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Writes optimized SQL queries from natural language for dialects like PostgreSQL, Snowflake, BigQuery, MySQL. Builds multi-CTE queries with joins, aggregations; optimizes performance on large tables.
npx claudepluginhub anthropics/knowledge-work-plugins --plugin dataHow this skill is triggered — by the user, by Claude, or both
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/data:write-queryThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
Writes correct, performant SQL for data warehouses like Snowflake, BigQuery, Databricks, PostgreSQL. Optimizes queries, translates dialects, uses CTEs, window functions, aggregations.
Generates optimized BigQuery SQL from natural language descriptions, converts queries from other SQL dialects to BigQuery syntax, and applies best practices like avoiding SELECT * and using ARRAY_AGG for latest records.
Generates optimized SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, Snowflake; reads schemas from files or docs. For data exploration, reports, and business analysis.
Share bugs, ideas, or general feedback.
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.