Claude-Skills snowflake-development

install

source · Clone the upstream repo

git clone https://github.com/borghei/Claude-Skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/borghei/Claude-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/engineering/snowflake-development" ~/.claude/skills/borghei-claude-skills-snowflake-development && rm -rf "$T"

manifest: engineering/snowflake-development/SKILL.md

source content

Snowflake Development

Category: Engineering Domain: Data Warehouse

Overview

The Snowflake Development skill provides tools for analyzing and optimizing Snowflake SQL queries, recommending warehouse sizing, and enforcing Snowflake-specific best practices. Helps data engineers reduce costs and improve query performance.

Quick Start

# Analyze a Snowflake SQL file for optimization opportunities
python scripts/snowflake_query_helper.py --file queries.sql --action analyze

# Get warehouse sizing recommendations
python scripts/snowflake_query_helper.py --action warehouse-sizing --workload "etl" --data-volume "500GB"

# Optimize a specific query
python scripts/snowflake_query_helper.py --file slow_query.sql --action optimize

Tools Overview

Tool Purpose Key Flags

snowflake_query_helper.py

Analyze, optimize Snowflake SQL and recommend warehouse sizes

--file

--action

--workload

--data-volume

Workflows

Query Performance Optimization

Collect slow queries from query history
Run analyzer to identify optimization opportunities
Apply recommended changes
Compare before/after execution plans

Warehouse Right-Sizing

Identify workload type (ETL, BI, ad-hoc, etc.)
Run warehouse-sizing with data volume
Review recommendations
Implement multi-cluster settings if applicable

Reference Documentation

Snowflake Best Practices - Query patterns, warehouse management, cost optimization

Common Patterns

Cost Reduction

Right-size warehouses (don't use XL for small queries)
Set auto-suspend to 60 seconds for ad-hoc warehouses
Use materialized views for frequently accessed aggregations
Partition large tables with clustering keys
Avoid SELECT * in production queries