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+---
+name: query-expert
+description: Master SQL and database queries across multiple systems. Generate optimized queries, analyze performance, design indexes, and troubleshoot slow queries for PostgreSQL, MySQL, MongoDB, and more.
+---
+
+# Query Expert
+
+Master database queries across SQL and NoSQL systems. Generate optimized queries, analyze performance with EXPLAIN plans, design effective indexes, and troubleshoot slow queries.
+
+## What This Skill Does
+
+Helps you write efficient, performant database queries:
+- **Generate Queries** - SQL, MongoDB, GraphQL queries
+- **Optimize Queries** - Performance tuning and refactoring
+- **Design Indexes** - Index strategies for faster queries
+- **Analyze Performance** - EXPLAIN plans and query analysis
+- **Troubleshoot** - Debug slow queries and bottlenecks
+- **Best Practices** - Query patterns and anti-patterns
+
+## Supported Databases
+
+### SQL Databases
+- **PostgreSQL** - Advanced features, CTEs, window functions
+- **MySQL/MariaDB** - InnoDB optimization, replication
+- **SQLite** - Embedded database optimization
+- **SQL Server** - T-SQL, execution plans, DMVs
+- **Oracle** - PL/SQL, partitioning, hints
+
+### NoSQL Databases
+- **MongoDB** - Aggregation pipelines, indexes
+- **Redis** - Key-value queries, Lua scripts
+- **Elasticsearch** - Full-text search queries
+- **Cassandra** - CQL, partition keys
+
+### Query Languages
+- **SQL** - Standard and vendor-specific
+- **MongoDB Query Language** - Find, aggregation
+- **GraphQL** - Efficient data fetching
+- **Cypher** - Neo4j graph queries
+
+## SQL Query Patterns
+
+### SELECT Queries
+
+#### Basic SELECT
+
+```sql
+-- ✅ Select only needed columns
+SELECT
+    user_id,
+    email,
+    created_at
+FROM users
+WHERE status = 'active'
+    AND created_at > NOW() - INTERVAL '30 days'
+ORDER BY created_at DESC
+LIMIT 100;
+
+-- ❌ Avoid SELECT *
+SELECT * FROM users;  -- Wastes resources
+```
+
+#### JOINs
+
+```sql
+-- INNER JOIN (most common)
+SELECT
+    o.order_id,
+    o.total,
+    c.name AS customer_name,
+    c.email
+FROM orders o
+INNER JOIN customers c ON o.customer_id = c.customer_id
+WHERE o.created_at >= '2024-01-01';
+
+-- LEFT JOIN (include all left rows)
+SELECT
+    c.customer_id,
+    c.name,
+    COUNT(o.order_id) AS order_count,
+    COALESCE(SUM(o.total), 0) AS total_spent
+FROM customers c
+LEFT JOIN orders o ON c.customer_id = o.customer_id
+GROUP BY c.customer_id, c.name;
+
+-- Multiple JOINs
+SELECT
+    o.order_id,
+    c.name AS customer_name,
+    p.product_name,
+    oi.quantity,
+    oi.price
+FROM orders o
+INNER JOIN customers c ON o.customer_id = c.customer_id
+INNER JOIN order_items oi ON o.order_id = oi.order_id
+INNER JOIN products p ON oi.product_id = p.product_id
+WHERE o.status = 'completed';
+```
+
+#### Subqueries
+
+```sql
+-- Subquery in WHERE
+SELECT name, email
+FROM customers
+WHERE customer_id IN (
+    SELECT DISTINCT customer_id
+    FROM orders
+    WHERE total > 1000
+);
+
+-- Correlated subquery
+SELECT
+    c.name,
+    (SELECT COUNT(*)
+     FROM orders o
+     WHERE o.customer_id = c.customer_id) AS order_count
+FROM customers c;
+
+-- ✅ Better: Use JOIN instead
+SELECT
+    c.name,
+    COUNT(o.order_id) AS order_count
+FROM customers c
+LEFT JOIN orders o ON c.customer_id = o.customer_id
+GROUP BY c.customer_id, c.name;
+```
+
+### Aggregation
+
+```sql
+-- GROUP BY with aggregates
+SELECT
+    category,
+    COUNT(*) AS product_count,
+    AVG(price) AS avg_price,
+    MIN(price) AS min_price,
+    MAX(price) AS max_price,
+    SUM(stock_quantity) AS total_stock
+FROM products
+GROUP BY category
+HAVING COUNT(*) > 5
+ORDER BY avg_price DESC;
+
+-- Multiple GROUP BY columns
+SELECT
+    DATE_TRUNC('month', created_at) AS month,
+    category,
+    SUM(total) AS monthly_sales
+FROM orders
+GROUP BY DATE_TRUNC('month', created_at), category
+ORDER BY month DESC, monthly_sales DESC;
+
+-- ROLLUP for subtotals
+SELECT
+    COALESCE(category, 'TOTAL') AS category,
+    COALESCE(brand, 'All Brands') AS brand,
+    SUM(sales) AS total_sales
+FROM products
+GROUP BY ROLLUP(category, brand);
+```
+
+### Window Functions (PostgreSQL, SQL Server, MySQL 8+)
+
+```sql
+-- ROW_NUMBER
+SELECT
+    customer_id,
+    order_date,
+    total,
+    ROW_NUMBER() OVER (
+        PARTITION BY customer_id
+        ORDER BY order_date DESC
+    ) AS order_rank
+FROM orders;
+
+-- Running totals
+SELECT
+    order_date,
+    total,
+    SUM(total) OVER (
+        ORDER BY order_date
+        ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
+    ) AS running_total
+FROM orders;
+
+-- RANK vs DENSE_RANK
+SELECT
+    product_name,
+    sales,
+    RANK() OVER (ORDER BY sales DESC) AS rank,
+    DENSE_RANK() OVER (ORDER BY sales DESC) AS dense_rank,
+    NTILE(4) OVER (ORDER BY sales DESC) AS quartile
+FROM products;
+
+-- LAG and LEAD
+SELECT
+    order_date,
+    total,
+    LAG(total, 1) OVER (ORDER BY order_date) AS prev_total,
