redshift-warehouse

Name: redshift-warehouse
Availability: InStock
Author: Stream

Query Redshift staging and raw tables, debug dbt models, and troubleshoot ETL pipelines.

Updated Feb 24, 2026

Search Tags:#data quality #dbt #sql #etl #data warehouse #redshift #querying

Ready to run

npx skills add https://github.com/Stream-claims/stream-engineering --skill redshift-warehouse

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: redshift-warehouse
Source: https://github.com/Stream-claims/stream-engineering/tree/main/skills/redshift-warehouse
Command: npx skills add https://github.com/Stream-claims/stream-engineering --skill redshift-warehouse

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a robust interface for querying, debugging, and troubleshooting issues within the Redshift data warehouse, ensuring data integrity and efficient data operations.

Core Features & Use Cases

Data Querying: Execute SQL queries against Redshift staging and raw tables.
dbt Model Debugging: Troubleshoot failures and data quality issues in dbt models.
ETL Pipeline Monitoring: Inspect data loading processes and verify ETL results.
Schema Exploration: Understand the structure and contents of the data warehouse.
Use Case: A data analyst needs to investigate why a specific dbt model is failing to load data correctly. They can use this skill to query the raw data, check the staging tables for expected formats, and examine dbt logs for errors.

Quick Start

Use the redshift-warehouse skill to query the all_case_data table in the staging environment and retrieve the 10 most recent entries.

Frequently Asked Questions about redshift-warehouse

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

FAQPage Schema

How do I query Redshift staging tables to debug dbt model failures?▼

Debugging dbt models in Redshift involves querying staging and raw tables to inspect data formats and verify data quality. By executing SQL queries against these tables, you can identify schema mismatches or missing records causing the dbt pipeline to fail.

What is the best way to monitor ETL pipelines and verify data loading in Redshift?▼

Monitoring ETL pipelines in Redshift requires inspecting data loading processes and verifying ETL results through direct SQL querying. This approach lets you check staging and raw tables to confirm that records are loading correctly and maintaining expected data integrity.

Do I need a VPN to use the Redshift Data API for querying data warehouses?▼

Using the Redshift Data API does not strictly require a VPN as it provides direct connectivity options for querying data. The skill utilizes Redshift Data API or VPN connections alongside AWS profiles and Secrets Manager for secure credential management during warehouse operations.

Can I explore Redshift database schemas and check data quality without direct cluster access?▼

Yes, you can explore Redshift schemas and check data quality using the Redshift Data API with AWS profiles and Secrets Manager. This method bypasses direct cluster access by securely managing credentials and allowing remote SQL querying of staging and raw tables.

Why does my dbt model fail to load data correctly in Redshift staging environments?▼

dbt models fail to load data correctly in Redshift staging environments due to schema mismatches, unexpected raw data formats, or ETL pipeline errors. Querying the staging tables and examining dbt logs helps isolate whether the issue is data quality or pipeline execution.

How to manage Redshift credentials securely when troubleshooting ETL pipelines?▼

Secure Redshift credential management for ETL troubleshooting utilizes AWS profiles and Secrets Manager to authenticate connections. This ensures secure access when querying the data warehouse via VPN or the Redshift Data API to inspect pipeline results.