Does this skill require manual SQL knowledge?

While it helps, the skill automates schema discovery and uses a pattern cache to suggest proven query strategies for common business questions based on your specific database structure.

How does the kernel management work?

The skill uses the 'uv' Python tool to manage a dedicated kernel that starts automatically on the first query. This allows for execution of SQL and post-processing of data using libraries like Polars and Pandas.

Can it handle very large datasets?

Yes, the skill includes specialized guardrails for tables with millions of rows, enforcing date filtering and providing tips to avoid expensive joins on initial exploration queries.

Which data warehouses are supported?

The skill supports common dialects including Snowflake, PostgreSQL, and BigQuery, with built-in logic to handle specific syntax differences in dates and casing.

SQL Data Warehouse Analyst

Name: SQL Data Warehouse Analyst
Author: astronomer

byastronomer

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데이터 과학 및 ML

Queries data warehouses and analyzes business metrics to provide actionable insights directly within Claude Code.

소개

This skill empowers Claude to act as a sophisticated data analyst by connecting to various data warehouses to answer complex business questions. It leverages a unique dual-layer caching system—Concepts for table mapping and Patterns for query strategies—ensuring efficient, accurate, and repeatable SQL execution. The skill automatically manages a dedicated Python kernel for processing results, handles schema discovery across your codebase, and provides specific guardrails for querying massive datasets to ensure performance and cost-efficiency.

주요 기능

Multi-dialect SQL support for Snowflake, PostgreSQL, and BigQuery
Safety-first querying for large-scale data with automatic filtering recommendations
Pattern and Concept caching to reuse proven query strategies and table mappings
Automated Python kernel management for SQL execution and data processing via Polars or Pandas
Intelligent table discovery through codebase analysis (Grep/SQL files) and schema lookups
77 GitHub stars

사용 사례

Answering complex business questions like 'who uses feature X' or 'what is the count of Y'
Discovering and mapping data concepts to physical warehouse table definitions
Performing trend analysis and metrics reporting on data warehouse tables

소개

주요 기능

Multi-dialect SQL support for Snowflake, PostgreSQL, and BigQuery
Safety-first querying for large-scale data with automatic filtering recommendations
Pattern and Concept caching to reuse proven query strategies and table mappings
Automated Python kernel management for SQL execution and data processing via Polars or Pandas
Intelligent table discovery through codebase analysis (Grep/SQL files) and schema lookups
77 GitHub stars

사용 사례

Answering complex business questions like 'who uses feature X' or 'what is the count of Y'
Discovering and mapping data concepts to physical warehouse table definitions
Performing trend analysis and metrics reporting on data warehouse tables