+    LEAD(total, 1) OVER (ORDER BY order_date) AS next_total,
+    total - LAG(total, 1) OVER (ORDER BY order_date) AS change
+FROM orders;
+```
+
+### CTEs (Common Table Expressions)
+
+```sql
+-- Simple CTE
+WITH active_customers AS (
+    SELECT customer_id, name, email
+    FROM customers
+    WHERE status = 'active'
+)
+SELECT
+    ac.name,
+    COUNT(o.order_id) AS order_count
+FROM active_customers ac
+LEFT JOIN orders o ON ac.customer_id = o.customer_id
+GROUP BY ac.customer_id, ac.name;
+
+-- Multiple CTEs
+WITH
+monthly_sales AS (
+    SELECT
+        DATE_TRUNC('month', order_date) AS month,
+        SUM(total) AS sales
+    FROM orders
+    GROUP BY DATE_TRUNC('month', order_date)
+),
+avg_monthly AS (
+    SELECT AVG(sales) AS avg_sales
+    FROM monthly_sales
+)
+SELECT
+    ms.month,
+    ms.sales,
+    am.avg_sales,
+    ms.sales - am.avg_sales AS variance
+FROM monthly_sales ms
+CROSS JOIN avg_monthly am
+ORDER BY ms.month;
+
+-- Recursive CTE (hierarchies)
+WITH RECURSIVE org_tree AS (
+    -- Base case
+    SELECT
+        employee_id,
+        name,
+        manager_id,
+        1 AS level,
+        ARRAY[employee_id] AS path
+    FROM employees
+    WHERE manager_id IS NULL
+
+    UNION ALL
+
+    -- Recursive case
+    SELECT
+        e.employee_id,
+        e.name,
+        e.manager_id,
+        ot.level + 1,
+        ot.path || e.employee_id
+    FROM employees e
+    INNER JOIN org_tree ot ON e.manager_id = ot.employee_id
+)
+SELECT * FROM org_tree ORDER BY path;
+```
+
+## Query Optimization
+
+### 1. Use Indexes Effectively
+
+```sql
+-- Create index on frequently queried columns
+CREATE INDEX idx_users_email ON users(email);
+CREATE INDEX idx_orders_customer_date ON orders(customer_id, order_date);
+
+-- Composite index (order matters!)
+CREATE INDEX idx_orders_composite
+ON orders(status, customer_id, order_date);
+
+-- ✅ This query uses the index
+SELECT * FROM orders
+WHERE status = 'pending'
+    AND customer_id = 123
+    AND order_date > '2024-01-01';
+
+-- ❌ This doesn't use the index (skips first column)
+SELECT * FROM orders
+WHERE customer_id = 123;
+
+-- Partial/Filtered index (smaller, faster)
+CREATE INDEX idx_active_users
+ON users(email)
+WHERE status = 'active';
+
+-- Covering index (includes all needed columns)
+CREATE INDEX idx_users_covering
+ON users(email)
+INCLUDE (name, created_at);
+```
+
+### 2. Avoid SELECT *
+
+```sql
+-- ❌ Bad: Retrieves all columns
+SELECT * FROM users;
+
+-- ✅ Good: Select only needed columns
+SELECT user_id, email, name FROM users;
+
+-- ✅ Good: More efficient for joins
+SELECT
+    u.user_id,
+    u.email,
+    o.order_id,
+    o.total
+FROM users u
+INNER JOIN orders o ON u.user_id = o.user_id;
+```
+
+### 3. Optimize JOINs
+
+```sql
+-- ❌ Bad: Filtering after JOIN
+SELECT u.name, o.total
+FROM users u
+LEFT JOIN orders o ON u.user_id = o.user_id
+WHERE o.status = 'completed';
+
+-- ✅ Good: Filter before JOIN
+SELECT u.name, o.total
+FROM users u
+INNER JOIN (
+    SELECT user_id, total
+    FROM orders
+    WHERE status = 'completed'
+) o ON u.user_id = o.user_id;
+
+-- ✅ Even better: Use WHERE with INNER JOIN
+SELECT u.name, o.total
+FROM users u
+INNER JOIN orders o ON u.user_id = o.user_id
+WHERE o.status = 'completed';
+```
+
+### 4. Use EXISTS Instead of IN
+
+```sql
+-- ❌ Slower: IN with subquery
+SELECT name FROM customers
+WHERE customer_id IN (
+    SELECT customer_id FROM orders WHERE total > 1000
+);
+
+-- ✅ Faster: EXISTS
+SELECT name FROM customers c
+WHERE EXISTS (
+    SELECT 1 FROM orders o
+    WHERE o.customer_id = c.customer_id
+        AND o.total > 1000
+);
+```
+
+### 5. Avoid Functions on Indexed Columns
+
+```sql
+-- ❌ Bad: Function prevents index usage
+SELECT * FROM users
+WHERE LOWER(email) = 'john@example.com';
+
+-- ✅ Good: Use functional index
+CREATE INDEX idx_users_email_lower ON users(LOWER(email));
+
+-- Or use case-insensitive collation
+SELECT * FROM users
+WHERE email = 'john@example.com' COLLATE utf8_general_ci;
+```
+
+### 6. Limit Result Sets
+
+```sql
+-- ✅ Use LIMIT/TOP for pagination
+SELECT * FROM orders
+ORDER BY created_at DESC
+LIMIT 20 OFFSET 0;
+
+-- ✅ Use WHERE to reduce rows early
+SELECT * FROM orders
+WHERE created_at > NOW() - INTERVAL '7 days'
+ORDER BY created_at DESC;
+```
+
+### 7. Batch Operations
+
+```sql
+-- ❌ Bad: Multiple single inserts
+INSERT INTO users (name, email) VALUES ('User1', 'user1@example.com');
+INSERT INTO users (name, email) VALUES ('User2', 'user2@example.com');
+
+-- ✅ Good: Batch insert
+INSERT INTO users (name, email) VALUES
+    ('User1', 'user1@example.com'),
+    ('User2', 'user2@example.com'),
+    ('User3', 'user3@example.com');
+
+-- ✅ Good: Batch update
+UPDATE products
+SET price = price * 1.1
+WHERE category IN ('Electronics', 'Computers');
+```
+
+## EXPLAIN Plans
+
+### PostgreSQL
+
+```sql
+-- Simple EXPLAIN
+EXPLAIN
+SELECT * FROM orders WHERE customer_id = 123;
+
+-- EXPLAIN ANALYZE (actually runs query)
+EXPLAIN ANALYZE
+SELECT
+    c.name,
+    COUNT(o.order_id) AS order_count
+FROM customers c
