trend_dir": "down"}
],
"chart": {
"chart_title": "Monthly Trend",
"categories": ["Jan", "Feb", "Mar", "Apr"],
"series": [{"name": "DAU", "values": [10200, 11500, 11800, 12300]}]
},
"breakdown": [
{"label": "iOS", "value": 45},
{"label": "Android", "value": 38},
{"label": "Web", "value": 17}
],
"flow": [
{"step": 1, "title": "Sign Up", "description": ""},
{"step": 2, "title": "Activate", "description": ""},
{"step": 3, "title": "Retain", "description": ""}
]
}
}Output Format
The script outputs a JSON result to stdout:
{
"status": "success",
"output": "/absolute/path/to/infographic.html",
"type": "stats",
"title": "My Infographic",
"palette": "auto",
"size_bytes": 8432
}On error:
{
"status": "error",
"errors": ["Missing required field: data"]
}Design Highlights
- **Zero external dependencies**: Pure Python standard library, no pip install needed
- **Self-contained output**: HTML with all CSS and SVG inline, no network required
- **Responsive layout**: Works on both desktop and mobile browsers
- **Professional palettes**: 8 preset color schemes + automatic selection
- **24+ built-in icons**: Common SVG icons, no font files needed
- **CJK-friendly**: Font stack includes Noto Sans SC, PingFang SC, Microsoft YaHei
Use Cases
- Visualization modules in data reports
- Product data dashboards
- Business process illustrations
- Quarterly/monthly data comparisons
- Team KPI displays
Dependencies
- Python 3.7+ (standard library only)
Read more
name: data-viz-renderer description: "Generate self-contained HTML/SVG infographics from JSON data, including stat cards, bar charts, flow diagrams, and mixed dashboards. Offers 8 color palettes and built-in icons with no external dependencies. Triggered when users request data visualization, infographics, charts, or dashboards." license: MIT type: tool tags: ["infographic", "visualization", "svg", "html", "chart", "dashboard"]
Data Viz Renderer
Generate self-contained HTML/SVG infographics from JSON data. Four supported types:
1. **Stats Cards** — KPI big numbers + trend arrows + icons 2. **Comparison Chart** — Grouped bar chart with multiple series 3. **Flow Diagram** — Step-by-step process with numbering, icons, and connecting arrows 4. **Dashboard** — Mixed layout: stat cards + bar chart + donut chart + flow
Output is a **fully self-contained** HTML file (all CSS/SVG inline, no external dependencies), ready to open directly in a browser.
Usage
Basic Usage
python3 scripts/build_infographic.py config.json
Also supports reading from stdin:
cat config.json | python3 scripts/build_infographic.py
The script outputs a JSON status to stdout and writes the generated HTML to the path specified in the `output` field.
JSON Configuration Format
Common fields:
| Field | Type | Required | Description | |-------|------|----------|-------------| | `title` | string | No | Infographic title | | `subtitle` | string | No | Subtitle | | `type` | string | Yes | `stats` / `comparison` / `flow` / `dashboard` | | `palette` | string | No | Color palette (default: `auto`) | | `data` | object/array | Yes | Data content (format depends on type) | | `output` | string | No | Output file path (default: `infographic.html`) | | `footer` | string | No | Footer text |
Color Palettes
Available values: `auto` (automatically chosen based on data), `ocean`, `sunset`, `forest`, `berry`, `vibrant`, `corporate`, `pastel`, `earth`
Data Format by Type
1. stats — Stat Cards
{
"type": "stats",
"data": [
{
"label": "Total Revenue",
"value": "$1.2M",
"icon": "money",
"trend": "+12.5%",
"trend_dir": "up"
},
{
"label": "Users",
"value": "45,230",
"icon": "users",
"trend": "+8.2%",
"trend_dir": "up"
}
]
}**icon** options: `users`, `user`, `money`, `percent`, `globe`, `clock`, `check`, `star`, `target`, `zap`, `chart-bar`, `chart-pie`, `database`, `rocket`, `shield`, `heart`, `light`, `search`, `mail`, `settings`, `flag`, `trending-up`, `trending-down`
**trend_dir**: `up` (green upward arrow) or `down` (red downward arrow)
2. comparison — Bar Chart Comparison
{
"type": "comparison",
"data": {
"chart_title": "Quarterly Revenue Comparison",
"categories": ["Q1", "Q2", "Q3", "Q4"],
"series": [
{"name": "2024", "values": [320, 410, 380, 520]},
{"name": "2025", "values": [380, 490, 450, 610]}
]
}
}3. flow — Flow Diagram
{
"type": "flow",
"data": [
{"step": 1, "title": "Requirements", "description": "Gather user needs", "icon": "search"},
{"step": 2, "title": "Design", "description": "Create technical plan", "icon": "light"},
{"step": 3, "title": "Development", "description": "Code and test", "icon": "settings"},
{"step": 4, "title": "Launch", "description": "Deploy to production", "icon": "rocket"}
]
}4. dashboard — Mixed Dashboard
{
"type": "dashboard",
"data": {
"stats": [
{"label": "DAU", "value": "12.3K", "icon": "users", "trend": "+5%", "trend_dir": "up"},
{"label": "Conversion Rate", "value": "3.8%", "icon": "target", "trend": "-0.2%", "trend_dir": "down"}
],
"chart": {
"chart_title": "Monthly Trend",
"categories": ["Jan", "Feb", "Mar", "Apr"],
"series": [{"name": "DAU", "values": [10200, 11500, 11800, 12300]}]
},
"breakdown": [
{"label": "iOS", "value": 45},
{"label": "Android", "value": 38},
{"label": "Web", "value": 17}
],
"flow": [
{"step": 1, "title": "Sign Up", "description": ""},
{"step": 2, "title": "Activate", "description": ""},
{"step": 3, "title": "Retain", "description": ""}
]
}
}Output Format
The script outputs a JSON result to stdout:
{
"status": "success",
"output": "/absolute/path/to/infographic.html",
"type": "stats",
"title": "My Infographic",
"palette": "auto",
"size_bytes": 8432
}On error:
{
"status": "error",
"errors": ["Missing required field: data"]
}Design Highlights
- **Zero external dependencies**: Pure Python standard library, no pip install needed
- **Self-contained output**: HTML with all CSS and SVG inline, no network required
- **Responsive layout**: Works on both desktop and mobile browsers
- **Professional palettes**: 8 preset color schemes + automatic selection
- **24+ built-in icons**: Common SVG icons, no font files needed
- **CJK-friendly**: Font stack includes Noto Sans SC, PingFang SC, Microsoft YaHei
Use Cases
- Visualization modules in data reports
- Product data dashboards
- Business process illustrations
- Quarterly/monthly data comparisons
- Team KPI displays
Dependencies
- Python 3.7+ (standard library only)
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Repo: zebbern/claude-code-guide
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