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1.6K downloads. Trust evidence available

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data-viz-wizard

Transform CSV data into stunning interactive chart visualizations with Chart.js Skill: data-viz-wizard Owner: voronindenis5 Summary: Transform CSV data into stunning interactive chart visualizations with Chart.js Tags: latest:0.1.0 Version history: v0.1.0 | 2026-08-14T06:18:18.732Z | auto - Initial release: Transform CSV data into interactive HTML chart visualizations with Chart.js. - Supports auto-detection of best chart type based on your data. - Generate single charts or multi-chart dashboard

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.6k

Updated

Oct 10, 2026

Version

0.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.6K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.6K downloadsadoption · observed Oct 10, 2026
Latest release: 0.1.0release · observed Aug 14, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17b6amkd3wzqgg640v03a9r1n83gxs1:data-viz-wizard

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-voronindenis5-data-viz-wizard/snapshot"

Documentation

CLAWHUB

14,866 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: data-viz-wizard
version: 1.0.0
author: Denis Voronin
license: MIT
description: Transform CSV data into stunning interactive chart visualizations with Chart.js
category: data-science
tags:
  - csv
  - charts
  - visualization
  - chartjs
  - dashboard
  - data-viz
---

# Data Viz Wizard

Transform any CSV file into stunning, interactive HTML chart visualizations powered by Chart.js.

## Quick Start

```bash
# Auto-detect best chart type
python scripts/viz_wizard.py chart sales.csv --type auto --output chart.html

# Generate a full dashboard with multiple charts
python scripts/viz_wizard.py dashboard data.csv --output dashboard.html

# Specify exact chart type and axes
python scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'

# Pipe data via stdin
cat data.csv | python scripts/viz_wizard.py --auto --output viz.html
```

## Commands

| Command | Description |
|---------|-------------|
| `chart <file>` | Generate a single chart (best auto-detected type) |
| `dashboard <file>` | Generate multi-chart dashboard from same dataset |
| `csv <file>` | Explicit chart with specified type and columns |
| _(stdin pipe)_ | `--auto` mode reads CSV from stdin |

## Flags

| Flag | Description |
|------|-------------|
| `--type` | Chart type: `auto`, `line`, `bar`, `stacked`, `area`, `scatter`, `pie`, `donut`, `radar`, `heatmap-grid` |
| `--x` | Column name for X-axis |
| `--y` | Column name(s) for Y-axis (comma-separated) |
| `--output` / `-o` | Output HTML file path |
| `--title` | Chart title |
| `--palette` | Color palette: `viridis`, `sunset`, `ocean`, `monochrome`, `neon` |
| `--trend` | Add trend line |
| `--moving-average` / `-ma` | Window size for moving average |
| `--theme` | `dark`, `light`, or `auto` (toggleable) |
| `--auto` | Full auto-mode (stdin) |

## Column Auto-Detection

The wizard auto-detects column types:
- **Date**: ISO dates, `YYYY-MM-DD`, `MM/DD/YYYY`, etc.
- **Numeric**: integers, floats, currency
- **Percentage**: values with `%` suffix
- **Categorical**: strings, low-cardinality text

## Output Features

Every generated HTML includes:
- Smooth Chart.js animations
- Professional color palettes
- Dark/light theme toggle ( persisted)
- Download chart as PNG button
- Responsive resize
- Smart tooltips (currency, percentage, date formatting)

## References

- [Chart Selection Guide](references/chart-selection.md) — Decision tree for choosing the right chart type
- [Color Palettes](references/palettes.md) — Palette definitions and when to use each

README.md

# 📊 Data Viz Wizard

> Transform any CSV into stunning interactive charts — instantly.

Data Viz Wizard reads your CSV data and generates **complete standalone HTML files** with beautiful, interactive Chart.js visualizations. No dependencies, no build step — just open the HTML in any browser.

