sqlx-patterns

Name: sqlx-patterns
Availability: InStock
Author: Expanly

Standardizes SQL/SQLX patterns for BigQuery and Dataform data transformations.

Updated Aug 27, 2026

Search Tags:#patterns #sql #deduplication #naming-conventions #sqlx #incremental #dataform

Ready to run

npx skills add https://github.com/Expanly/expanly-claude-code-agents --skill sqlx-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sqlx-patterns
Source: https://github.com/Expanly/expanly-claude-code-agents/tree/main/plugins/expanly-scoring-model/skills/sqlx-patterns
Command: npx skills add https://github.com/Expanly/expanly-claude-code-agents --skill sqlx-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill standardizes SQL/SQLX coding patterns to ensure consistent, reliable data transformations.

Core Features & Use Cases

SQLX File Structure guidance for views, incremental tables, and base tables.
Dependency Patterns and Reference Resolution for Dataform projects.
Comprehensive patterns for Safe Operations, ID Normalization, Deduplication, CTEs, Window Functions, JSON handling, and Naming Conventions.
Real-world use case: Apply patterns to BigQuery/Dataform scoring pipelines to enforce consistency and governance.

Quick Start

Consult the documented patterns and implement them in your Dataform SQLX files to standardize transformations.

Frequently Asked Questions about sqlx-patterns

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

FAQPage Schema

What are the best SQLX patterns for reliable Dataform pipelines?▼

The best SQLX patterns for reliable Dataform pipelines standardize view, incremental, and table configurations to ensure consistent transformations. They enforce safe operations, dependency references, and naming conventions across your projects.

How do I handle deduplication and ID normalization in BigQuery transformations?▼

To handle deduplication and ID normalization in BigQuery transformations, apply standardized SQL patterns using CTEs and window functions. This ensures consistent identifier formatting and removes duplicate records reliably across your data models.

How do I structure incremental tables and views in Dataform SQLX files?▼

To structure incremental tables and views in Dataform SQLX files, apply standardized file structure patterns that define specific configurations for each type. This approach enforces dependency resolution and consistent reference handling across your pipeline.

Does this SQLX pattern skill work for BigQuery scoring pipelines?▼

Yes, these SQLX patterns work for BigQuery scoring pipelines by applying governance and consistency rules to your transformations. They cover safe operations and performance considerations specifically for BigQuery and Dataform environments.

When should I use CTEs and window functions in SQLX transformations?▼

You should use CTEs and window functions in SQLX transformations when handling complex logic like deduplication and JSON handling. These patterns ensure safe operations and maintain performance considerations within your BigQuery data models.