---
# ═══════════════════════════════════════════════════════════════════════════════
# CLAUDE OFFICE SKILL - Enhanced Metadata v2.0
# ═══════════════════════════════════════════════════════════════════════════════
# Basic Information
name: report-generator
description: "Generate professional data reports with charts, tables, and visualizations"
version: "1.0"
author: claude-office-skills
license: MIT
# Categorization
category: presentation
tags:
- report
- generator
- data
- visualization
department: All
# AI Model Compatibility
models:
recommended:
- claude-sonnet-4
- claude-opus-4
compatible:
- claude-3-5-sonnet
- gpt-4
- gpt-4o
# MCP Tools Integration
mcp:
server: office-mcp
tools:
- create_docx
- create_xlsx
- create_chart
- create_pptx
# Skill Capabilities
capabilities:
- report_generation
- data_visualization
# Language Support
languages:
- en
- zh
---
# 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'''
{title}
{title}
${data['revenue']:,.0f}
Total Revenue
{data['growth']:.1%}
Growth Rate
'''
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/)