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data-analyzer

Profile dataset structure, completeness, and statistical validity with pandas and numpy.

Updated May 5, 2026
Search Tags:#data quality#data analysis#profiling#pandas#statistics#eda
One-click install
npx skills add https://github.com/wachawo/claude-skills --skill data-analyzer-wachawo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyzer
Source: https://github.com/wachawo/claude-skills/tree/main/skills/data-analyzer
Command: npx skills add https://github.com/wachawo/claude-skills --skill data-analyzer-wachawo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This skill addresses the uncertainty and risk associated with working with new or complex datasets by providing a standardized, rigorous framework for profiling data quality and structure before analysis begins.

Core Features & Use Cases

  • Automated Profiling: Quickly generate structural overviews, null profiles, and distribution summaries to identify data health issues.
  • Statistical Validation: Detect outliers and multicollinearity using robust statistical methods like IQR, z-score, and correlation matrices.
  • Standardized Reporting: Ensure consistent documentation of findings and quality checks using pre-built templates and checklists.
  • Use Case: Before training a machine learning model, use this skill to verify that your input features are clean, free of unexpected nulls, and statistically sound.

Quick Start

Activate the data-analyzer skill to perform a full structural and quality profile on the dataset located at data/sales_records.csv.

Frequently Asked Questions about data-analyzer

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

FAQPage Schema
How do I perform exploratory data analysis on a CSV file using pandas?▼

You can perform exploratory data analysis by running automated profiling scripts to generate structural overviews, null profiles, and distribution summaries for tabular datasets using pandas and numpy.

What is the best way to detect outliers and multicollinearity in a dataset before machine learning?▼

Detect outliers and multicollinearity by applying robust statistical validation methods like IQR, z-score, and correlation matrices to verify that input features are statistically sound and free of unexpected nulls.

How do I profile data quality and null values in tabular datasets?▼

Profile data quality by executing diagnostic scripts that assess dataset completeness and structure, generating comprehensive quality reports that document null profiles and data health issues.

Do I need numpy and pandas installed to run automated data profiling scripts?▼

Yes, you need pandas and numpy installed, as the data profiling scripts require these dependencies to execute diagnostic commands and generate statistical validity checks across tabular formats.

Can I use this approach to standardize data quality reporting for new datasets?▼

Yes, you can standardize data quality reporting by using pre-built templates and checklists to ensure consistent documentation of structural findings, completeness, and statistical validity checks.

What statistical methods are used for outlier detection in exploratory data analysis?▼

The exploratory data analysis uses IQR and z-score statistical methods to detect outliers, alongside correlation matrices to identify multicollinearity and ensure the statistical validity of input features.

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