interactive-report-generator

name	interactive-report-generator
description	Generate interactive HTML reports with Plotly visualizations from data analysis results. Supports dashboards, charts, and professional styling.
version	1.0.0
category	workspace-hub
type	skill
trigger	manual
auto_execute	false
capabilities	["plotly_visualization","html_report_generation","dashboard_creation","csv_data_integration","responsive_design"]
tools	["Write","Read","Bash"]
related_skills	["data-validation-reporter","yaml-workflow-executor"]

Interactive Report Generator

Create professional, interactive HTML reports with Plotly visualizations.

Quick Start

# Generate report from data
/interactive-report-generator --data results.csv --output report.html

# Create dashboard
/interactive-report-generator --type dashboard --config dashboard.yaml

# Generate from analysis results
/interactive-report-generator --source analysis_output/

When to Use

USE when:

• Creating analysis reports
• Generating data visualizations
• Building dashboards
• Presenting results to stakeholders

DON'T USE when:

• Static images are required
• PDF export is primary format
• Real-time streaming data

Prerequisites

• Python 3.9+
• plotly>=5.15.0
• pandas>=2.0.0
• Data in CSV/DataFrame format

Overview

Generates interactive HTML reports compliant with workspace-hub HTML_REPORTING_STANDARDS.md:

1. Interactive plots only - No static matplotlib
2. CSV data import - Relative paths
3. Responsive design - Works on all devices
4. Professional styling - Consistent theming
5. Export options - PNG, SVG from plots

Core Templates

1. Basic Report Template

"""
ABOUTME: Interactive HTML report generator
ABOUTME: Creates Plotly-based reports from analysis data
"""

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from pathlib import Path
from datetime import datetime
from typing  , , , 


 () -> Path:
    
    config = config  {}

    
    html_content = 

    
    output_path.parent.mkdir(parents=, exist_ok=)
    output_path.write_text(html_content)

     output_path


 () -> go.Figure:
    
    fig = go.Figure()

     col  y_cols:
        fig.add_trace(go.Scatter(
            x=data[x_col],
            y=data[col],
            mode=,
            name=col,
            hovertemplate=
        ))

    fig.update_layout(
        title=title,
        xaxis_title=x_col,
        yaxis_title=,
        hovermode=,
        template=,
        height=
    )

     fig


 () -> go.Figure:
    
    fig = px.histogram(
        data,
        x=column,
        title=title,
        template=,
        nbins=
    )

    fig.update_layout(
        xaxis_title=column,
        yaxis_title=,
        height=
    )

     fig


 () -> go.Figure:
    
     columns:
        corr_data = data[columns].corr()
    :
        corr_data = data.select_dtypes(include=).corr()

    fig = px.imshow(
        corr_data,
        title=title,
        template=,
        color_continuous_scale=,
        zmin=-,
        zmax=
    )

    fig.update_layout(height=)

     fig


 () -> Path:
    
    
    fig = make_subplots(
        rows=, cols=,
        subplot_titles=(
            ,
            ,
            ,
            
        ),
        specs=[
            [{: }, {: }],
            [{: }, {: }]
        ]
    )

    
    numeric_cols = data.select_dtypes(include=).columns.tolist()

     (numeric_cols) >= :
        
        fig.add_trace(
            go.Scatter(
                x=data.index,
                y=data[numeric_cols[]],
                mode=,
                name=numeric_cols[]
            ),
            row=, col=
        )

        
        fig.add_trace(
            go.Histogram(x=data[numeric_cols[]], name=),
            row=, col=
        )

        
        fig.add_trace(
            go.Scatter(
                x=data[numeric_cols[]],
                y=data[numeric_cols[]],
                mode=,
                name=
            ),
            row=, col=
        )

    
    stats = data.describe().reset_index()
    fig.add_trace(
        go.Table(
            header=(values=(stats.columns)),
            cells=(values=[stats[col].()  col  stats.columns])
        ),
        row=, col=
    )

    fig.update_layout(
        height=,
        showlegend=,
        title_text=
    )

    
    fig.write_html(output_path, include_plotlyjs=)

     output_path