data-analyst-workflow

Analyze CSV datasets through cleaning, statistics, and anomaly detection.

1|Updated Mar 16, 2026

Search Tags:#analyze #data-cleaning #descriptive-statistics #exploratory-data-analysis #csv-analysis

Ready to run

npx skills add https://github.com/00PrabalK00/claude-skills --skill data-analyst-workflow

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analyst-workflow
Source: https://github.com/00PrabalK00/claude-skills/tree/main/skills/data-analyst-workflow
Command: npx skills add https://github.com/00PrabalK00/claude-skills --skill data-analyst-workflow

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates and documents the end-to-end data-analytic workflow from raw CSV data to clear, plain-language takeaways, reducing manual guesswork and improving reproducibility.

Core Features & Use Cases

Inspect schema, data types, missingness, duplicates, and obvious quality issues before analyzing outcomes.
Clean or normalize data and document every meaningful assumption.
Compute descriptive statistics and comparisons that answer the actual question.
Detect anomalies, outliers, seasonality, and segmentation effects before drawing conclusions.
Present findings in plain language with tables or charts only when they improve understanding.
Use cases include CSV analyses for KPI checks, recurring analytics workflows, and exploratory data work.

Quick Start

Analyze a CSV dataset by inspecting schema, cleaning data, computing descriptive statistics, and summarizing actionable takeaways in plain language.

Frequently Asked Questions about data-analyst-workflow

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

FAQPage Schema

How do I analyze a CSV dataset to get plain-English insights?▼

To analyze a CSV dataset, this workflow inspects the schema, cleans data while documenting assumptions, computes descriptive statistics, detects anomalies, and summarizes actionable takeaways in plain language.

What is the best way to clean raw data and document assumptions for reproducible analytics?▼

The best way to clean raw data for reproducible analytics is to normalize values and document every meaningful assumption during the process, ensuring downstream statistical computation and interpretation remain transparent.

How does exploratory data analysis detect outliers and seasonality before drawing conclusions?▼

Exploratory data analysis detects outliers and seasonality by inspecting schema and data types first, then computing descriptive statistics and comparisons to identify segmentation effects before interpreting outcomes.

Can I use this for recurring KPI checks and exploratory data work on CSV files?▼

Yes, you can use this workflow for recurring KPI checks and exploratory data work because it is explicitly scoped for CSV analyses, automating data cleaning, anomaly detection, and statistical computation.

Why does descriptive statistics need schema inspection and missingness checks first?▼

Descriptive statistics require schema inspection and missingness checks first because identifying data types, duplicates, and obvious quality issues ensures reliable, actionable insights rather than skewed statistical computations.

What are the limitations of automating a data-cleaning workflow for CSV analysis?▼

A limitation of automating a data-cleaning workflow for CSV analysis is that it requires documented assumptions for every meaningful change, meaning heavily unstructured data or non-CSV formats fall outside its intended scope.