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csv\n  - charts\n  - visualization\n  - chartjs\n  - dashboard\n  - data-viz\n---\n\n# Data Viz Wizard\n\nTransform any CSV file into stunning, interactive HTML chart visualizations powered by Chart.js.\n\n## Quick Start\n\n```bash\n# Auto-detect best chart type\npython scripts/viz_wizard.py chart sales.csv --type auto --output chart.html\n\n# Generate a full dashboard with multiple charts\npython scripts/viz_wizard.py dashboard data.csv --output dashboard.html\n\n# Specify exact chart type and axes\npython scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'\n\n# Pipe data via stdin\ncat data.csv | python scripts/viz_wizard.py --auto --output viz.html\n```\n\n## Commands\n\n| Command | Description |\n|---------|-------------|\n| `chart <file>` | Generate a single chart (best auto-detected type) |\n| `dashboard <file>` | Generate multi-chart dashboard from same dataset |\n| `csv <file>` | Explicit chart with specified type and columns |\n| _(stdin pipe)_ | `--auto` mode reads CSV from stdin |\n\n## Flags\n\n| Flag | Description |\n|------|-------------|\n| `--type` | Chart type: `auto`, `line`, `bar`, `stacked`, `area`, `scatter`, `pie`, `donut`, `radar`, `heatmap-grid` |\n| `--x` | Column name for X-axis |\n| `--y` | Column name(s) for Y-axis (comma-separated) |\n| `--output` / `-o` | Output HTML file path |\n| `--title` | Chart title |\n| `--palette` | Color palette: `viridis`, `sunset`, `ocean`, `monochrome`, `neon` |\n| `--trend` | Add trend line |\n| `--moving-average` / `-ma` | Window size for moving average |\n| `--theme` | `dark`, `light`, or `auto` (toggleable) |\n| `--auto` | Full auto-mode (stdin) |\n\n## Column Auto-Detection\n\nThe wizard auto-detects column types:\n- **Date**: ISO dates, `YYYY-MM-DD`, `MM/DD/YYYY`, etc.\n- **Numeric**: integers, floats, currency\n- **Percentage**: values with `%` suffix\n- **Categorical**: strings, low-cardinality text\n\n## Output Features\n\nEvery generated HTML includes:\n- Smooth Chart.js animations\n- Professional color palettes\n- Dark/light theme toggle ( persisted)\n- Download chart as PNG button\n- Responsive resize\n- Smart tooltips (currency, percentage, date formatting)\n\n## References\n\n- [Chart Selection Guide](references/chart-selection.md) — Decision tree for choosing the right chart type\n- [Color Palettes](references/palettes.md) — Palette definitions and when to use each\n\nFile v0.1.0:README.md\n\n# 📊 Data Viz Wizard\n\n> Transform any CSV into stunning interactive charts — instantly.\n\nData 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.\n\n## ✨ Features\n\n- **Auto-detect column types**: dates, numbers, percentages, categories\n- **Smart chart selection**: picks the best chart type for your data automatically\n- **10 chart types**: line, bar, stacked bar, area, scatter, pie, donut, radar, heatmap-grid\n- **Multi-chart dashboards**: generate an entire dashboard from one CSV\n- **5 professional palettes**: viridis, sunset, ocean, monochrome, neon\n- **Trend lines & moving averages**: built-in analytical overlays\n- **Theme toggle**: dark/light mode with persistence\n- **PNG export**: download any chart as an image\n- **Responsive**: works on desktop and mobile\n- **Zero dependencies**: pure Python stdlib, no pip install needed\n\n## 🚀 Quick Start\n\n```bash\n# Auto-detect the best chart for your data\npython scripts/viz_wizard.py chart sales.csv --type auto --output chart.html\n\n# Generate a full dashboard\npython scripts/viz_wizard.py dashboard data.csv --output dashboard.html\n\n# Explicit chart with custom axes\npython scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'\n\n# Pipe data through stdin\ncat data.csv | python scripts/viz_wizard.py --auto --output viz.html\n```\n\nOpen the generated HTML file in any browser. That's it.