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Report Generator

claude-office-skills/skills
2.8k installs182 stars
Summary

Turns data into formatted reports with charts, tables, and KPIs. You feed it CSV, Excel, or JSON files, specify what kind of report you need (sales dashboard, executive summary, analytics report), and it generates HTML, PDF, or Office documents with visualizations. Uses matplotlib and plotly for charts, can output to multiple formats via the office-mcp tools. The real value is stitching together the whole pipeline instead of manually wrangling pandas dataframes and chart libraries. Works best when you have structured data and need a repeatable reporting format, though the examples lean heavily on Python code generation rather than purely automated output. Good for monthly reports or standard dashboards you generate regularly.

Install to Claude Code

npx -y skills add claude-office-skills/skills --skill report-generator --agent claude-code

Installs into .claude/skills of the current project.

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CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
Put your SEO on autopilot
Put your SEO on autopilot
An agent that runs the SEO playbooks that move rankings and ships PRs you control.
Get founding access →
Vibe Prospecting MCPVibe Prospecting MCP
Vibe Prospecting MCP
Connect Claude to +800M contacts, +150M companies. Find & Enrich leads in chat.
Try For Free →
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Agent, run crypto. Access onchain data & trade routes via 1inch.
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Files
SKILL.mdView on GitHub
Featured
CodeRabbit
CodeRabbit
AI writes the code. CodeRabbit catches the slop.
Try For Free →
AppSignal
AppSignal
Monitor with ease. Code with confidence.
Start Free Trial →
Make money from your Skills
Make money from your Skills
On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →
Put your SEO on autopilot
Put your SEO on autopilot
An agent that runs the SEO playbooks that move rankings and ships PRs you control.
Get founding access →
Vibe Prospecting MCPVibe Prospecting MCP
Vibe Prospecting MCP
Connect Claude to +800M contacts, +150M companies. Find & Enrich leads in chat.
Try For Free →
Make your agent a DeFi expert
Make your agent a DeFi expert
Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →
Categories
Automation & WorkflowsOffice & Documents
First SeenApr 16, 2026
View on GitHub

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

# 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

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