report-generator

Generate professional data reports with charts, tables, and visualizations

Ir a la instalación

Repositorio: johnalbertini14-glitch/openclaw-skills
Última actividad en el origen: 30 de enero de 2026 a las 04:35
Idioma detectado de SKILL.md: inglés
Estrellas: 1
Forks: 0

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Volver a la búsqueda Volver a la instalación

name	report-generator
description	Generate professional data reports with charts, tables, and visualizations
author	claude-office-skills
version	1.0
tags	["report","visualization","charts","data","automation"]
models	["claude-sonnet-4","claude-opus-4"]
tools	["computer","code_execution","file_operations"]

Report Generator Skill

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, )  f:
        f.write(html)
    
     output_path

create_sales_report(, )

Resources