\n\n## 📋 Commands\n\n| Command | Description |\n|---------|-------------|\n| `chart <file>` | Single chart with auto type detection |\n| `dashboard <file>` | Multi-chart dashboard from one dataset |\n| `csv <file>` | Explicit type + column specification |\n| _(pipe)_ | `--auto` mode for stdin input |\n\n## 🎨 Color Palettes\n\n| Palette | Best For |\n|---------|----------|\n| `viridis` | Scientific/data — perceptually uniform |\n| `sunset` | Warm, energetic — marketing dashboards |\n| `ocean` | Cool, calm — financial/business reports |\n| `monochrome` | Clean, minimal — print-friendly |\n| `neon` | Vibrant, bold — presentations |\n\n## 📁 Structure\n\n```\ndata-viz-wizard/\n├── SKILL.md                     # Skill definition\n├── scripts/\n│   └── viz_wizard.py            # Main script (Python stdlib only)\n├── references/\n│   ├── chart-selection.md       # Chart type decision tree\n│   └── palettes.md              # Color palette guide\n├── examples/\n│   ├── sales.csv                # Sample sales data\n│   └── metrics.csv              # Sample metrics data\n├── README.md\n└── LICENSE\n```\n\n## 📝 License\n\nMIT © Denis Voronin\n\nFile v0.1.0:_meta.json\n\n{\n  \"ownerId\": \"kn75wwn4x6djaf28jbykeamazd81gtdp\",\n  \"slug\": \"data-viz-wizard\",\n  \"version\": \"0.1.0\",\n  \"publishedAt\": 1786688298732\n}\n\nFile v0.1.0:references/chart-selection.md\n\n# Chart Selection Guide\n\nDecision tree for choosing the right chart type for your data.\n\n## Decision Tree\n\n```\nSTART: What columns do you have?\n\n├── Date + Numeric\n│   ├── 1 numeric series → LINE chart\n│   ├── 2-3 numeric series → LINE chart (multi-line)\n│   ├── Many series over time → AREA chart (stacked)\n│   └── Single value over time + volume → AREA chart\n│\n├── Categorical + Numeric\n│   ├── ≤ 8 categories, 1 metric → PIE or DONUT chart\n│   ├── ≤ 8 categories, 2+ metrics → STACKED BAR\n│   ├── Many categories → BAR chart (horizontal if labels are long)\n│   └── Categories need ranking → BAR chart (sorted)\n│\n├── Numeric + Numeric\n│   ├── Looking for correlation → SCATTER chart\n│   └── Time-ordered pairs → SCATTER with trend line\n│\n├── Multiple Metrics (3-8, same scale)\n│   ├── Comparing across categories → RADAR chart\n│   └── Time series comparison → LINE chart\n│\n├── Two Categorical + Numeric\n│   └── → HEATMAP-GRID (matrix view)\n│\n└── Percentage data\n    ├── Parts of a whole → PIE or DONUT\n    └── Over time → LINE chart (with % formatting)\n```\n\n## Chart Types Reference\n\n| Chart Type | Best For | Data Shape |\n|-----------|----------|------------|\n| **Line** | Trends over time | Date × Numeric |\n| **Bar** | Category comparison | Category × Numeric |\n| **Stacked Bar** | Part-to-whole across categories | Category × Multiple Numerics |\n| **Area** | Cumulative trends | Date × Numeric |\n| **Scatter** | Correlation analysis | Numeric × Numeric |\n| **Pie** | Simple proportions (≤8 slices) | Category × Single Metric |\n| **Donut** | Cleaner proportions | Category × Single Metric |\n| **Radar** | Multi-dimensional comparison | 3-8 metrics × Categories |\n| **Heatmap Grid** | Matrix density/intensity | 2 Categories × Numeric |\n\n## Auto-Detection Logic\n\nThe `--type auto` flag uses this priority:\n\n1. **Date column present?** → Line chart (best for time series)\n2. **Categorical + 1 numeric, ≤8 categories?** → Pie/Donut\n3. **Categorical + numerics?** → Bar chart\n4. **2+ numerics, no date?** → Scatter\n5. **4+ numerics, no date?