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+---
+description: Generate SQL queries from natural language — supports BigQuery, PostgreSQL, MySQL, and more
+argument-hint: "<what you want to know, in plain English>"
+---
+
+# /write-query -- SQL Query Generator
+
+Describe what data you need in plain English and get an optimized SQL query. Supports multiple dialects and can read your schema from uploaded files.
+
+## Invocation
+
+```
+/write-query Show me daily active users for the last 30 days, broken down by plan tier
+/write-query Find users who signed up last month but never completed onboarding
+/write-query [upload a schema diagram] What's the conversion rate from trial to paid by cohort?
+```
+
+## Workflow
+
+### Step 1: Understand the Question
+
+Parse the user's natural language request to identify:
+- What data is being requested (metrics, dimensions, filters)
+- Time range and granularity
+- Grouping and ordering preferences
+- Output expectations (raw data, aggregated, ranked)
+
+### Step 2: Determine Schema
+
+If a schema is available (uploaded diagram, DDL, or description):
+- Map the request to specific tables and columns
+- Identify necessary joins
+
+If no schema is provided:
+- Ask for the database type (BigQuery, PostgreSQL, MySQL, etc.)
+- Infer a reasonable schema from the question and ask the user to confirm
+- Use common SaaS data model conventions as defaults
+
+### Step 3: Generate Query
+
+Apply the **sql-queries** skill:
+
+- Write the SQL query in the correct dialect
+- Optimize for readability and performance
+- Include comments explaining key logic
+- Add CTEs for complex queries to improve readability
+- Handle edge cases (NULLs, timezone considerations, duplicate handling)
+
+### Step 4: Present and Iterate
+
+```
+## SQL Query: [What It Does]
+
+**Dialect**: [BigQuery / PostgreSQL / MySQL / etc.]
+**Tables used**: [list]
+
+### Query
+[SQL code block with comments]
+
+### What This Returns
+[Description of the output: columns, rows, expected result shape]
+
+### Assumptions
+- [Schema assumptions made]
+- [Business logic assumptions]
+
+### Notes
+- [Performance considerations for large datasets]
+- [Edge cases handled or flagged]
+```
+
+Offer:
+- "Want me to **modify this** — add filters, change grouping, extend the time range?"
+- "Should I **create a companion query** for a related metric?"
+- "Want me to **build a dashboard** around this query?"
+- "Need a **cohort analysis** version of this?"
+
+## Notes
+
+- Always include comments in the SQL — PMs share queries with analysts who need to understand intent
+- Default to readable over clever — CTEs over nested subqueries
+- Flag queries that might be slow on large datasets and suggest optimization
+- If the request is ambiguous (e.g., "active users"), ask the user to define the metric precisely
+- Offer to generate the query in multiple dialects if the user is unsure which database they're using