+LEFT JOIN orders o ON c.customer_id = o.customer_id
+GROUP BY c.customer_id, c.name;
+
+-- Look for:
+-- - Seq Scan (bad, needs index)
+-- - Index Scan (good)
+-- - Bitmap Heap Scan (good for multiple rows)
+-- - Hash Join vs Nested Loop
+-- - High cost numbers
+```
+
+### MySQL
+
+```sql
+-- EXPLAIN
+EXPLAIN
+SELECT * FROM orders WHERE customer_id = 123;
+
+-- EXPLAIN ANALYZE (MySQL 8.0.18+)
+EXPLAIN ANALYZE
+SELECT * FROM orders WHERE customer_id = 123;
+
+-- Look for:
+-- - type: ALL (table scan, bad)
+-- - type: index (index scan, good)
+-- - type: ref (index lookup, great)
+-- - Extra: Using filesort (may need index)
+-- - Extra: Using temporary (may need optimization)
+```
+
+## Indexing Strategies
+
+### When to Index
+
+**✅ Index these columns:**
+- Primary keys (automatic)
+- Foreign keys
+- Columns in WHERE clauses
+- Columns in JOIN conditions
+- Columns in ORDER BY
+- Columns in GROUP BY
+
+**❌ Don't index:**
+- Small tables (< 1000 rows)
+- Columns with low cardinality (few distinct values)
+- Frequently updated columns
+- Large text/blob columns
+
+### Index Types
+
+```sql
+-- B-Tree (default, most common)
+CREATE INDEX idx_users_email ON users(email);
+
+-- Hash index (equality only, PostgreSQL)
+CREATE INDEX idx_users_email_hash ON users USING HASH(email);
+
+-- GIN (full-text search, arrays, JSONB)
+CREATE INDEX idx_posts_content_gin
+ON posts USING GIN(to_tsvector('english', content));
+
+-- GiST (geometric, full-text)
+CREATE INDEX idx_locations_gist
+ON locations USING GIST(coordinates);
+
+-- Partial index (filtered)
+CREATE INDEX idx_orders_pending
+ON orders(customer_id)
+WHERE status = 'pending';
+
+-- Expression index
+CREATE INDEX idx_users_email_domain
+ON users((email ~~ '%@gmail.com%'));
+```
+
+### Composite Index Order
+
+```sql
+-- Index column order matters!
+CREATE INDEX idx_orders_search
+ON orders(status, customer_id, created_at);
+
+-- ✅ Uses index (left-most column)
+WHERE status = 'completed'
+
+-- ✅ Uses index (left-most columns)
+WHERE status = 'completed' AND customer_id = 123
+
+-- ✅ Uses full index
+WHERE status = 'completed'
+    AND customer_id = 123
+    AND created_at > '2024-01-01'
+
+-- ❌ Doesn't use index (skips first column)
+WHERE customer_id = 123
+
+-- ❌ Doesn't use index (skips first column)
+WHERE created_at > '2024-01-01'
+```
+
+## MongoDB Queries
+
+### Find Queries
+
+```javascript
+// Basic find
+db.users.find({ status: 'active' })
+
+// Find with projection
+db.users.find(
+    { status: 'active' },
+    { name: 1, email: 1, _id: 0 }
+)
+
+// Find with operators
+db.orders.find({
+    total: { $gt: 100, $lt: 1000 },
+    status: { $in: ['pending', 'processing'] },
+    'customer.city': 'New York'
+})
+
+// Find with sort and limit
+db.products.find({ category: 'Electronics' })
+    .sort({ price: -1 })
+    .limit(10)
+
+// Count
+db.users.countDocuments({ status: 'active' })
+```
+
+### Aggregation Pipeline
+
+```javascript
+// Group and count
+db.orders.aggregate([
+    { $match: { status: 'completed' } },
+    { $group: {
+        _id: '$customer_id',
+        total_orders: { $sum: 1 },
+        total_spent: { $sum: '$total' },
+        avg_order: { $avg: '$total' }
+    }},
+    { $sort: { total_spent: -1 } },
+    { $limit: 10 }
+])
+
+// Lookup (JOIN)
+db.orders.aggregate([
+    { $lookup: {
+        from: 'customers',
+        localField: 'customer_id',
+        foreignField: '_id',
+        as: 'customer'
+    }},
+    { $unwind: '$customer' },
+    { $project: {
+        order_id: 1,
+        total: 1,
+        'customer.name': 1,
+        'customer.email': 1
+    }}
+])
+
+// Complex aggregation
+db.sales.aggregate([
+    // Filter
+    { $match: {
+        date: { $gte: ISODate('2024-01-01') }
+    }},
+
+    // Add computed fields
+    { $addFields: {
+        month: { $month: '$date' },
+        year: { $year: '$date' }
+    }},
+
+    // Group by month
+    { $group: {
+        _id: { year: '$year', month: '$month' },
+        total_sales: { $sum: '$amount' },
+        order_count: { $sum: 1 },
+        avg_sale: { $avg: '$amount' }
+    }},
+
+    // Sort
+    { $sort: { '_id.year': 1, '_id.month': 1 } },
+
+    // Reshape
+    { $project: {
+        _id: 0,
+        date: {
+            $concat: [
+                { $toString: '$_id.year' },
+                '-',
+                { $toString: '$_id.month' }
+            ]
+        },
+        total_sales: 1,
+        order_count: 1,
+        avg_sale: { $round: ['$avg_sale', 2] }
+    }}
+])
+```
+
+### MongoDB Indexes
+
+```javascript
+// Single field index
+db.users.createIndex({ email: 1 })
+
+// Compound index
+db.orders.createIndex({ customer_id: 1, created_at: -1 })
+
+// Unique index
+db.users.createIndex({ email: 1 }, { unique: true })
+
+// Partial index
+db.orders.createIndex(
+    { customer_id: 1 },
+    { partialFilterExpression: { status: 'active' } }
+)
+
+// Text index
+db.products.createIndex({ name: 'text', description: 'text' })
+
+// TTL index (auto-delete after time)
+db.sessions.createIndex(
