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Data Analyst

Clean data, generate charts, and calculate business metrics with Python scripts.

Updated Feb 27, 2026
Search Tags:#python#reporting#data analysis#visualization#csv#business metrics
One-click install
npx skills add https://github.com/Mysense775/deya-openclaw --skill data-analyst-mysense775
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Data Analyst
Source: https://github.com/Mysense775/deya-openclaw/tree/main/data-analyst
Command: npx skills add https://github.com/Mysense775/deya-openclaw --skill data-analyst-mysense775

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, plotly, openpyxl, jinja2, fpdf2, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to analyze data, generate visualizations, and calculate key business metrics without needing deep data science expertise.

Core Features & Use Cases

  • Data Processing: Load, clean, and prepare data from various formats (CSV, JSON, Excel).
  • Visualization: Create insightful charts (line, bar, pie, heatmap, etc.) to understand trends and distributions.
  • Business Metrics: Calculate essential metrics like LTV, CAC, Churn, Retention, ARPU, and MRR.
  • Use Case: Analyze monthly sales data to identify top-performing products, visualize revenue trends over time, and calculate customer lifetime value to inform marketing strategies.

Quick Start

Use the Data Analyst skill to analyze the file 'sales_data.csv' and generate a line chart of revenue over time.

Frequently Asked Questions about Data Analyst

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate business metrics like MRR and CAC from a CSV file?▼

To calculate business metrics like MRR and CAC from a CSV file, you can use Python-based data analysis scripts to load your data, process it, and automatically compute key performance indicators like LTV, Churn, Retention, ARPU, and MRR.

Can I generate data visualizations like heatmaps and line charts using Python?▼

Yes, you can generate data visualizations like heatmaps and line charts using Python by leveraging libraries such as matplotlib, seaborn, and plotly to create insightful charts that reveal data trends and distributions.

What's the best way to clean and prepare Excel and JSON data for reporting?▼

The best way to clean and prepare Excel and JSON data for reporting is to use automated Python data processing scripts that load, clean, and structure raw data formats for rapid exploration and visualization.

Do I need deep data science expertise to track KPIs and automate reporting?▼

No, you do not need deep data science expertise to track KPIs and automate reporting, as AI-driven data analysis capabilities enable you to generate visualizations and calculate key business metrics without specialized knowledge.

Does this data analysis approach support exporting automated reports to PDF?▼

Yes, this data analysis approach supports exporting automated reports to PDF by utilizing Python libraries like jinja2 for templating and fpdf2 for generating the final document outputs from your analyzed data.

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