data-write-query

name	data-write-query
description	Write optimized SQL for your dialect with best practices

Write SQL Queries

If you see unfamiliar placeholders or need to check which tools are connected, please ask about available integrations.

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

Usage

You can ask to write a SQL query (e.g., "Write a query to count orders" or "Get the top 10 users").

Arguments

• description — Description of what data you need and the business logic

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 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
• 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.

Tips

• Mention your SQL dialect upfront to get the right syntax immediately
• If you know the table names, include them -- otherwise I 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