** → Radar\n6. **Fallback** → Bar chart\n\n## When to Override Auto\n\n- **Stacked bar**: use when you want to show composition across groups\n- **Area**: use for cumulative or volume data\n- **Radar**: use when comparing 3-8 metrics on a similar scale\n- **Heatmap**: use when you have two categorical dimensions and want intensity\n\nFile v0.1.0:references/palettes.md\n\n# Color Palette Guide\n\n## Available Palettes\n\n### 🟣 Viridis (Default)\n```\n#440154 → #482878 → #3E4989 → #31688E → #26828E → #1F9E89 → #35B779 → #6DCD59 → #B4DE2C → #FDE725\n```\n- **Perceptually uniform** — equal steps in color = equal steps in data\n- **Colorblind safe** — works for all types of color vision\n- **Best for**: Scientific data, heatmaps, sequential data, anything where accurate perception matters\n- **Use when**: Data accuracy is paramount, professional/scientific context\n\n### 🌅 Sunset\n```\n#3C1C2D → #6B2737 → #A0333F → #D44E50 → #F2784B → #F8A358 → #FBC96D → #F7F7B7 → #D9F0A3 → #A1DAB4\n```\n- Warm gradient from deep purple to soft green\n- **Best for**: Marketing dashboards, growth metrics, energy/enthusiasm\n- **Use when**: You want emotional warmth, storytelling data, growth narratives\n\n### 🌊 Ocean\n```\n#011A3A → #013A63 → #0353A4 → #0AA6C2 → #2EC4B6 → #5BC0BE → #6FFFE9 → #5390D9 → #48BFE3 → #56CFE1\n```\n- Cool blues and teals\n- **Best for**: Financial reports, corporate dashboards, calm/professional tone\n- **Use when**: Business/corporate context, trust/reliability themes, financial data\n\n### ⬛ Monochrome\n```\n#1a1a2e → #16213e → #1e2a45 → #2d3561 → #3a4373 → #4a5a8a → #5e72a4 → #7488b8 → #8da0cc → #a8b8e0\n```\n- Subtle grayscale-to-blue gradient\n- **Best for**: Print-friendly reports, minimal design, executive summaries\n- **Use when**: Data should speak for itself, print/PDF export, accessibility\n\n### 💜 Neon\n```\n#FF006E → #FB5607 → #FFBE0B → #8338EC → #3A86FF → #06FFA5 → #00F5D4 → #FF4081 → #7B2FF7 → #F72585\n```\n- Vibrant, high-contrast, attention-grabbing\n- **Best for**: Presentations, social media sharing, dashboards that need to pop\n- **Use when**: Engagement matters, presentations, younger audiences, bold statements\n\n## How to Choose\n\n| Context | Recommended Palette |\n|---------|-------------------|\n| Scientific / Academic | Viridis |\n| Business / Finance | Ocean |\n| Marketing / Growth | Sunset |\n| Executive / Print | Monochrome |\n| Presentation / Viral | Neon |\n| Heatmap | Viridis |\n| Pie/Donut | Neon or Sunset |\n| Line Chart (multi-series) | Ocean or Viridis |\n| Dashboard | Ocean (professional) or Neon (engagement) |\n\n## Usage\n\n```bash\n--palette viridis     # default\n--palette sunset\n--palette ocean\n--palette monochrome\n--palette neon\n```\n\n## Color Application Rules\n\n1. **Sequential data** (time series, rankings): Use palettes in order (viridis, ocean)\n2. **Categorical data** (pie, donut): High contrast palettes work best (neon, sunset)\n3. **Multi-series**: Assign colors cyclically from the palette\n4. **Single series**: First color of the palette\n5. **Transparency**: Area fills use 20% opacity, bars use 80%, points use full opacity\n\nFile v0.1.0:skill-card.md\n\n## Description:\n\nTransform CSV data into interactive chart visualizations with Chart.js.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[voronindenis5](https://clawhub.ai/user/voronindenis5)\n\n### License/Terms of Use:\n\nMIT\n\n## Use Case:\n\nDevelopers and analysts use this skill to turn CSV files into single charts or multi-chart dashboards with automatic chart selection, palette guidance, and export-ready HTML output.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Generated HTML can execute unescaped CSV or title content.\n\nMitigation: Treat generated reports as active web content; sanitize or review CSV data and chart titles before opening, sharing, or hosting reports, especially when inputs come from other people.