/write-query

name: write-query
description: 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.
argument-hint: "<description of what data you need>"

/write-query - Write Optimized SQL

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).

Write a SQL query from a natural language description, optimized for your specific SQL dialect and following best practices.

Usage

/write-query <description of what data you need>

Workflow

1. Understand the Request

Parse the user's description to identify:

• **Output columns**: What fields should the result include?
• **Filters**: What conditions limit the data (time ranges, segments, statuses)?
• **Aggregations**: Are there GROUP BY operations, counts, sums, averages?
• **Joins**: Does this require combining multiple tables?
• **Ordering**: How should results be sorted?
• **Limits**: Is there a top-N or sample requirement?

2. Determine SQL Dialect

If the user's SQL dialect is not already known, ask which they use:

• **PostgreSQL** (including Aurora, RDS, Supabase, Neon)
• **Snowflake**
• **BigQuery** (Google Cloud)
• **Redshift** (Amazon)
• **Databricks SQL**
• **MySQL** (including Aurora MySQL, PlanetScale)
• **SQL Server** (Microsoft)
• **DuckDB**
• **SQLite**
• **Other** (ask for specifics)

Remember the dialect for future queries in the same session.

3. Discover Schema (If Warehouse Connected)

If a data warehouse MCP server is connected:

1. Search for relevant tables based on the user's description 2. Inspect column names, types, and relationships 3. Check for partitioning or clustering keys that affect performance 4. Look for pre-built views or materialized views that might simplify the query

4. Write the Query

Follow these best practices:

**Structure:**

• Use CTEs (WITH clauses) for readability when queries have multiple logical steps
• One CTE per logical transformation or data source
• Name CTEs descriptively (e.g., `daily_signups`, `active_users`, `revenue_by_product`)

**Performance:**

• Never use `SELECT *` in production queries -- specify only needed columns
• Filter early (push WHERE clauses as close to the base tables as possible)
• Use partition filters when available (especially date partitions)
• Prefer `EXISTS` over `IN` for subqueries with large result sets
• Use appropriate JOIN types (don't use LEFT JOIN when INNER JOIN is correct)
• Avoid correlated subqueries when a JOIN or window function works
• Be mindful of exploding joins (many-to-many)

**Readability:**

• Add comments explaining the "why" for non-obvious logic
• Use consistent indentation and formatting
• Alias tables with meaningful short names (not just `a`, `b`, `c`)
• Put each major clause on its own line

**Dialect-specific optimizations:**

• Apply dialect-specific syntax and functions (see `sql-queries` skill for details)
• Use dialect-appropriate date functions, string functions, and window syntax
• Note any dialect-specific performance features (e.g., Snowflake clustering, BigQuery partitioning)

5. Present the Query

Provide:

1. **The complete query** in a SQL code block with syntax highlighting 2. **Brief explanation** of what each CTE or section does 3. **Performance notes** if relevant (expected cost, partition usage, potential bottlenecks) 4. **Modification suggestions** -- how to adjust for common variations (different time range, different granularity, additional filters)

6. Offer to Execute

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.

Examples

**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.

Tips

• Mention your SQL dialect upfront to get the right syntax immediately
• If you know the table names, include them -- otherwise Claude will help you find them
• Specify if you need the query to be idempotent (safe to re-run) or one-time
• For recurring queries, mention if it should be parameterized for date ranges