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name: snowflake-development description: > This skill should be used when the user asks to "optimize Snowflake queries", "analyze Snowflake SQL performance", "size Snowflake warehouses", "review Snowflake data models", or "troubleshoot Snowflake cost issues". license: MIT + Commons Clause metadata: version: 1.0.0 author: borghei category: engineering domain: data-warehouse updated: 2026-04-02 tags: [snowflake, sql, data-warehouse, query-optimization, warehouse-sizing]

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.

Clarify First

Before analyzing or sizing, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Action — analyze / optimize / warehouse-sizing (--action; selects the workflow)
• SQL file or query — the specific query(ies) to optimize (--file; the subject of the analysis)
• Workload type & data volume — ETL / BI / ad-hoc and the GB scale (--workload/--data-volume; drives the warehouse recommendation)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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

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

Warehouse Right-Sizing

1. Identify workload type (ETL, BI, ad-hoc, etc.)
2. Run warehouse-sizing with data volume
3. Review recommendations
4. 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