Turn a plain-language data request into SQL tailored to your database. Build queries that combine tables, filter records, and calculate summaries, with explanations of each step, performance considerations, and suggestions for adjusting dates or other conditions.
Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.
Review the instructions the agent will receive before you install.
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Write a SQL query from a natural language description, optimized for your specific SQL dialect and following best practices.
/write-query <description of what data you need>
Parse the user's description to identify:
If the user's SQL dialect is not already known, ask which they use:
Remember the dialect for future queries in the same session.
If a data warehouse MCP server is connected:
Follow these best practices:
Structure:
daily_signups, active_users, revenue_by_product)Performance:
SELECT * in production queries -- specify only needed columnsEXISTS over IN for subqueries with large result setsReadability:
a, b, c)Dialect-specific optimizations:
sql-queries skill for details)Provide:
If a data warehouse is connected, offer to run the query and analyze the results. If the user wants to run it themselves, the query is ready to copy-paste.
Simple aggregation:
/write-query Count of orders by status for the last 30 days
Complex analysis:
/write-query Cohort retention analysis -- group users by their signup month, then show what percentage are still active (had at least one event) at 1, 3, 6, and 12 months after signup
Performance-critical:
/write-query We have a 500M row events table partitioned by date. Find the top 100 users by event count in the last 7 days with their most recent event type.
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