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Translate SQL queries between database dialects (Snowflake, BigQuery, PostgreSQL, MySQL, etc.)
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Basé sur la classification professionnelle SOC
| 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.
Analyze and optimize SQL queries for better performance
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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
REQUIRED after building or modifying ANY dbt model that has columns declared in `schema.yml` / `_models.yml`. Run `altimate-dbt schema-verify --model <name>` to diff actual columns against the spec, and treat any `mismatch` verdict as "not done." The most common reason "the build is green but the tests still fail" is that the model produces the right *data values* in the wrong *column shape* — extra columns, missing columns, wrong order, wrong types. Many dbt equality tests grade the column tuple `(name, type, position)` exactly, and the agent's prior bias is to add "helpful" extras (`p1`/`p2`/`p3` rank breakdowns, name-resolved variants, lineage metadata) or reorder columns "more logically." Both break the contract. This skill enforces the mechanical check that catches those bugs before declaring done. Use it before declaring any model task complete.
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