+    { created_at: 1 },
+    { expireAfterSeconds: 3600 }
+)
+
+// List indexes
+db.users.getIndexes()
+
+// Analyze query performance
+db.orders.find({ customer_id: 123 }).explain('executionStats')
+```
+
+## GraphQL Queries
+
+```graphql
+# Basic query
+query {
+  users {
+    id
+    name
+    email
+  }
+}
+
+# Query with arguments
+query {
+  user(id: "123") {
+    name
+    email
+    orders {
+      id
+      total
+      status
+    }
+  }
+}
+
+# Query with variables
+query GetUser($userId: ID!) {
+  user(id: $userId) {
+    name
+    email
+    orders(limit: 10, status: COMPLETED) {
+      id
+      total
+      createdAt
+    }
+  }
+}
+
+# Fragments (reusable fields)
+fragment UserFields on User {
+  id
+  name
+  email
+  createdAt
+}
+
+query {
+  user(id: "123") {
+    ...UserFields
+    orders {
+      id
+      total
+    }
+  }
+}
+
+# Avoid N+1 queries with DataLoader
+query {
+  orders {
+    id
+    total
+    customer {  # Batched by DataLoader
+      name
+      email
+    }
+  }
+}
+```
+
+## Common Anti-Patterns
+
+### ❌ N+1 Query Problem
+
+```sql
+-- Bad: N+1 queries
+SELECT * FROM customers;  -- 1 query
+-- Then for each customer:
+SELECT * FROM orders WHERE customer_id = ?;  -- N queries
+
+-- Good: Single JOIN query
+SELECT
+    c.customer_id,
+    c.name,
+    o.order_id,
+    o.total
+FROM customers c
+LEFT JOIN orders o ON c.customer_id = o.customer_id;
+```
+
+### ❌ Using OR on Different Columns
+
+```sql
+-- Bad: Can't use indexes effectively
+SELECT * FROM products
+WHERE name = 'iPhone' OR category = 'Electronics';
+
+-- Good: Use UNION
+SELECT * FROM products WHERE name = 'iPhone'
+UNION
+SELECT * FROM products WHERE category = 'Electronics';
+```
+
+### ❌ Implicit Type Conversion
+
+```sql
+-- Bad: '123' is string, user_id is integer
+SELECT * FROM users WHERE user_id = '123';
+
+-- Good: Use correct type
+SELECT * FROM users WHERE user_id = 123;
+```
+
+## Query Performance Checklist
+
+- [ ] Select only needed columns (no SELECT *)
+- [ ] Add indexes to WHERE/JOIN/ORDER BY columns
+- [ ] Use EXPLAIN to analyze query plan
+- [ ] Avoid functions on indexed columns
+- [ ] Use EXISTS instead of IN for subqueries
+- [ ] Batch INSERT/UPDATE operations
+- [ ] Use appropriate JOIN types
+- [ ] Filter early (WHERE before JOIN)
+- [ ] Use LIMIT for large result sets
+- [ ] Monitor slow query logs
+- [ ] Update statistics regularly
+- [ ] Avoid SELECT DISTINCT when possible
+- [ ] Use covering indexes when appropriate
+
+## Resources
+
+- **PostgreSQL**: https://www.postgresql.org/docs/current/performance-tips.html
+- **MySQL**: https://dev.mysql.com/doc/refman/8.0/en/optimization.html
+- **MongoDB**: https://docs.mongodb.com/manual/core/query-optimization/
+- **Use The Index, Luke**: https://use-the-index-luke.com/
+
+---
+
+**"Premature optimization is the root of all evil, but slow queries are the root of all frustration."**
--- a/skills/query-expert/references/indexing.md
+++ b/skills/query-expert/references/indexing.md
@@ -0,0 +1,7 @@
+# Indexing Strategies - See SKILL.md for complete indexing guide including:
+- When to create indexes
+- Composite index column order
+- Covering indexes
+- Partial/filtered indexes
+- Index types (B-Tree, Hash, GIN, GiST)
+- Index maintenance
--- a/skills/query-expert/references/nosql_queries.md
+++ b/skills/query-expert/references/nosql_queries.md
@@ -0,0 +1,6 @@
+# NoSQL Queries - See SKILL.md for complete NoSQL guide including:
+- MongoDB find() queries
+- MongoDB aggregation pipelines
+- MongoDB indexes
+- GraphQL queries
+- Query optimization for NoSQL
--- a/skills/query-expert/references/optimization.md
+++ b/skills/query-expert/references/optimization.md
@@ -0,0 +1,9 @@
+# Query Optimization - See SKILL.md for complete optimization guide including:
+- Avoid SELECT *
+- Use indexes effectively
+- Optimize JOINs
+- EXISTS vs IN
+- Function usage on indexed columns
+- LIMIT and pagination
+- Batch operations
+- Common anti-patterns
--- a/skills/query-expert/references/sql_patterns.md
+++ b/skills/query-expert/references/sql_patterns.md
@@ -0,0 +1,7 @@
+# SQL Query Patterns - See SKILL.md for complete SQL guide including:
+- SELECT queries with JOINs
+- Aggregation with GROUP BY
+- Window functions
+- CTEs (Common Table Expressions)  
+- Subqueries
+- Best practices and anti-patterns
--- a/skills/query-expert/scripts/analyze_performance.sh
+++ b/skills/query-expert/scripts/analyze_performance.sh
@@ -0,0 +1,297 @@
+#!/bin/bash
+
+# Query Expert - Performance Analyzer
+# Analyze EXPLAIN output and provide optimization recommendations
+
+set -e
+
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+print_info() {
+    echo -e "${BLUE}ℹ $1${NC}"
+}
+
+print_warning() {
+    echo -e "${YELLOW}⚠ $1${NC}"
+}
+
+print_success() {
+    echo -e "${GREEN}✓ $1${NC}"
+}
+
+echo ""
+echo "╔════════════════════════════════════════════════════════════╗"
+echo "║                                                            ║"
+echo "║          Query Expert - Performance Analyzer              ║"