## ✨ Features

- **Auto-detect column types**: dates, numbers, percentages, categories
- **Smart chart selection**: picks the best chart type for your data automatically
- **10 chart types**: line, bar, stacked bar, area, scatter, pie, donut, radar, heatmap-grid
- **Multi-chart dashboards**: generate an entire dashboard from one CSV
- **5 professional palettes**: viridis, sunset, ocean, monochrome, neon
- **Trend lines & moving averages**: built-in analytical overlays
- **Theme toggle**: dark/light mode with persistence
- **PNG export**: download any chart as an image
- **Responsive**: works on desktop and mobile
- **Zero dependencies**: pure Python stdlib, no pip install needed

## 🚀 Quick Start

```bash
# Auto-detect the best chart for your data
python scripts/viz_wizard.py chart sales.csv --type auto --output chart.html

# Generate a full dashboard
python scripts/viz_wizard.py dashboard data.csv --output dashboard.html

# Explicit chart with custom axes
python scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'

# Pipe data through stdin
cat data.csv | python scripts/viz_wizard.py --auto --output viz.html
```

Open the generated HTML file in any browser. That's it.

## 📋 Commands

| Command | Description |
|---------|-------------|
| `chart <file>` | Single chart with auto type detection |
| `dashboard <file>` | Multi-chart dashboard from one dataset |
| `csv <file>` | Explicit type + column specification |
| _(pipe)_ | `--auto` mode for stdin input |

## 🎨 Color Palettes

| Palette | Best For |
|---------|----------|
| `viridis` | Scientific/data — perceptually uniform |
| `sunset` | Warm, energetic — marketing dashboards |
| `ocean` | Cool, calm — financial/business reports |
| `monochrome` | Clean, minimal — print-friendly |
| `neon` | Vibrant, bold — presentations |

## 📁 Structure

```
data-viz-wizard/
├── SKILL.md                     # Skill definition
├── scripts/
│   └── viz_wizard.py            # Main script (Python stdlib only)
├── references/
│   ├── chart-selection.md       # Chart type decision tree
│   └── palettes.md              # Color palette guide
├── examples/
│   ├── sales.csv                # Sample sales data
│   └── metrics.csv              # Sample metrics data
├── README.md
└── LICENSE
```

## 📝 License

MIT © Denis Voronin

_meta.json

{
  "ownerId": "kn75wwn4x6djaf28jbykeamazd81gtdp",
  "slug": "data-viz-wizard",
  "version": "0.1.0",
  "publishedAt": 1786688298732
}

references/chart-selection.md

# Chart Selection Guide

Decision tree for choosing the right chart type for your data.

## Decision Tree

```
START: What columns do you have?

├── Date + Numeric
│   ├── 1 numeric series → LINE chart
│   ├── 2-3 numeric series → LINE chart (multi-line)
│   ├── Many series over time → AREA chart (stacked)
│   └── Single value over time + volume → AREA chart
│
├── Categorical + Numeric
│   ├── ≤ 8 categories, 1 metric → PIE or DONUT chart
│   ├── ≤ 8 categories, 2+ metrics → STACKED BAR
│   ├── Many categories → BAR chart (horizontal if labels are long)
│   └── Categories need ranking → BAR chart (sorted)
│
├── Numeric + Numeric
│   ├── Looking for correlation → SCATTER chart
│   └── Time-ordered pairs → SCATTER with trend line
│
├── Multiple Metrics (3-8, same scale)
│   ├── Comparing across categories → RADAR chart
│   └── Time series comparison → LINE chart
│
├── Two Categorical + Numeric
│   └── → HEATMAP-GRID (matrix view)
│
└── Percentage data
    ├── Parts of a whole → PIE or DONUT
    └── Over time → LINE chart (with % formatting)
```

## Chart Types Reference

| Chart Type | Best For | Data Shape |
|-----------|----------|------------|
| **Line** | Trends over time | Date × Numeric |
| **Bar** | Category comparison | Category × Numeric |
| **Stacked Bar** | Part-to-whole across categories | Category × Multiple Numerics |
| **Area** | Cumulative trends | Date × Numeric |
| **Scatter** | Correlation analysis | Numeric × Numeric |
| **Pie** | Simple proportions (≤8 slices) | Category × Single Metric |
| **Donut** | Cleaner proportions | Category × Single Metric |
| **Radar** | Multi-dimensional comparison | 3-8 metrics × Categories |
| **Heatmap Grid** | Matrix density/intensity | 2 Categories × Numeric |

