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data-analyst

Analyze structured CSV, JSON, and tabular datasets for findings and outliers.

2|1|Updated May 17, 2026
Search Tags:#insights#json#data analysis#statistics#csv#outliers#exploratory analysis
One-click install
npx skills add https://github.com/rakibulism/agent-skills-os --skill data-analyst-rakibulism
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/rakibulism/agent-skills-os/tree/main/skills/data-analyst
Command: npx skills add https://github.com/rakibulism/agent-skills-os --skill data-analyst-rakibulism

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you make sense of structured data by turning CSV, JSON, and tables into clear, defensible findings. It is designed for exploratory analysis and answering specific questions without hiding assumptions or overstating certainty.

Core Features & Use Cases

  • Data profiling: Summarizes shape, missingness, ranges, distributions, and outliers before deeper analysis.
  • Question-driven analysis: Answers targeted questions with explicit operational definitions and caveats.
  • Statistical discipline: Uses appropriate summaries for skewed data, distinguishes correlation from causation, and flags surprising results.
  • Practical reporting: Produces findings, caveats, and next steps in a format that supports decision-making.
  • Use case: Analyze a sales export to identify unusually strong or weak regions, then explain the evidence and limitations clearly.

Quick Start

Ask the data analyst skill to analyze your CSV or JSON dataset and answer your specific business question with findings, caveats, and recommended next steps.

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 to find outliers and summarize distributions?▼

To analyze a CSV file, this Skill profiles the data shape, identifies outliers, and reviews distributions. It applies appropriate summary statistics to produce defensible findings, explicit assumptions, and caveats for your exploratory analysis.

What is the best way to perform question-driven data analysis on JSON datasets?▼

Question-driven data analysis on JSON datasets requires explicit operational definitions. This Skill answers your targeted business questions by applying statistical discipline, distinguishing correlation from causation, and flagging surprising results with practical reporting.

How do I detect outliers in tabular datasets without overstating statistical certainty?▼

Detecting outliers in tabular datasets requires appropriate summaries for skewed data. This Skill performs distribution review and outlier detection while explicitly stating assumptions, caveats, and limitations to prevent overstating certainty in your findings.

Does this data analysis approach work for exploratory profiling of business and product workflows?▼

Exploratory profiling applies directly to business, product, and research workflows. This Skill summarizes missingness, ranges, and distributions from structured data, then outputs findings and recommended next steps formatted for decision-making.

Can I get reproducible code when running statistical tests on structured data?▼

Reproducible code is provided when computation is needed for statistical tests. This Skill ensures your question-driven analysis and relationship testing across structured datasets remain transparent, defensible, and verifiable.

Why does my data analysis lack clear caveats and next steps for decision-making?▼

Data analysis lacks clear caveats when statistical discipline is absent. This Skill enforces explicit assumptions, flags surprising results, and delivers practical reporting with findings, limitations, and next steps to support decision-making.

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