+echo "║                                                            ║"
+echo "╚════════════════════════════════════════════════════════════╝"
+echo ""
+
+print_info "Performance Analysis Guide"
+echo ""
+
+cat << 'EOF'
+## PostgreSQL EXPLAIN Analysis
+
+### Run EXPLAIN
+```sql
+EXPLAIN ANALYZE
+SELECT * FROM orders WHERE customer_id = 123;
+```
+
+### What to Look For:
+
+**Seq Scan (Table Scan) ❌**
+→ Reading entire table
+→ Fix: Add index on filtered column
+→ CREATE INDEX idx_orders_customer ON orders(customer_id);
+
+**Index Scan ✅**
+→ Using index efficiently
+→ Good performance for specific rows
+
+**Bitmap Heap Scan ✅**
+→ Good for returning multiple rows
+→ Efficient index usage
+
+**Nested Loop ⚠️**
+→ Can be slow with large datasets
+→ Consider: Hash Join or Merge Join
+
+**Hash Join ✅**
+→ Good for large joins
+→ Requires memory
+
+**Cost Numbers**
+→ Higher = slower
+→ Compare before/after optimization
+→ cost=0.00..35.50 rows=10
+
+**Actual Time**
+→ Real execution time
+→ actual time=0.023..0.156 rows=10 loops=1
+
+---
+
+## MySQL EXPLAIN Analysis
+
+### Run EXPLAIN
+```sql
+EXPLAIN SELECT * FROM orders WHERE customer_id = 123;
+```
+
+### Type Column:
+
+**ALL ❌**
+→ Full table scan
+→ Fix: Add index
+
+**index ⚠️**
+→ Full index scan
+→ Better than ALL, but could improve
+
+**range ✅**
+→ Index range scan
+→ Good for WHERE with >, <, BETWEEN
+
+**ref ✅✅**
+→ Index lookup
+→ Excellent performance
+
+**eq_ref ✅✅✅**
+→ Unique index lookup
+→ Best performance
+
+**const ✅✅✅**
+→ Constant lookup (primary key)
+→ Fastest possible
+
+### Extra Column:
+
+**Using filesort ⚠️**
+→ Sorting in memory/disk
+→ Fix: Add index on ORDER BY columns
+
+**Using temporary ⚠️**
+→ Creating temporary table
+→ Fix: Optimize GROUP BY or DISTINCT
+
+**Using index ✅**
+→ Covering index (index-only scan)
+→ Excellent performance
+
+**Using where ✅**
+→ Filtering after read
+→ Normal for WHERE clauses
+
+---
+
+## MongoDB Explain Analysis
+
+### Run Explain
+```javascript
+db.orders.find({ customer_id: 123 }).explain("executionStats")
+```
+
+### What to Look For:
+
+**COLLSCAN ❌**
+→ Full collection scan
+→ Fix: Create index
+→ db.orders.createIndex({ customer_id: 1 })
+
+**IXSCAN ✅**
+→ Index scan
+→ Good performance
+
+**executionTimeMillis**
+→ Total execution time
+→ < 100ms good, > 1000ms needs optimization
+
+**nReturned vs totalDocsExamined**
+→ Efficiency ratio
+→ Ideally close to 1:1
+→ If totalDocsExamined >> nReturned, add index
+
+**Index Usage**
+→ indexName: "customer_id_1" ✅
+→ indexName: null ❌ (no index used)
+
+---
+
+## Index Recommendations
+
+### When to Create Index:
+
+1. **WHERE Clause**
+   CREATE INDEX idx_table_column ON table(column);
+
+2. **JOIN Columns**
+   CREATE INDEX idx_table_join_col ON table(join_column);
+
+3. **ORDER BY**
+   CREATE INDEX idx_table_sort ON table(sort_column);
+
+4. **Composite Index (order matters!)**
+   CREATE INDEX idx_multi ON table(col1, col2, col3);
+   → Works for: col1 | col1,col2 | col1,col2,col3
+   → NOT for: col2 | col3 | col2,col3
+
+5. **Covering Index**
+   CREATE INDEX idx_covering ON table(filter_col) INCLUDE (select_cols);
+   → Index contains all needed columns
+   → Fastest possible (index-only scan)
+
+### When NOT to Index:
+
+- Small tables (< 1000 rows)
+- Columns with low cardinality (few distinct values)
+- Frequently updated columns
+- Large text/blob columns
+
+---
+
+## Query Optimization Checklist
+
+Performance Issues:
+[ ] Check EXPLAIN plan
+[ ] Look for table scans (Seq Scan, ALL, COLLSCAN)
+[ ] Identify missing indexes
+[ ] Check JOIN types (Nested Loop on large tables)
+[ ] Look for filesort or temporary tables
+[ ] Verify index usage (Using index)
+
+Optimizations:
+[ ] Create indexes on WHERE columns
+[ ] Create indexes on JOIN columns  
+[ ] Use composite indexes (correct order)
+[ ] Add covering indexes for frequent queries
+[ ] Use LIMIT to reduce result set
+[ ] Avoid SELECT * (select only needed columns)
+[ ] Avoid functions on indexed columns
+[ ] Use EXISTS instead of IN (subqueries)
+[ ] Filter early (before JOIN)
+[ ] Use appropriate JOIN type
+
+Monitoring:
+[ ] Run EXPLAIN ANALYZE before optimization
+[ ] Create indexes
+[ ] Run EXPLAIN ANALYZE after optimization
+[ ] Compare execution time and cost
+[ ] Test with production-like data volume
+[ ] Monitor slow query log
+
+---
+
+## Example Optimization
+
+### Before (Slow)
+```sql
+-- EXPLAIN shows: Seq Scan, cost=1000.00
+SELECT * FROM orders WHERE customer_id = 123;
+```
+
+### Optimization Steps
+```sql
+-- 1. Create index
+CREATE INDEX idx_orders_customer ON orders(customer_id);
+
+-- 2. Optimize query (avoid SELECT *)
+SELECT order_id, total, created_at
+FROM orders
+WHERE customer_id = 123
+ORDER BY created_at DESC
+LIMIT 100;
+
+-- 3. Check improvement
+EXPLAIN ANALYZE
+SELECT order_id, total, created_at
+FROM orders
+WHERE customer_id = 123
+ORDER BY created_at DESC
+LIMIT 100;
+```
+
+### After (Fast)
+```
+→ Index Scan using idx_orders_customer
+→ cost=0.29..15.50 (95% improvement!)