\n\nRisk: Generated reports load remote JavaScript despite being described as standalone.\n\nMitigation: Verify the Chart.js dependency path before relying on offline or self-contained behavior, and avoid opening reports with sensitive data until the CDN dependency is fixed or replaced.\n\n## Reference(s):\n\n- [Chart Selection Guide](references/chart-selection.md)\n- [Color Palette Guide](references/palettes.md)\n- [Source Repository](https://github.com/voronindenis5/data-viz-wizard)\n- [ClawHub Skill Page](https://clawhub.ai/voronindenis5/skills/data-viz-wizard)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, code, shell commands, guidance]\n\n**Output Format:** [Markdown guidance with shell commands; generated artifacts are standalone HTML and JavaScript files.]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [The generated reports include interactive charts, theme toggling, PNG export, responsive layout, and Chart.js-based rendering.]\n\n## Skill Version(s):\n\n0.1.0 (source: ClawHub release metadata; artifact frontmatter lists 1.0.0)\n\n## Ethical Considerations:\n\nUsers should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.\n\nFile v0.1.0:LICENSE\n\nMIT License\n\nCopyright (c) 2026 Denis Voronin\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.","readmeExcerpt":"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","codeSnippets":[],"executableExamples":[{"language":"bash","snippet":"# Auto-detect best chart type\npython scripts/viz_wizard.py chart sales.csv --type auto --output chart.html\n\n# Generate a full dashboard with multiple charts\npython scripts/viz_wizard.py dashboard data.csv --output dashboard.html\n\n# Specify exact chart type and axes\npython scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'\n\n# Pipe data via stdin\ncat data.csv | python scripts/viz_wizard.py --auto --output viz.html"},{"language":"bash","snippet":"# Auto-detect the best chart for your data\npython scripts/viz_wizard.py chart sales.csv --type auto --output chart.html\n\n# Generate a full dashboard\npython scripts/viz_wizard.py dashboard data.csv --output dashboard.html\n\n# Explicit chart with custom axes\npython scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'\n\n# Pipe data through stdin\ncat data.csv | python scripts/viz_wizard.py --auto --output viz.html"},{"language":"text","snippet":"data-viz-wizard/\n├── SKILL.md                     # Skill definition\n├── scripts/\n│   └── viz_wizard.py            # Main script (Python stdlib only)\n├── references/\n│   ├── chart-selection.md       # Chart type decision tree\n│   └── palettes.md              # Color palette guide\n├── examples/\n│   ├── sales.csv                # Sample sales data\n│   └── metrics.csv              # Sample metrics data\n├── README.md\n└── LICENSE"},{"language":"text","snippet":"START: What columns do you have?\n\n├── Date + Numeric\n│   ├── 1 numeric series → LINE chart\n│   ├── 2-3 numeric series → LINE chart (multi-line)\n│   ├── Many series over time → AREA chart (stacked)\n│   └── Single value over time + volume → AREA chart\n│\n├── Categorical + Numeric\n│   ├── ≤ 8 categories, 1 metric → PIE or DONUT chart\n│   ├── ≤ 8 categories, 2+ metrics → STACKED BAR\n│   ├── Many categories → BAR chart (horizontal if labels are long)\n│   └── Categories need ranking → BAR chart (sorted)\n│\n├── Numeric + Numeric\n│   ├── Looking for correlation → SCATTER chart\n│   └── Time-ordered pairs → SCATTER with trend line\n│\n├── Multiple Metrics (3-8, same scale)\n│   ├── Comparing across categories → RADAR chart\n│   └── Time series comparison → LINE chart\n│\n├── Two Categorical + Numeric\n│   └── → HEATMAP-GRID (matrix view)\n│\n└── Percentage data\n    ├── Parts