snowflake-development
FeaturedComprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening.
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Quality Score: 99/100
Skill Content
Details
- Author
- sickn33
- Repository
- sickn33/antigravity-awesome-skills
- Created
- 4 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
snowflake-development
Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors.
snowflake-known-pitfalls
Identify and avoid Snowflake anti-patterns and common mistakes in SQL, warehouse management, data loading, and access control. Use when reviewing Snowflake configurations, onboarding new users, or auditing existing Snowflake deployments for best practices. Trigger with phrases like "snowflake mistakes", "snowflake anti-patterns", "snowflake pitfalls", "snowflake what not to do", "snowflake code review".
sql-queries
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
snowflake-architecture-variants
Choose and implement Snowflake architecture blueprints: data lakehouse, data mesh, data sharing, and Snowpark-native patterns for different scales. Use when designing Snowflake data platforms, choosing between architectures, or implementing data sharing and Snowpark patterns. Trigger with phrases like "snowflake architecture", "snowflake lakehouse", "snowflake data mesh", "snowflake data sharing", "snowflake Snowpark".
sql-queries
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.