+→ actual time=0.015..0.023
+```
+
+---
+
+## Tools
+
+**PostgreSQL:**
+- EXPLAIN ANALYZE
+- pg_stat_statements extension
+- pgBadger (log analyzer)
+
+**MySQL:**
+- EXPLAIN
+- SHOW PROFILE
+- MySQL Workbench Performance Dashboard
+
+**MongoDB:**
+- explain("executionStats")
+- MongoDB Compass (GUI)
+- Database Profiler
+
+---
+
+EOF
+
+print_success "Performance analysis guide displayed"
+echo ""
+print_info "Next Steps:"
+echo "  1. Run EXPLAIN on your slow query"
+echo "  2. Identify the bottleneck (table scan, no index, etc.)"
+echo "  3. Apply recommended optimization"
+echo "  4. Re-run EXPLAIN to verify improvement"
+echo "  5. Test with production data volume"
+echo ""
--- a/skills/query-expert/scripts/generate_query.sh
+++ b/skills/query-expert/scripts/generate_query.sh
@@ -0,0 +1,436 @@
+#!/bin/bash
+
+# Query Expert - Query Generator
+# Generate optimized database queries with best practices
+
+set -e
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+# Helper functions
+print_success() {
+    echo -e "${GREEN}✓ $1${NC}"
+}
+
+print_info() {
+    echo -e "${BLUE}ℹ $1${NC}"
+}
+
+print_warning() {
+    echo -e "${YELLOW}⚠ $1${NC}"
+}
+
+prompt_input() {
+    local prompt="$1"
+    local var_name="$2"
+    local required="${3:-false}"
+
+    while true; do
+        echo -e "${BLUE}${prompt}${NC}"
+        read -r input
+
+        if [ -z "$input" ] && [ "$required" = true ]; then
+            echo -e "${RED}This field is required.${NC}"
+            continue
+        fi
+
+        eval "$var_name='$input'"
+        break
+    done
+}
+
+prompt_select() {
+    local prompt="$1"
+    local var_name="$2"
+    shift 2
+    local options=("$@")
+
+    echo -e "${BLUE}${prompt}${NC}"
+    PS3="Select (1-${#options[@]}): "
+    select opt in "${options[@]}"; do
+        if [ -n "$opt" ]; then
+            eval "$var_name='$opt'"
+            break
+        else
+            echo -e "${RED}Invalid selection.${NC}"
+        fi
+    done
+}
+
+# Banner
+echo ""
+echo "╔════════════════════════════════════════════════════════════╗"
+echo "║                                                            ║"
+echo "║              Query Expert - Query Generator               ║"
+echo "║                                                            ║"
+echo "╚════════════════════════════════════════════════════════════╝"
+echo ""
+
+# Step 1: Database Type
+print_info "Step 1/5: Database Type"
+prompt_select "Which database?" DB_TYPE \
+    "PostgreSQL" \
+    "MySQL" \
+    "SQLite" \
+    "SQL Server" \
+    "MongoDB" \
+    "GraphQL"
+
+# Step 2: Query Type
+print_info "Step 2/5: Query Type"
+
+case $DB_TYPE in
+    "MongoDB")
+        prompt_select "What type of query?" QUERY_TYPE \
+            "Find" \
+            "Aggregation" \
+            "Update" \
+            "Insert" \
+            "Delete"
+        ;;
+    "GraphQL")
+        prompt_select "What type of query?" QUERY_TYPE \
+            "Query" \
+            "Mutation" \
+            "Subscription"
+        ;;
+    *)
+        prompt_select "What type of query?" QUERY_TYPE \
+            "SELECT" \
+            "INSERT" \
+            "UPDATE" \
+            "DELETE" \
+            "JOIN" \
+            "Aggregate (GROUP BY)" \
+            "Window Function" \
+            "CTE (WITH)"
+        ;;
+esac
+
+# Step 3: Table/Collection
+print_info "Step 3/5: Target Table/Collection"
+if [ "$DB_TYPE" = "MongoDB" ]; then
+    prompt_input "Collection name (e.g., users, orders):" TABLE_NAME true
+else
+    prompt_input "Table name (e.g., users, orders):" TABLE_NAME true
+fi
+
+# Step 4: Columns/Fields
+print_info "Step 4/5: Columns/Fields"
+prompt_input "Columns to select (comma-separated, or * for all):" COLUMNS
+COLUMNS=${COLUMNS:-"*"}
+
+# Step 5: Conditions
+print_info "Step 5/5: Conditions (optional)"
+prompt_input "WHERE conditions (e.g., status = 'active'):" CONDITIONS
+
+# Generate query based on selections
+generate_sql_select() {
+    cat << EOF
+
+-- Generated SELECT Query
+-- Database: $DB_TYPE
+-- Optimized for performance
+
+SELECT
+    ${COLUMNS//,/,${'\n'}    }
+FROM $TABLE_NAME
+EOF
+
+    if [ -n "$CONDITIONS" ]; then
+        echo "WHERE $CONDITIONS"
+    fi
+
+    cat << 'EOF'
+-- Optional: Add ORDER BY
+-- ORDER BY created_at DESC
+-- Optional: Add LIMIT
+-- LIMIT 100;
+EOF
+
+    echo ""
+    print_info "Optimization Tips:"
+    echo "  • Select only needed columns (avoid SELECT *)"
+    echo "  • Add index on WHERE columns: CREATE INDEX idx_${TABLE_NAME}_${CONDITIONS%% *} ON $TABLE_NAME(${CONDITIONS%% *})"
+    echo "  • Use LIMIT for large result sets"
+    echo "  • Add ORDER BY for consistent results"
+}
+
+generate_sql_join() {
+    prompt_input "Second table name:" TABLE2
+    prompt_input "JOIN column (e.g., customer_id):" JOIN_COL
+
+    cat << EOF
+
+-- Generated JOIN Query
+-- Database: $DB_TYPE
+
+SELECT
+    ${TABLE_NAME:0:1}.${COLUMNS//,/,${'\n'}    ${TABLE_NAME:0:1}.}
+FROM $TABLE_NAME ${TABLE_NAME:0:1}
+INNER JOIN $TABLE2 ${TABLE2:0:1}
+    ON ${TABLE_NAME:0:1}.$JOIN_COL = ${TABLE2:0:1}.$JOIN_COL
+EOF
+
+    if [ -n "$CONDITIONS" ]; then
