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data-analyst

Analyze business data with SQL and Python to generate dashboard-ready visualizations and insights.

1|Updated Apr 23, 2026
Search Tags:#storytelling#dashboards#data#sql#visualization#analyze#bi
One-click install
npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill data-analyst-mtsatryan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/mtsatryan/openclaw-ai-agents/tree/main/data-analyst
Command: npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill data-analyst-mtsatryan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data teams struggle to transform raw data into clear, actionable business insights while maintaining governance, accurate metrics, and scalable reporting.

Core Features & Use Cases

  • Data discovery and KPI mapping through SQL, Python analytics, and BI tools.
  • Dashboard development and data storytelling to communicate insights to stakeholders.
  • Data quality checks, statistical analysis, and cohort/retention analyses for business decisions.

Quick Start

Instruct the system to load the relevant business context and data sources, run an initial analysis, and deliver a reproducible BI insight with a dashboard-ready visualization.

Frequently Asked Questions about data-analyst

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

FAQPage Schema
How do I turn raw business data into actionable insights and visualizations?▼

To turn raw business data into actionable insights, you can apply statistical analysis and SQL queries to generate dashboard-ready visualizations. This process ensures accurate KPI mapping and reproducible BI reporting for decision-makers.

What is data storytelling and how does it help dashboard development?▼

Data storytelling is the practice of communicating analytical findings to stakeholders through clear visualizations. It enhances dashboard development by translating complex SQL data and cohort analyses into actionable business insights.

How do I perform data quality checks and KPI mapping for BI projects?▼

You perform data quality checks and KPI mapping by running data discovery processes using SQL and Python analytics. This ensures accurate metrics and governs your BI projects before generating final statistical analyses.

Can I use SQL and Python analytics to run cohort and retention analyses?▼

Yes, you can use SQL and Python analytics to execute cohort and retention analyses. These statistical methods track business decisions over time while maintaining strong guardrails and reproducible analytical workflows.

What is the best way to generate reproducible BI insights for stakeholders?▼

The best way to generate reproducible BI insights is to load business context and data sources, run an initial analysis, and deliver a dashboard-ready visualization. This ensures governed, accurate metrics for decision-makers.

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