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data-analyst

Analyze data with Python Pandas and SQL to generate reports and visualizations.

Updated Mar 1, 2026
Search Tags:#reporting#data analysis#sql#visualization#pandas#spreadsheet
One-click install
npx skills add https://github.com/zangxin75/openclaw-skills --skill data-analyst-zangxin75
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/zangxin75/openclaw-skills/tree/main/data-analyst
Command: npx skills add https://github.com/zangxin75/openclaw-skills --skill data-analyst-zangxin75

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers AI agents to perform complex data analysis tasks, turning raw data into actionable insights through visualization, SQL querying, and spreadsheet automation.

Core Features & Use Cases

  • SQL Queries: Write and execute SQL against various databases.
  • Spreadsheet Analysis: Process CSV, Excel, and Google Sheets data using Python/Pandas.
  • Data Visualization: Create charts and graphs to represent data trends and comparisons.
  • Report Generation: Automate the creation of data-driven reports.
  • Use Case: Analyze monthly sales data from a CSV file, identify top-performing product categories, and generate a summary report with visualizations.

Quick Start

Use the data-analyst skill to analyze the data in 'sales_data.csv' and generate a summary report.

Frequently Asked Questions about data-analyst

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

FAQPage Schema
How do I analyze a CSV file and generate a summary report with visualizations?▼

To analyze a CSV file and generate a summary report, use Python and Pandas for spreadsheet analysis and data cleaning. This process transforms raw data into actionable insights by creating visualizations like charts and graphs to represent trends for business intelligence and decision-making.

What is the best way to run SQL queries for business intelligence reporting?▼

The best way to run SQL queries for business intelligence reporting is using an analysis tool that writes and executes SQL directly against various databases. This approach transforms raw database records into actionable insights, automating the creation of data-driven reports for decision-making.

Can I use Pandas to clean and process Excel spreadsheet data for statistical analysis?▼

Yes, you can use Pandas to clean and process Excel spreadsheet data for statistical analysis. The library handles data cleaning and processes spreadsheet formats, performing statistical analysis to transform raw spreadsheet data into actionable insights for business intelligence.

Do I need to configure data sources before performing data visualization and analysis?▼

Yes, you must configure data sources before performing data visualization and analysis. Configuring data sources supplies the raw data required for cleaning, statistical analysis, and the generation of visual charts, ensuring the scripts execute properly to produce actionable insights.

How does spreadsheet automation work for transforming raw data into insights?▼

Spreadsheet automation works for transforming raw data into insights by using Python scripts to initialize and execute data processing. It uses Pandas to clean data, perform statistical analysis, and generate visualizations and reports, turning raw spreadsheets into actionable business intelligence.

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