SKILL.md

Report Generator Skill

Name: report-generator
Author: claude-office-skills

Overview

This skill enables automatic generation of professional data reports. Create dashboards, KPI summaries, and analytical reports with charts, tables, and insights from your data.

How to Use

Provide data (CSV, Excel, JSON, or describe it)

Specify the type of report needed

I'll generate a formatted report with visualizations

Example prompts:

"Generate a sales report from this data"

"Create a monthly KPI dashboard"

"Build an executive summary with charts"

"Produce a data analysis report"

Domain Knowledge

Report Components

# Report structure

report = {

    'title': 'Monthly Sales Report',

    'period': 'January 2024',

    'sections': [

        'executive_summary',

        'kpi_dashboard',

        'detailed_analysis',

        'charts',

        'recommendations'

    ]

}

Using Python for Reports

import pandas as pd

import matplotlib.pyplot as plt

from reportlab.lib.pagesizes import letter

from reportlab.pdfgen import canvas

def generate_report(data, output_path):

    # Load data

    df = pd.read_csv(data)

    # Calculate KPIs

    total_revenue = df['revenue'].sum()

    avg_order = df['revenue'].mean()

    growth = df['revenue'].pct_change().mean()

    # Create charts

    fig, axes = plt.subplots(2, 2, figsize=(12, 10))

    df.plot(kind='bar', ax=axes[0,0], title='Revenue by Month')

    df.plot(kind='line', ax=axes[0,1], title='Trend')

    plt.savefig('charts.png')

    # Generate PDF

    # ... PDF generation code

    return output_path

HTML Report Template

def generate_html_report(data, title):

    html = f'''

    <!DOCTYPE html>

    <html>

    <head>

        <title>{title}</title>

        <style>

            body {{ font-family: Arial; margin: 40px; }}

            .kpi {{ display: flex; gap: 20px; }}

            .kpi-card {{ background: #f5f5f5; padding: 20px; border-radius: 8px; }}

            .metric {{ font-size: 2em; font-weight: bold; color: #2563eb; }}

            table {{ border-collapse: collapse; width: 100%; }}

            th, td {{ border: 1px solid #ddd; padding: 12px; text-align: left; }}

        </style>

    </head>

    <body>

        <h1>{title}</h1>

        <div class="kpi">

            <div class="kpi-card">

                <div class="metric">${data['revenue']:,.0f}</div>

                <div>Total Revenue</div>

            </div>

            <div class="kpi-card">

                <div class="metric">{data['growth']:.1%}</div>

                <div>Growth Rate</div>

            </div>

        </div>

        <!-- More content -->

    </body>

    </html>

    '''

    return html

Example: Sales Report

import pandas as pd

import matplotlib.pyplot as plt

def create_sales_report(csv_path, output_path):

    # Read data

    df = pd.read_csv(csv_path)

    # Calculate metrics

    metrics = {

        'total_revenue': df['amount'].sum(),

        'total_orders': len(df),

        'avg_order': df['amount'].mean(),

        'top_product': df.groupby('product')['amount'].sum().idxmax()

    }

    # Create visualizations

    fig, axes = plt.subplots(2, 2, figsize=(14, 10))

    # Revenue by product

    df.groupby('product')['amount'].sum().plot(

        kind='bar', ax=axes[0,0], title='Revenue by Product'

    )

    # Monthly trend

    df.groupby('month')['amount'].sum().plot(

        kind='line', ax=axes[0,1], title='Monthly Revenue'

    )

    plt.tight_layout()

    plt.savefig(output_path.replace('.html', '_charts.png'))

    # Generate HTML report

    html = generate_html_report(metrics, 'Sales Report')

    with open(output_path, 'w') as f:

        f.write(html)

    return output_path

create_sales_report('sales_data.csv', 'sales_report.html')

Resources

Matplotlib

Plotly

ReportLab

report-generator