+        echo "WHERE $CONDITIONS"
+    fi
+
+    echo ";"
+    echo ""
+    print_info "JOIN Types:"
+    echo "  • INNER JOIN - Only matching rows"
+    echo "  • LEFT JOIN - All left rows + matching right"
+    echo "  • RIGHT JOIN - All right rows + matching left"
+    echo "  • FULL OUTER JOIN - All rows from both"
+    echo ""
+    print_info "Optimization:"
+    echo "  • Add indexes on JOIN columns"
+    echo "  • Filter early with WHERE"
+    echo "  • Use INNER JOIN when possible"
+}
+
+generate_sql_aggregate() {
+    prompt_input "GROUP BY columns (comma-separated):" GROUP_COLS
+    prompt_input "Aggregate function (e.g., COUNT(*), SUM(amount)):" AGG_FUNC
+
+    cat << EOF
+
+-- Generated Aggregate Query
+-- Database: $DB_TYPE
+
+SELECT
+    ${GROUP_COLS//,/,${'\n'}    },
+    $AGG_FUNC AS total
+FROM $TABLE_NAME
+EOF
+
+    if [ -n "$CONDITIONS" ]; then
+        echo "WHERE $CONDITIONS"
+    fi
+
+    cat << EOF
+GROUP BY ${GROUP_COLS//,/,${'\n'}    }
+-- Optional: Add HAVING for aggregate filters
+-- HAVING COUNT(*) > 10
+ORDER BY total DESC;
+EOF
+
+    echo ""
+    print_info "Aggregate Functions:"
+    echo "  • COUNT(*) - Count rows"
+    echo "  • SUM(column) - Sum values"
+    echo "  • AVG(column) - Average"
+    echo "  • MIN/MAX(column) - Min/Max values"
+}
+
+generate_sql_cte() {
+    cat << EOF
+
+-- Generated CTE (Common Table Expression)
+-- Database: $DB_TYPE
+
+WITH ${TABLE_NAME}_filtered AS (
+    SELECT
+        ${COLUMNS//,/,${'\n'}        }
+    FROM $TABLE_NAME
+EOF
+
+    if [ -n "$CONDITIONS" ]; then
+        echo "    WHERE $CONDITIONS"
+    fi
+
+    cat << 'EOF'
+)
+SELECT *
+FROM table_filtered
+-- Add JOINs or additional filtering here
+;
+EOF
+
+    echo ""
+    print_info "CTE Benefits:"
+    echo "  • Improves readability"
+    echo "  • Reusable within same query"
+    echo "  • Supports recursion"
+    echo "  • Better than subqueries in many cases"
+}
+
+generate_mongodb_find() {
+    cat << EOF
+
+// Generated MongoDB Find Query
+// Collection: $TABLE_NAME
+
+db.$TABLE_NAME.find(
+EOF
+
+    if [ -n "$CONDITIONS" ]; then
+        echo "    { $CONDITIONS },"
+    else
+        echo "    {},"
+    fi
+
+    if [ "$COLUMNS" != "*" ]; then
+        echo "    { ${COLUMNS//,/: 1, }: 1, _id: 0 }"
+    else
+        echo "    {}"
+    fi
+
+    cat << 'EOF'
+)
+.sort({ created_at: -1 })
+.limit(100);
+EOF
+
+    echo ""
+    print_info "MongoDB Optimization:"
+    echo "  • Create index: db.$TABLE_NAME.createIndex({ field: 1 })"
+    echo "  • Use projection to limit fields"
+    echo "  • Add sort and limit for performance"
+    echo "  • Use explain(): .explain('executionStats')"
+}
+
+generate_mongodb_aggregation() {
+    cat << EOF
+
+// Generated MongoDB Aggregation Pipeline
+// Collection: $TABLE_NAME
+
+db.$TABLE_NAME.aggregate([
+    // Stage 1: Match (filter)
+    { \$match: {
+EOF
+
+    if [ -n "$CONDITIONS" ]; then
+        echo "        $CONDITIONS"
+    fi
+
+    cat << 'EOF'
+    }},
+
+    // Stage 2: Group (aggregate)
+    { $group: {
+        _id: '$field',
+        count: { $sum: 1 },
+        total: { $sum: '$amount' },
+        average: { $avg: '$amount' }
+    }},
+
+    // Stage 3: Sort
+    { $sort: { total: -1 } },
+
+    // Stage 4: Limit
+    { $limit: 10 }
+]);
+EOF
+
+    echo ""
+    print_info "Aggregation Stages:"
+    echo "  • \$match - Filter documents"
+    echo "  • \$group - Group and aggregate"
+    echo "  • \$project - Reshape documents"
+    echo "  • \$lookup - JOIN collections"
+    echo "  • \$sort - Sort results"
+    echo "  • \$limit - Limit results"
+}
+
+generate_graphql_query() {
+    cat << EOF
+
+# Generated GraphQL Query
+
+query Get${TABLE_NAME^} {
+  $TABLE_NAME {
+    ${COLUMNS//,/
+    }
+  }
+}
+
+# With variables:
+query Get${TABLE_NAME^}(\$id: ID!) {
+  ${TABLE_NAME}(id: \$id) {
+    ${COLUMNS//,/
+    }
+  }
+}
+EOF
+
+    echo ""
+    print_info "GraphQL Best Practices:"
+    echo "  • Request only needed fields"
+    echo "  • Use fragments for reusable fields"
+    echo "  • Implement DataLoader to avoid N+1"
+    echo "  • Add pagination (first, after)"
+}
+
+# Generate based on query type
+case $DB_TYPE in
+    "MongoDB")
+        case $QUERY_TYPE in
+            "Find")
+                generate_mongodb_find
+                ;;
+            "Aggregation")
+                generate_mongodb_aggregation
+                ;;
+        esac
+        ;;
+    "GraphQL")
+        generate_graphql_query
+        ;;
+    *)
+        case $QUERY_TYPE in
+            "SELECT")
+                generate_sql_select
+                ;;
+            "JOIN")
+                generate_sql_join
+                ;;
+            "Aggregate (GROUP BY)")
+                generate_sql_aggregate
+                ;;
+            "CTE (WITH)")
+                generate_sql_cte
+                ;;
+        esac
+        ;;
+esac
+
+# Summary
+echo ""
+echo "╔════════════════════════════════════════════════════════════╗"
+echo "║                    Query Generated                         ║"
+echo "╚════════════════════════════════════════════════════════════╝"
