write-query

SKILL.md

/write-query - Write Optimized SQL

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.

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:

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