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write-query

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by anthropic · part of anthropics/knowledge-work-plugins

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…

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🧩 One of 7 skills in the anthropics/knowledge-work-plugins package — works on its own, and pairs well with its siblings.

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…

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by anthropic

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… npx skills add https://github.com/anthropics/knowledge-work-plugins --skill write-query Download ZIPGitHub22.3k

/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.

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:

Copy & paste — that's it
/write-query Count of orders by status for the last 30 days

Complex analysis:

Copy & paste — that's it
/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:

Copy & paste — that's it
/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