Can this skill help identify data quality issues?

Yes, it includes dedicated modules for detecting invalid values, missing data, impossible ranges, and duplicate records across your tables.

How does this improve Claude's data analysis capabilities?

It gives Claude a domain-specific framework for choosing the right analytical approach, ensuring queries are syntactically correct and follow data science best practices.

Does it support complex statistical modeling?

It provides templates for essential statistical analysis, including percentile distributions, standard deviations, Z-score outlier detection, and correlation coefficients.

What databases are supported by these SQL patterns?

The skill utilizes standard SQL patterns compatible with major relational database systems including PostgreSQL, MySQL, BigQuery, and Snowflake.

Is cohort analysis included?

Yes, the skill contains specialized patterns for user cohort analysis and retention rate calculations over time.

SQL Data Analyst

Name: SQL Data Analyst
Author: pluginagentmarketplace

bypluginagentmarketplace

Ciencia de Datos y ML

Performs advanced data analysis and business intelligence using specialized SQL patterns for statistical and exploratory insights.

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The SQL Data Analyst skill empowers Claude to execute sophisticated data science tasks directly within relational databases. It provides a comprehensive library of patterns for exploratory data analysis (EDA), advanced statistical functions like Z-scores and percentiles, and business-critical modeling such as cohort analysis and YoY growth tracking. This skill is ideal for developers and analysts who need to transform raw data into actionable insights while maintaining high standards for data quality and query efficiency.

Características Principales

Statistical analysis including outlier detection via Z-score and IQR methods
Trend analysis using moving averages and window functions
Advanced comparative analysis for YoY growth and user cohort retention
Data quality validation and duplicate detection logic
Exploratory Data Analysis (EDA) and automated data profiling patterns
0 GitHub stars

Casos de Uso

Generating automated business intelligence reports from raw database tables
Auditing large datasets for data quality issues, outliers, and inconsistencies
Performing deep-dive customer behavior analysis through cohort and retention modeling

Acerca de

Características Principales

Statistical analysis including outlier detection via Z-score and IQR methods
Trend analysis using moving averages and window functions
Advanced comparative analysis for YoY growth and user cohort retention
Data quality validation and duplicate detection logic
Exploratory Data Analysis (EDA) and automated data profiling patterns
0 GitHub stars

Casos de Uso

Generating automated business intelligence reports from raw database tables
Auditing large datasets for data quality issues, outliers, and inconsistencies
Performing deep-dive customer behavior analysis through cohort and retention modeling