report-generator

تایید شده

Generate professional data reports with charts, tables, and visualizations

@openclawv1.0MIT۱۴۰۴/۱۲/۳
55از ۱۰۰
(0)
۱.۰k
۱۲۴
۱۳۲

نصب مهارت

مهارت‌ها کدهای شخص ثالث از مخازن عمومی GitHub هستند. SkillHub الگوهای مخرب شناخته‌شده را اسکن می‌کند اما نمی‌تواند امنیت را تضمین کند. قبل از نصب، کد منبع را بررسی کنید.

نصب سراسری (سطح کاربر):

npx skillhub install openclaw/skills/report-generator

نصب در پروژه فعلی:

npx skillhub install openclaw/skills/report-generator --project

مسیر پیشنهادی: ~/.claude/skills/report-generator/

بررسی هوش مصنوعی

کیفیت دستورالعمل55
دقت توضیحات45
کاربردی بودن61
صحت فنی60

Decent report generation reference with correct Python examples, but no actual scripts — code is inline. Description is generic without trigger phrases. Useful framework but Claude already knows pandas/matplotlib.

محتوای SKILL.md

---
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

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

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

### Using Python for Reports

```python
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

```python
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

```python
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](https://matplotlib.org/)
- [Plotly](https://plotly.com/)
- [ReportLab](https://www.reportlab.com/)