of a whole → PIE or DONUT\n    └── Over time → LINE chart (with % formatting)"},{"language":"text","snippet":"#440154 → #482878 → #3E4989 → #31688E → #26828E → #1F9E89 → #35B779 → #6DCD59 → #B4DE2C → #FDE725"},{"language":"text","snippet":"#3C1C2D → #6B2737 → #A0333F → #D44E50 → #F2784B → #F8A358 → #FBC96D → #F7F7B7 → #D9F0A3 → #A1DAB4"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: data-viz-wizard\nversion: 1.0.0\nauthor: Denis Voronin\nlicense: MIT\ndescription: Transform CSV data into stunning interactive chart visualizations with Chart.js\ncategory: data-science\ntags:\n  - csv\n  - charts\n  - visualization\n  - chartjs\n  - dashboard\n  - data-viz\n---\n\n# Data Viz Wizard\n\nTransform any CSV file into stunning, interactive HTML chart visualizations powered by Chart.js.\n\n## Quick Start\n\n```bash\n# Auto-detect best chart type\npython scripts/viz_wizard.py chart sales.csv --type auto --output chart.html\n\n# Generate a full dashboard with multiple charts\npython scripts/viz_wizard.py dashboard data.csv --output dashboard.html\n\n# Specify exact chart type and axes\npython scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'\n\n# Pipe data via stdin\ncat data.csv | python scripts/viz_wizard.py --auto --output viz.html\n```\n\n## Commands\n\n| Command | Description |\n|---------|-------------|\n| `chart <file>` | Generate a single chart (best auto-detected type) |\n| `dashboard <file>` | Generate multi-chart dashboard from same dataset |\n| `csv <file>` | Explicit chart with specified type and columns |\n| _(stdin pipe)_ | `--auto` mode reads CSV from stdin |\n\n## Flags\n\n| Flag | Description |\n|------|-------------|\n| `--type` | Chart type: `auto`, `line`, `bar`, `stacked`, `area`, `scatter`, `pie`, `donut`, `radar`, `heatmap-grid` |\n| `--x` | Column name for X-axis |\n| `--y` | Column name(s) for Y-axis (comma-separated) |\n| `--output` / `-o` | Output HTML file path |\n| `--title` | Chart title |\n| `--palette` | Color palette: `viridis`, `sunset`, `ocean`, `monochrome`, `neon` |\n| `--trend` | Add trend line |\n| `--moving-average` / `-ma` | Window size for moving average |\n| `--theme` | `dark`, `light`, or `auto` (toggleable) |\n| `--auto` | Full auto-mode (stdin) |\n\n## Column Auto-Detection\n\nThe wizard auto-detects column types:\n- **Date**: ISO dates, `YYYY-MM-DD`, `MM/DD/YYYY`, etc.\n- **Numeric**: integers, floats, currency\n- **Percentage**: values with `%` suffix\n- **Categorical**: strings, low-cardinality text\n\n## Output Features\n\nEvery generated HTML includes:\n- Smooth Chart.js animations\n- Professional color palettes\n- Dark/light theme toggle ( persisted)\n- Download chart as PNG button\n- Responsive resize\n- Smart tooltips (currency, percentage, date formatting)\n\n## References\n\n- [Chart Selection Guide](references/chart-selection.md) — Decision tree for choosing the right chart type\n- [Color Palettes](references/palettes.md) — Palette definitions and when to use each"},{"path":"README.md","content":"# 📊 Data Viz Wizard\n\n> Transform any CSV into stunning interactive charts — instantly.\n\nData 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.\n\n## ✨ Features\n\n- **Auto-detect column types**: dates, numbers, percentages, categories\n- **Smart chart selection**: picks the best chart type for your data automatically\n- **10 chart types**: line, bar, stacked bar, area, scatter, pie, donut, radar, heatmap-grid\n- **Multi-chart dashboards**: generate an entire dashboard from one CSV\n- **5 professional palettes**: viridis, sunset, ocean, monochrome, neon\n- **Trend lines & moving averages**: built-in analytical overlays\n- **Theme toggle**: dark/light mode with persistence\n- **PNG export**: download any chart as an