Agent skill · data analytics · borghei
snowflake-development
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".
Why this skill is useful
Adds specific scripts for analyzing and optimizing Snowflake SQL queries that are not commonly known or available in public documentation.
What it needs
Requires python installed locally. About 2k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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 Tools Overview Tool Purpose Key Flags ------ --------- ----------- snowflakequeryhelper.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
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/snowflake-development