Query MotherDuck

Use this skill when executing SQL queries for analytics, aggregations, transformations, or data exploration against MotherDuck databases.

Prerequisites

• An established MotherDuck connection (or an active MotherDuck MCP server)
• Target database and tables identified

Default Posture

• Write DuckDB SQL, not PostgreSQL SQL, even when using the PG endpoint.
• Always use fully qualified "database"."schema"."table" names.
• Preserve the intended grain of every result set; state the grain before optimizing or materializing a query.
• Filter early, aggregate early, and prefer serving tables or summaries for repeated reads.
• Keep SQL obvious, multi-line, and explicit about grain, filters, and output shape.
• Treat DDL, DML, ATTACH, DETACH, recovery commands such as CREATE SNAPSHOT, ALTER DATABASE ... SET SNAPSHOT, UNDROP DATABASE, and lifecycle commands such as SHUTDOWN as writes. Use the MotherDuck MCP query_rw tool when the user's change request authorizes the write. Ask for confirmation only when the action is destructive, externally visible, or outside the stated scope.
• Tag long-lived integrations with custom_user_agent when the connection path supports it.

Workflow

1. Confirm the actual tables, columns, and grain before writing SQL.
2. Write the query in SQL first, then wrap it in Python or TypeScript only if needed.
3. Use CTEs and DuckDB-native patterns such as GROUP BY ALL, QUALIFY, and arg_max.
4. Check the plan, row count, and shape for pushdown, unnecessary sorts, or repeated raw rescans.
5. Materialize expensive repeated queries into serving tables or light views when warranted.

Open Next

• Read references/QUERY_PLAYBOOK.md for DuckDB query patterns, exploration SQL, performance rules, common analytical shapes, and common mistakes

Related Skills

• motherduck-connect for session setup
• motherduck-duckdb-sql for syntax and function reference
• motherduck-explore for understanding the source schema before writing queries