write-query

write-query

/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

Weekly Installs293Repositoryanthropics/know…-pluginsGitHub Stars10.6KFirst SeenMar 13, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex279gemini-cli277opencode276cursor276github-copilot275amp275