## Auto-Detection Logic

The `--type auto` flag uses this priority:

1. **Date column present?** → Line chart (best for time series)
2. **Categorical + 1 numeric, ≤8 categories?** → Pie/Donut
3. **Categorical + numerics?** → Bar chart
4. **2+ numerics, no date?** → Scatter
5. **4+ numerics, no date?** → Radar
6. **Fallback** → Bar chart

## When to Override Auto

- **Stacked bar**: use when you want to show composition across groups
- **Area**: use for cumulative or volume data
- **Radar**: use when comparing 3-8 metrics on a similar scale
- **Heatmap**: use when you have two categorical dimensions and want intensity

references/palettes.md

# Color Palette Guide

## Available Palettes

### 🟣 Viridis (Default)
```
#440154 → #482878 → #3E4989 → #31688E → #26828E → #1F9E89 → #35B779 → #6DCD59 → #B4DE2C → #FDE725
```
- **Perceptually uniform** — equal steps in color = equal steps in data
- **Colorblind safe** — works for all types of color vision
- **Best for**: Scientific data, heatmaps, sequential data, anything where accurate perception matters
- **Use when**: Data accuracy is paramount, professional/scientific context

### 🌅 Sunset
```
#3C1C2D → #6B2737 → #A0333F → #D44E50 → #F2784B → #F8A358 → #FBC96D → #F7F7B7 → #D9F0A3 → #A1DAB4
```
- Warm gradient from deep purple to soft green
- **Best for**: Marketing dashboards, growth metrics, energy/enthusiasm
- **Use when**: You want emotional warmth, storytelling data, growth narratives

### 🌊 Ocean
```
#011A3A → #013A63 → #0353A4 → #0AA6C2 → #2EC4B6 → #5BC0BE → #6FFFE9 → #5390D9 → #48BFE3 → #56CFE1
```
- Cool blues and teals
- **Best for**: Financial reports, corporate dashboards, calm/professional tone
- **Use when**: Business/corporate context, trust/reliability themes, financial data

### ⬛ Monochrome
```
#1a1a2e → #16213e → #1e2a45 → #2d3561 → #3a4373 → #4a5a8a → #5e72a4 → #7488b8 → #8da0cc → #a8b8e0
```
- Subtle grayscale-to-blue gradient
- **Best for**: Print-friendly reports, minimal design, executive summaries
- **Use when**: Data should speak for itself, print/PDF export, accessibility

### 💜 Neon
```
#FF006E → #FB5607 → #FFBE0B → #8338EC → #3A86FF → #06FFA5 → #00F5D4 → #FF4081 → #7B2FF7 → #F72585
```
- Vibrant, high-contrast, attention-grabbing
- **Best for**: Presentations, social media sharing, dashboards that need to pop
- **Use when**: Engagement matters, presentations, younger audiences, bold statements

## How to Choose

| Context | Recommended Palette |
|---------|-------------------|
| Scientific / Academic | Viridis |
| Business / Finance | Ocean |
| Marketing / Growth | Sunset |
| Executive / Print | Monochrome |
| Presentation / Viral | Neon |
| Heatmap | Viridis |
| Pie/Donut | Neon or Sunset |
| Line Chart (multi-series) | Ocean or Viridis |
| Dashboard | Ocean (professional) or Neon (engagement) |

## Usage

```bash
--palette viridis     # default
--palette sunset
--palette ocean
--palette monochrome
--palette neon
```

## Color Application Rules

1. **Sequential data** (time series, rankings): Use palettes in order (viridis, ocean)
2. **Categorical data** (pie, donut): High contrast palettes work best (neon, sunset)
3. **Multi-series**: Assign colors cyclically from the palette
4. **Single series**: First color of the palette
5. **Transparency**: Area fills use 20% opacity, bars use 80%, points use full opacity

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