image\n- **Responsive**: works on desktop and mobile\n- **Zero dependencies**: pure Python stdlib, no pip install needed\n\n## 🚀 Quick Start\n\n```bash\n# Auto-detect the best chart for your data\npython scripts/viz_wizard.py chart sales.csv --type auto --output chart.html\n\n# Generate a full dashboard\npython scripts/viz_wizard.py dashboard data.csv --output dashboard.html\n\n# Explicit chart with custom axes\npython scripts/viz_wizard.py csv metrics.csv --type line --x date --y revenue --title 'Revenue Trend'\n\n# Pipe data through stdin\ncat data.csv | python scripts/viz_wizard.py --auto --output viz.html\n```\n\nOpen the generated HTML file in any browser. That's it.\n\n## 📋 Commands\n\n| Command | Description |\n|---------|-------------|\n| `chart <file>` | Single chart with auto type detection |\n| `dashboard <file>` | Multi-chart dashboard from one dataset |\n| `csv <file>` | Explicit type + column specification |\n| _(pipe)_ | `--auto` mode for stdin input |\n\n## 🎨 Color Palettes\n\n| Palette | Best For |\n|---------|----------|\n| `viridis` | Scientific/data — perceptually uniform |\n| `sunset` | Warm, energetic — marketing dashboards |\n| `ocean` | Cool, calm — financial/business reports |\n| `monochrome` | Clean, minimal — print-friendly |\n| `neon` | Vibrant, bold — presentations |\n\n## 📁 Structure\n\n```\ndata-viz-wizard/\n├── SKILL.md                     # Skill definition\n├── scripts/\n│   └── viz_wizard.py            # Main script (Python stdlib only)\n├── references/\n│   ├── chart-selection.md       # Chart type decision tree\n│   └── palettes.md              # Color palette guide\n├── examples/\n│   ├── sales.csv                # Sample sales data\n│   └── metrics.csv              # Sample metrics data\n├── README.md\n└── LICENSE\n```\n\n## 📝 License\n\nMIT © Denis Voronin"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn75wwn4x6djaf28jbykeamazd81gtdp\",\n  \"slug\": \"data-viz-wizard\",\n  \"version\": \"0.1.0\",\n  \"publishedAt\": 1786688298732\n}"},{"path":"references/chart-selection.md","content":"# Chart Selection Guide\n\nDecision tree for choosing the right chart type for your data.\n\n## Decision Tree\n\n```\nSTART: What columns do you have?\n\n├── Date + Numeric\n│   ├── 1 numeric series → LINE chart\n│   ├── 2-3 numeric series → LINE chart (multi-line)\n│   ├── Many series over time → AREA chart (stacked)\n│   └── Single value over time + volume → AREA chart\n│\n├── Categorical + Numeric\n│   ├── ≤ 8 categories, 1 metric → PIE or DONUT chart\n│   ├── ≤ 8 categories, 2+ metrics → STACKED BAR\n│   ├── Many categories → BAR chart (horizontal if labels are long)\n│   └── Categories need ranking → BAR chart (sorted)\n│\n├── Numeric + Numeric\n│   ├── Looking for correlation → SCATTER chart\n│   └── Time-ordered pairs → SCATTER with trend line\n│\n├── Multiple Metrics (3-8, same scale)\n│   ├── Comparing across categories → RADAR chart\n│   └── Time series comparison → LINE chart\n│\n├── Two Categorical + Numeric\n│   └── → HEATMAP-GRID (matrix view)\n│\n└── Percentage data\n    ├── Parts of a whole → PIE or DONUT\n    └── Over time → LINE chart (with % formatting)\n```\n\n## Chart Types Reference\n\n| Chart Type | Best For | Data Shape |\n|-----------|----------|------------|\n| **Line** | Trends over time | Date × Numeric |\n| **Bar** | Category comparison | Category × Numeric |\n| **Stacked Bar** | Part-to-whole across categories | Category × Multiple Numerics |\n| **Area** | Cumulative trends | Date × Numeric |\n| **Scatter** | Correlation analysis | Numeric × Numeric |\n| **Pie** | Simple proportions (≤8 slices) | Category × Single Metric |\n| **Donut** | Cleaner proportions | Category × Single Metric |\n| **Radar** | Multi-dimensional comparison | 3-8 metrics × Categories |\n| **Heatmap Grid** | Matrix density/intensity | 2 Categories × Numeric |\n\n## Auto-Detection Logic\n\nThe `--type auto` flag uses this priority:\n\n1. **Date column present?