What is the benefit of the five-phase process?

The structure ensures that analytical questions are fully understood and logic is verified before final execution, reducing the need for debugging and ensuring results are reproducible.

How does this skill help with complex joins?

It includes a dedicated 'Design Query Logic' phase that identifies required tables and analyzes join cardinality to prevent 'row explosion' issues before any SQL is written.

Does it handle data validation?

Yes, it includes a 'Verify Results' phase with a checklist for row counts, aggregate reasonableness, NULL value checks, and manual spot-checks.

Can I use this for different SQL dialects like PostgreSQL or MySQL?

Yes, the skill provides patterns for various dialects, including SQLite, PostgreSQL, and MySQL, while focusing on universal best practices for query structure.

Analytical SQL Writing

Name: Analytical SQL Writing
Author: tilmon-engineering

bytilmon-engineering

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数据库管理

Guides systematic SQL query development for data analysis, ensuring accuracy and reproducibility through a structured five-phase process.

关于

The Analytical SQL Writing skill provides a comprehensive framework for developing robust SQL queries within DataPeeker analysis sessions. It steers Claude through a disciplined workflow that covers clarifying analytical questions, designing logical table joins, writing clean and commented SQL, and performing rigorous verification. By implementing these patterns, users can avoid common data pitfalls like row explosions, incorrect aggregations, and edge-case errors, making it an essential tool for turning raw data into reliable business intelligence.

主要功能

Five-phase systematic query development workflow
Best-practice SQL patterns including CTEs and explicit NULL handling
Standardized templates for documenting analytical requirements
2 GitHub stars
Advanced join strategy planning to prevent data duplication
Automated verification checklists for result accuracy

使用场景

Designing complex multi-table joins for large relational datasets
Debugging and verifying the accuracy of analytical aggregates and metrics
Translating vague business questions into precise, answerable SQL requirements

关于

主要功能

Five-phase systematic query development workflow
Best-practice SQL patterns including CTEs and explicit NULL handling
Standardized templates for documenting analytical requirements
2 GitHub stars
Advanced join strategy planning to prevent data duplication
Automated verification checklists for result accuracy

使用场景

Designing complex multi-table joins for large relational datasets
Debugging and verifying the accuracy of analytical aggregates and metrics
Translating vague business questions into precise, answerable SQL requirements