数据报告生成器 / Report Generator Skill

中文说明 / Chinese Overview

本 Skill 用于把结构化数据、指标和分析要求组织成专业报告，支持摘要、方法、数据表、图表、发现、限制和结论等章节，并可面向 DOCX、XLSX 或 PPTX 输出。它只处理用户提供的数据，不接入 BI 平台或外部数据源，也不需要账号、API Key 或外部服务。

中文输入与输出

• 输入：数据文件或表格、指标定义、受众、时间范围、图表要求和输出格式。
• 输出：报告正文、数据表、图表配置、洞察摘要和待核验事项。
• 限制：不会把相关性直接表述为因果关系；数据质量和业务结论必须人工复核。

English Overview

This Skill turns user-provided structured data, metrics, and analysis requirements into professional reports with summaries, methods, tables, charts, findings, limitations, and conclusions. It supports DOCX, XLSX, and PPTX delivery without connecting to BI platforms or external data sources.

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

1. Provide data (CSV, Excel, JSON, or describe it)
2. Specify the type of report needed
3. 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