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Data Viz Renderer

CommunityPopular
zebbern
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, or dashboards.

Overview

Publisherzebbern
Repositoryclaude-code-guide
Skill namedata-viz-renderer
Stars
4.6K
Forks
464
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zebbern on GitHub. Read the source before you install it.

Installation

Install the Data Viz Renderer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zebbern/claude-code-guide.git /tmp/claude-code-guide
mkdir -p .claude/skills
cp -r /tmp/claude-code-guide/skills/data-viz-renderer .claude/skills/data-viz-renderer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Viz Renderer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Viz Renderer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Viz Renderer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

bash
python3 scripts/build_infographic.py config.json

Also supports reading from stdin:

bash
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:

FieldTypeRequiredDescription
titlestringNoInfographic title
subtitlestringNoSubtitle
typestringYesstats / comparison / flow / dashboard
palettestringNoColor palette (default: auto)
dataobject/arrayYesData content (format depends on type)
outputstringNoOutput file path (default: infographic.html)
footerstringNoFooter 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
json
{
  "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
json
{
  "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
json
{
  "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
json
{
  "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:

json
{
  "status": "success",
  "output": "/absolute/path/to/infographic.html",
  "type": "stats",
  "title": "My Infographic",
  "palette": "auto",
  "size_bytes": 8432
}

On error:

json
{
  "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)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Data Viz Renderer AI skill do?

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.

Why use Data Viz Renderer on TypingMind?

Because you install it once and use it with any model. Data Viz Renderer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Viz Renderer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zebbern/claude-code-guide/tree/main/skills/data-viz-renderer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Data Viz Renderer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Viz Renderer?

As many as you like. As long as a model supports skills, you can use Data Viz Renderer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Viz Renderer AI skill free?

Yes. It is published on GitHub by zebbern under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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