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Translate SQL queries between database dialects (Snowflake, BigQuery, PostgreSQL, MySQL, etc.)
| name | sql-translate |
| description | Translate SQL queries between database dialects (Snowflake, BigQuery, PostgreSQL, MySQL, etc.) |
Agent: builder or migrator (may write translated SQL to files) Tools used: sql_translate, read, write, altimate_core_validate
Translate SQL queries from one database dialect to another using sqlglot's transpilation engine.
Determine source and target dialects — If the user did not specify both dialects, ask which source and target dialects to use. Common dialects: snowflake, bigquery, postgres, mysql, tsql, hive, spark, databricks, redshift, duckdb.
Get the SQL to translate — Either:
read)Call sql_translate with:
sql: The SQL query textsource_dialect: The source dialecttarget_dialect: The target dialectReview the result:
success is true, present the translated SQLwarnings, explain each one and what may need manual adjustmentsuccess is false, explain the error and suggest fixesFormat the output showing:
Offer next steps if applicable:
altimate_core_validate on the translated SQL to verify syntaxThe user invokes this skill with optional dialect and SQL arguments:
/sql-translate — Interactive: ask for source dialect, target dialect, and SQL/sql-translate snowflake postgres — Translate from Snowflake to PostgreSQL (will ask for SQL)/sql-translate snowflake postgres SELECT DATEADD(day, 7, CURRENT_TIMESTAMP()) — Full inline translation| Dialect | Key |
|---|---|
| Snowflake | snowflake |
| BigQuery | bigquery |
| PostgreSQL | postgres |
| MySQL | mysql |
| SQL Server | tsql |
| Hive | hive |
| Spark SQL | spark |
| Databricks | databricks |
| Redshift | redshift |
| DuckDB | duckdb |
| SQLite | sqlite |
| Oracle | oracle |
| Trino/Presto | trino / presto |
Use the tools: sql_translate, read, write, altimate_core_validate.
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REQUIRED before writing or modifying ANY dbt model. Invoke this skill FIRST whenever a task says "create", "build", "add", "modify", "update", "fix", or "refactor" a dbt model, staging file, mart, incremental, or snapshot. Skipping this skill is the leading cause of silent-correctness bugs — models that compile and `dbt build` cleanly but produce wrong values. It contains the patterns that prevent the most common such bugs encountered in real dbt projects: • Incremental high-water marks (`>=` vs `>` ties → silent row dropout) • Snapshot strategy selection (timestamp vs check, `unique_key` choice) • `LEFT JOIN + COUNT(*)` phantom rows from unmatched parents • Type harmonization in `COALESCE` / `CASE` / `UNION` legs • Date-spine completeness (every period present, even empty ones) • Off-by-one window boundaries (`BETWEEN d - (N-1) AND d` for N-wide) • Uniqueness enforcement when schema implies a key • Window-function `LIMIT` with deterministic tiebreaker • Verifying transformation correctness with dbt unit te
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