Analyze SQL and dbt models for anti-patterns and performance bottlenecks.
npx skills add https://github.com/clawdata/clawdata --skill sql-reviewer
Please help me install this Agent Skill. Skill: sql-reviewer Source: https://github.com/clawdata/clawdata/tree/main/skills/sql-reviewer Command: npx skills add https://github.com/clawdata/clawdata --skill sql-reviewer
Review SQL queries and dbt models for best practices, performance, and anti-patterns.
Run sql-reviewer on your SQL and dbt models to generate a structured review report.
High-intent search queries and answers about installing and using this skill.
SQL review analyzes queries and dbt models to identify anti-patterns, performance bottlenecks, and quality gaps. It detects issues like SELECT * usage, missing WHERE clauses on large tables, and improper materialisation, providing structured feedback with severity and line references.
Linting dbt models checks ref()/source() usage and validates materialisation recommendations to enforce best practices. It applies static analysis to identify quality gaps and outputs actionable feedback with line references and suggested fixes for production readiness.
Static analysis catches Cartesian joins, missing WHERE clauses on large tables, and SELECT * usage during SQL review. It scans queries to detect these common issues, generating a structured report with severity levels, issue descriptions, and fixes to improve query quality.
SQL review integrates into code review workflows, CI checks, and data pipeline design reviews for data teams. It enforces checks against anti-patterns and outputs a structured review containing severity, line references, issue descriptions, and suggested fixes to ensure production readiness.
Dbt model review flags improper materialisation and SELECT * usage because they are anti-patterns that degrade data pipeline performance and quality. The review enforces best practices by identifying these gaps and providing actionable feedback with line references and fixes.
SQL review focuses on static analysis of SQL and dbt models to identify anti-patterns, performance bottlenecks, and quality gaps. It enforces checks for SELECT * usage, missing WHERE clauses, and improper materialisation, outputting a structured review with severity and fixes.
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