Csv Data Visualizer

export default async function csv_data_visualizer(input) {
  console.log("🧠 Running skill: csv-data-visualizer");
  
  // TODO: implement actual logic for this skill
  return {
    message: "Skill 'csv-data-visualizer' executed successfully!",
    input
  };
}


{
  "name": "@ai-labs-claude-skills/csv-data-visualizer",
  "version": "1.0.0",
  "description": "Claude AI skill: csv-data-visualizer",
  "main": "index.js",
  "files": [
    "."
  ],
  "license": "MIT",
  "author": "AI Labs"
}

# CSV Data Visualization Guide

This guide provides best practices for choosing appropriate visualization types based on data characteristics.

## Choosing the Right Visualization

### For Numeric Data Distribution

**Histogram**
- **When to use**: Show the distribution of a single numeric variable
- **Best for**: Understanding data spread, central tendency, and shape
- **Example use cases**: Age distribution, salary ranges, test scores
- **Command**: `--histogram column_name --bins 30`

**Box Plot**
- **When to use**: Compare distributions across categories or identify outliers
- **Best for**: Showing median, quartiles, and outliers
- **Example use cases**: Salary by department, performance by region
- **Command**: `--boxplot column_name --group-by category`

**Violin Plot**
- **When to use**: Similar to box plot but shows full distribution shape
- **Best for**: Detailed distribution comparison with density information
- **Example use cases**: Score distributions across groups
- **Command**: `--violin column_name --group-by category`

### For Relationships Between Variables

**Scatter Plot**
- **When to use**: Show relationship between two numeric variables
- **Best for**: Identifying correlations, trends, and clusters
- **Example use cases**: Height vs weight, price vs demand, age vs income
- **Command**: `--scatter x_column y_column --color category`
- **With trend line**: Automatically added when no color grouping

**Correlation Heatmap**
- **When to use**: Show correlations between multiple numeric variables
- **Best for**: Identifying which variables are related
- **Example use cases**: Feature correlation analysis, multicollinearity detection
- **Command**: `--correlation`

### For Time Series Data

**Line Chart**
- **When to use**: Show trends over time or ordered sequences
- **Best for**: Temporal patterns, trends, seasonality
- **Example use cases**: Sales over time, stock prices, temperature trends
- **Command**: `--line date_column value_column`
- **Multiple series**: `--line date_column "value1,value2,value3"`

### For Categorical Data

**Bar Chart**
- **When to use**: Compare values across categories
- **Best for**: Discrete categories with counts or aggregated values
- **Example use cases**: Sales by region, counts by category
- **Command**: `--bar category_column`

**Pie Chart**
- **When to use**: Show composition of a whole (use sparingly)
- **Best for**: Simple proportions with few categories (3-5 max)
- **Example use cases**: Market share, budget allocation
- **Command**: `--pie category_column`
- **Note**: Use bar charts instead for better comparisons

## Data Profiling Recommendations

Before creating visualizations, run data profiling to understand:
- Data types and ranges
- Missing data patterns
- Outliers and data quality issues
- Statistical distributions

**Usage**: `python3 scripts/data_profile.py data.csv`

This helps identify:
- Which columns are suitable for visualization
- Data quality issues that need addressing
- Appropriate visualization types for each column

## Dashboard Best Practices

**Automatic Dashboard**
- Analyzes data types and creates appropriate visualizations
- Good starting point for exploratory analysis
- Usage: `python3 scripts/create_dashboard.py data.csv`

**Custom Dashboard**
- Create JSON config file specifying exact plots desired
- Better for specific analysis goals or presentations
- Allows precise control over layout and content

### Dashboard Configuration Example

```json
{
  "title":