** → Line chart (best for time series)\n2. **Categorical + 1 numeric, ≤8 categories?** → Pie/Donut\n3. **Categorical + numerics?** → Bar chart\n4. **2+ numerics, no date?** → Scatter\n5. **4+ numerics, no date?** → Radar\n6. **Fallback** → Bar chart\n\n## When to Override Auto\n\n- **Stacked bar**: use when you want to show composition across groups\n- **Area**: use for cumulative or volume data\n- **Radar**: use when comparing 3-8 metrics on a similar scale\n- **Heatmap**: use when you have two categorical dimensions and want intensity"},{"path":"references/palettes.md","content":"# Color Palette Guide\n\n## Available Palettes\n\n### 🟣 Viridis (Default)\n```\n#440154 → #482878 → #3E4989 → #31688E → #26828E → #1F9E89 → #35B779 → #6DCD59 → #B4DE2C → #FDE725\n```\n- **Perceptually uniform** — equal steps in color = equal steps in data\n- **Colorblind safe** — works for all types of color vision\n- **Best for**: Scientific data, heatmaps, sequential data, anything where accurate perception matters\n- **Use when**: Data accuracy is paramount, professional/scientific context\n\n### 🌅 Sunset\n```\n#3C1C2D → #6B2737 → #A0333F → #D44E50 → #F2784B → #F8A358 → #FBC96D → #F7F7B7 → #D9F0A3 → #A1DAB4\n```\n- Warm gradient from deep purple to soft green\n- **Best for**: Marketing dashboards, growth metrics, energy/enthusiasm\n- **Use when**: You want emotional warmth, storytelling data, growth narratives\n\n### 🌊 Ocean\n```\n#011A3A → #013A63 → #0353A4 → #0AA6C2 → #2EC4B6 → #5BC0BE → #6FFFE9 → #5390D9 → #48BFE3 → #56CFE1\n```\n- Cool blues and teals\n- **Best for**: Financial reports, corporate dashboards, calm/professional tone\n- **Use when**: Business/corporate context, trust/reliability themes, financial data\n\n### ⬛ Monochrome\n```\n#1a1a2e → #16213e → #1e2a45 → #2d3561 → #3a4373 → #4a5a8a → #5e72a4 → #7488b8 → #8da0cc → #a8b8e0\n```\n- Subtle grayscale-to-blue gradient\n- **Best for**: Print-friendly reports, minimal design, executive summaries\n- **Use when**: Data should speak for itself, print/PDF export, accessibility\n\n### 💜 Neon\n```\n#FF006E → #FB5607 → #FFBE0B → #8338EC → #3A86FF → #06FFA5 → #00F5D4 → #FF4081 → #7B2FF7 → #F72585\n```\n- Vibrant, high-contrast, attention-grabbing\n- **Best for**: Presentations, social media sharing, dashboards that need to pop\n- **Use when**: Engagement matters, presentations, younger audiences, bold statements\n\n## How to Choose\n\n| Context | Recommended Palette |\n|---------|-------------------|\n| Scientific / Academic | Viridis |\n| Business / Finance | Ocean |\n| Marketing / Growth | Sunset |\n| Executive / Print | Monochrome |\n| Presentation / Viral | Neon |\n| Heatmap | Viridis |\n| Pie/Donut | Neon or Sunset |\n| Line Chart (multi-series) | Ocean or Viridis |\n| Dashboard | Ocean (professional) or Neon (engagement) |\n\n## Usage\n\n```bash\n--palette viridis     # default\n--palette sunset\n--palette ocean\n--palette monochrome\n--palette neon\n```\n\n## Color Application Rules\n\n1. **Sequential data** (time series, rankings): Use palettes in order (viridis, ocean)\n2. **Categorical data** (pie, donut): High contrast palettes work best (neon, sunset)\n3. **Multi-series**: Assign colors cyclically from the palette\n4. **Single series**: First color of the palette\n5. **Transparency**: Area fills use 20% opacity, bars use 80%, points use full opacity"}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":"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 | 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