sql-query-generation

# SQL Query Generation This skill enables an AI agent to translate natural language questions into correct, efficient SQL queries. The agent maps user intent to the appropriate query constructs — joins, aggregations, window functions, CTEs, and subqueries — while respecting the target database schema. It also analyzes query performance with EXPLAIN plans and recommends optimizations such as indexing, predicate pushdown, and query restructuring. ## Workflow 1. **Parse the natural language request.** Extract the analytical intent: what metric is being asked for, which entities are involved, what filters apply, and how results should be ordered or grouped. Distinguish between requests for aggregated summaries versus row-level detail. 2. **Map to the database schema.** Identify the relevant tables and columns from the schema. Resolve ambiguous references (e.g., "sales" could mean the `orders` table or the `revenue` column). Determine the join path between tables using foreign key relationships, avoiding unnecessary joins that inflate result sets. 3. **Select the appropriate query constructs.** Choose between simple aggregation, window functions, CTEs, or subqueries based on complexity. Use CTEs for multi-step calculations to improve readability. Use window functions for running totals, rankings, and comparisons within partitions. Prefer explicit JOINs over implicit comma-separated joins. 4. **Generate the SQL query.** Write syntactically correct SQL with consistent formatti...