report-generator

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''' {title} 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; }} {title} ${data['revenue']:,.0f} Total Revenue {data['growth']:.1%} Growth Rate ''' 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 Weekly Installs191Repositoryclaude-office-s…s/skillsGitHub Stars12First Seen9 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled onclaude-code139opencode92github-copilot91gemini-cli89amp89cline89