专业报告生成器 - 自动创建数据报告、KPI仪表板与可视化图表

report-generator by claude-office-skills/skills

317 周安装量

26 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/claude-office-skills/skills --skill report-generator

数据可视化自动化数据分析

🇨🇳中文介绍

报告生成器技能

概述

该技能支持自动生成专业的数据报告。利用您的数据创建包含图表、表格和洞察信息的仪表板、KPI 摘要和分析报告。

使用方法

提供数据（CSV、Excel、JSON 格式或描述数据）
指定所需报告的类型
我将生成包含可视化内容的格式化报告

示例提示：

"根据此数据生成销售报告"
"创建月度 KPI 仪表板"
"构建包含图表的执行摘要"
"生成数据分析报告"

领域知识

报告组件

# Report structure
report = {
    'title': 'Monthly Sales Report',
    'period': 'January 2024',
    'sections': [
        'executive_summary',
        'kpi_dashboard',
        'detailed_analysis',
        'charts',
        'recommendations'
    ]
}

使用 Python 生成报告

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

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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

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')

🇺🇸English

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, 'w') as f:
        f.write(html)
    
    return output_path

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

Resources

Weekly Installs

Repository

claude-office-s…s/skills

GitHub Stars

First Seen

1 day ago

Security Audits

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Installed on

claude-code23

gemini-cli6

github-copilot6

codex6

amp6

cline6