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:

Search for relevant tables based on the user's description
Inspect column names, types, and relationships
Check for partitioning or clustering keys that affect performance
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:

The complete query in a SQL code block with syntax highlighting
Brief explanation of what each CTE or section does
Performance notes if relevant (expected cost, partition usage, potential bottlenecks)
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

write-query

/write-query - Write Optimized SQL

Usage

Workflow

1. Understand the Request