SaurabhSSB

🥈Silver

Automate data analysis and visualization tasks using Python, R, and statistics. Ideal for operations teams needing to streamline workflows and gain insights from data. Connects to n8n for workflow automation and supports Claude for AI-driven data processing.

2110Updated 1mo ago

Intermediate30min to implementautomation

Saves ~200 min per use

Quick InstallView Source

git clone https://github.com/SaurabhSSB/SaurabhSSB.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/SaurabhSSB/SaurabhSSB

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the following [DATA] from [COMPANY] in the [INDUSTRY] sector. Provide a summary of key insights, trends, and outliers. Generate visualizations to support your findings. Use Python or R for the analysis and ensure the visualizations are suitable for a business audience.

Example Output

# Data Analysis Summary

## Key Insights
- The [DATA] shows a steady increase in customer acquisition over the past quarter, with a notable spike in [MONTH].
- Customer retention rates have improved by 15% compared to the previous quarter.
- There is a significant outlier in the data for [REGION], which may require further investigation.

## Trends
- The trend indicates a positive growth trajectory for [COMPANY] in the [INDUSTRY] sector.
- The data suggests that marketing campaigns in [MONTH] were particularly effective.

## Visualizations
1. **Customer Acquisition Over Time**: A line chart showing the increase in customer acquisition over the past quarter.
2. **Customer Retention Rates**: A bar chart comparing retention rates across different quarters.
3. **Regional Performance**: A heatmap highlighting the performance of different regions, with a focus on the outlier in [REGION].