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data-analysis-sql

Run SQL-driven data profiling, distribution analysis, and summary statistics.

1|Updated Nov 18, 2025
Search Tags:#sql#statistics#data-analysis#eda#data-quality#outlier-detection#query-automation
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-sql --skill data-analysis-sql
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis-sql
Source: https://github.com/pluginagentmarketplace/custom-plugin-sql/tree/main/skills/data-analyst
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-sql --skill data-analysis-sql

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Data analysts often struggle with writing and validating large sets of SQL queries to perform profiling, aggregations, and insight generation. This skill provides a ready-to-use SQL toolkit that standardizes analyses, accelerates discovery, and improves reproducibility.

Core Features & Use Cases

  • Exploratory Data Analysis (EDA) with data profiling, distribution analysis, and summary statistics.
  • Advanced analytics features including outlier detection, percentile calculations, and trend analysis across real-world datasets.
  • Reproducible SQL workflows that can be shared, audited, and extended within teams and projects.

Quick Start

Process a sample dataset with the included SQL snippets to generate a starter analysis report.

Frequently Asked Questions about data-analysis-sql

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

FAQPage Schema
How do I perform exploratory data analysis using SQL?▼

Exploratory data analysis with SQL involves running standardized queries for data profiling, distribution analysis, and summary statistics to understand dataset characteristics and generate reproducible analysis reports.

What is the best way to automate outlier detection in SQL?▼

Automating outlier detection in SQL is best handled by applying standardized statistical query templates that identify anomalies and calculate percentiles across real-world datasets at scale.

Can I use SQL for trend analysis and data profiling at scale?▼

SQL is fully capable of trend analysis and data profiling at scale when using structured query workflows that standardize aggregations and insight generation for reproducible team auditing.

How do I standardize and share reproducible SQL data analysis workflows?▼

Standardize and share reproducible SQL workflows by packaging queries with documented SQL bodies, YAML frontmatter, and optional assets or scripts so teams can audit and extend analyses.

Does SQL data analysis require external dependencies or Python libraries?▼

SQL data analysis can be performed without external dependencies, using standalone SQL scripts and included assets to execute profiling, summary statistics, and outlier detection directly.

Why should I use standardized SQL queries instead of ad-hoc data analysis scripts?▼

Standardized SQL queries prevent the validation struggles of ad-hoc scripts by accelerating data discovery, ensuring reproducibility, and providing auditable workflows across large datasets.

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