sql-database-assistant

name: sql-database-assistantdescription: >  This skill should be used when the user asks to "optimize SQL queries",  "explore database schemas", "generate migration SQL", "analyze query  performance", or "document database structure".license: MIT + Commons Clausemetadata:   version: 1.0.0  author: borghei  category: engineering  domain: databases  updated: 2026-04-02  tags: [sql, database, optimization, migration, schema]

SQL Database Assistant

Category: Engineering Domain: Database Development & Optimization

Overview

The SQL Database Assistant skill provides tools for analyzing SQL query performance, exploring database schemas from DDL files, and generating migration SQL from schema differences. It helps teams write efficient queries, maintain clean schemas, and manage database evolution safely.

Clarify First

Before analyzing or generating, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Task: query optimization / schema documentation / migration generation (selects the script).
• SQL input: the query, the DDL file, or the from/to schema pair to operate on (the script's actual input).
• Target database / dialect: Postgres / MySQL / etc. The scripts have no dialect option and always emit PostgreSQL-style migration SQL, so for another database, adapt the generated statements by hand and note which ones changed.

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Quick Start

# Analyze a SQL query for performance issues
python scripts/query_optimizer.py --file slow_query.sql

# Analyze inline SQL
python scripts/query_optimizer.py --query "SELECT * FROM users WHERE name LIKE '%john%'"

# Explore schema from DDL file
python scripts/schema_explorer.py --file schema.sql

# Generate migration from schema diff
python scripts/migration_generator.py --from old_schema.sql --to new_schema.sql

# JSON output
python scripts/query_optimizer.py --file query.sql --format json

Tools Overview

query_optimizer.py

Analyzes SQL queries for performance issues and optimization opportunities.

schema_explorer.py

Generates documentation from SQL DDL (CREATE TABLE) files.

migration_generator.py

Generates migration SQL by comparing two schema DDL files.

Workflows

Query Optimization Workflow

1. Identify slow queries - Collect queries from slow query log
2. Analyze - Run query_optimizer.py on each query
3. Review findings - Prioritize by estimated impact
4. Optimize - Apply suggested improvements
5. Verify - Re-analyze to confirm optimization

Schema Documentation Workflow

1. Export DDL - Dump schema from database
2. Explore - Run schema_explorer.py to generate docs
3. Review - Check relationships and data types
4. Publish - Include in project documentation

Migration Workflow

1. Capture current - Export current schema DDL
2. Define target - Write desired schema DDL
3. Generate migration - Run migration_generator.py
4. Review SQL - Check generated migration for safety
5. Test - Apply to staging database first
6. Deploy - Apply to production with rollback plan

CI Integration

# Lint SQL queries (--file takes one file, so loop over the directory)
for f in queries/*.sql; do
  python scripts/query_optimizer.py --file "$f" --format json --strict || exit 1
done

# Generate schema docs
python scripts/schema_explorer.py --file schema.sql --format markdown > SCHEMA.md

Scope & Limitations

In Scope:

• Static review of a SQL query from a file or string with query_optimizer.py: SELECT *, likely index candidates, correlated subqueries, missing WHERE, join checks and leading-wildcard LIKE, with --strict for CI.
• Schema documentation from DDL with schema_explorer.py: tables, columns, indexes and foreign keys, in text, JSON or Markdown.
• Migration SQL from two DDL files with migration_generator.py, with up steps and commented rollback steps.
• Index strategies, query patterns and anti-patterns in references/sql-optimization.md.

Out of Scope:

• Connecting to a database, running EXPLAIN or applying migrations.
• Data modeling and schema design (see engineering/database-designer/).
• Zero-downtime migration planning for large systems (see engineering/migration-architect/).
• Snowflake-specific tuning (see engineering/snowflake-development/).

Important Caveats:

• All three tools parse SQL text with patterns. Index advice does not use real table sizes, statistics or query plans.
• The scripts have no dialect option. Generated migrations use PostgreSQL-style syntax such as ALTER COLUMN ... TYPE and need adapting for other databases.
• A renamed column is generated as a drop and an add, and rollback steps restore structure, not data. Generated migrations need review and a test on a copy of the database before production.
• --file takes a single file, not a directory.

Reference Documentation

• SQL Optimization - Index strategies, query patterns, anti-patterns

Common Patterns Quick Reference

Query Anti-Patterns

Index Guidelines

Migration Safety

• Always generate rollback SQL alongside forward migration
• Test migrations against a copy of production data
• Add columns as nullable first, then backfill, then add constraints
• Never rename columns directly; add new, migrate data, drop old