+echo ""
+print_success "Query generated for $DB_TYPE"
+print_success "Type: $QUERY_TYPE"
+echo ""
+print_info "Next steps:"
+echo "  1. Review and test the query"
+echo "  2. Add appropriate indexes"
+echo "  3. Use EXPLAIN to analyze performance"
+echo "  4. Add error handling in production"
+echo "  5. Monitor query performance"
+echo ""
+print_info "Performance Tools:"
+case $DB_TYPE in
+    "PostgreSQL")
+        echo "  • EXPLAIN ANALYZE query"
+        echo "  • pg_stat_statements extension"
+        ;;
+    "MySQL")
+        echo "  • EXPLAIN query"
+        echo "  • SHOW PROFILE"
+        ;;
+    "MongoDB")
+        echo "  • query.explain('executionStats')"
+        echo "  • db.collection.getIndexes()"
+        ;;
+esac
+echo ""
--- a/skills/query-expert/scripts/optimize_query.sh
+++ b/skills/query-expert/scripts/optimize_query.sh
@@ -0,0 +1,196 @@
+#!/bin/bash
+
+# Query Expert - Query Optimizer
+# Analyze and optimize SQL queries
+
+set -e
+
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+MAGENTA='\033[0;35m'
+NC='\033[0m'
+
+PASS_COUNT=0
+WARN_COUNT=0
+FAIL_COUNT=0
+
+print_success() {
+    echo -e "${GREEN}✓ GOOD${NC} $1"
+    ((PASS_COUNT++))
+}
+
+print_warning() {
+    echo -e "${YELLOW}⚠ IMPROVE${NC} $1"
+    ((WARN_COUNT++))
+}
+
+print_error() {
+    echo -e "${RED}✗ ISSUE${NC} $1"
+    ((FAIL_COUNT++))
+}
+
+print_info() {
+    echo -e "${BLUE}ℹ INFO${NC} $1"
+}
+
+print_section() {
+    echo ""
+    echo -e "${MAGENTA}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
+    echo -e "${MAGENTA}$1${NC}"
+    echo -e "${MAGENTA}━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━${NC}"
+}
+
+echo ""
+echo "╔════════════════════════════════════════════════════════════╗"
+echo "║                                                            ║"
+echo "║            Query Expert - Query Optimizer                 ║"
+echo "║                                                            ║"
+echo "╚════════════════════════════════════════════════════════════╝"
+echo ""
+
+if [ -z "$1" ]; then
+    print_info "Usage: $0 <query-file.sql>"
+    print_info "Example: $0 slow_query.sql"
+    exit 1
+fi
+
+QUERY_FILE="$1"
+
+if [ ! -f "$QUERY_FILE" ]; then
+    echo -e "${RED}File not found: $QUERY_FILE${NC}"
+    exit 1
+fi
+
+QUERY=$(cat "$QUERY_FILE")
+
+# Section 1: SELECT * Detection
+print_section "1. COLUMN SELECTION"
+
+if echo "$QUERY" | grep -qi "SELECT \*"; then
+    print_error "Using SELECT * (selects all columns)"
+    echo "         Fix: SELECT only needed columns"
+    echo "         SELECT user_id, name, email FROM users;"
+else
+    print_success "Selecting specific columns"
+fi
+
+# Section 2: Index Usage
+print_section "2. INDEX OPPORTUNITIES"
+
+if echo "$QUERY" | grep -qi "WHERE"; then
+    WHERE_COLS=$(echo "$QUERY" | grep -oi "WHERE [^;]*" | grep -o "[a-zA-Z_][a-zA-Z0-9_]*\s*=" | awk '{print $1}')
+    
+    if [ -n "$WHERE_COLS" ]; then
+        print_info "Columns in WHERE clause should have indexes:"
+        for col in $WHERE_COLS; do
+            echo "         CREATE INDEX idx_table_$col ON table($col);"
+        done
+    fi
+fi
+
+if echo "$QUERY" | grep -qi "JOIN.*ON"; then
+    print_info "JOIN columns should have indexes:"
+    echo "         CREATE INDEX idx_table_join_col ON table(join_col);"
+fi
+
+if echo "$QUERY" | grep -qi "ORDER BY"; then
+    print_info "ORDER BY columns benefit from indexes:"
+    echo "         Consider composite index with WHERE + ORDER BY columns"
+fi
+
+# Section 3: JOIN Analysis
+print_section "3. JOIN OPTIMIZATION"
+
+if echo "$QUERY" | grep -qi "LEFT JOIN" && echo "$QUERY" | grep -qi "WHERE"; then
+    print_warning "LEFT JOIN with WHERE on right table"
+    echo "         Consider using INNER JOIN instead"
+fi
+
+if echo "$QUERY" | grep -qi "WHERE.*IN\s*(SELECT"; then
+    print_error "Using IN with subquery"
+    echo "         Fix: Use EXISTS or JOIN instead"
+    echo "         WHERE EXISTS (SELECT 1 FROM ...)"
+fi
+
+# Section 4: Function Usage
+print_section "4. FUNCTION ON COLUMNS"
+
+if echo "$QUERY" | grep -Eqi "WHERE.*(LOWER|UPPER|SUBSTRING|DATE|YEAR|MONTH)\s*\("; then
+    print_error "Function on indexed column in WHERE"
+    echo "         Fix: Use functional index or avoid function"
+    echo "         CREATE INDEX idx_table_lower_col ON table(LOWER(col));"
+fi
+
+# Section 5: DISTINCT Usage
+print_section "5. DISTINCT USAGE"
+
+if echo "$QUERY" | grep -qi "SELECT DISTINCT"; then
+    print_warning "Using DISTINCT (potentially expensive)"
+    echo "         Consider: Is DISTINCT necessary?"
+    echo "         Alternative: Use GROUP BY if aggregating"
+fi
+
+# Section 6: Subqueries
+print_section "6. SUBQUERY OPTIMIZATION"
+
+SUBQUERY_COUNT=$(echo "$QUERY" | grep -oi "SELECT" | wc -l)
+if [ "$SUBQUERY_COUNT" -gt 1 ]; then
+    if echo "$QUERY" | grep -qi "FROM.*SELECT"; then
+        print_info "Contains subqueries - consider CTEs for readability"
+        echo "         WITH cte AS (SELECT ...) SELECT ... FROM cte"
+    fi
+fi
+
+# Section 7: LIMIT Usage
+print_section "7. RESULT SET SIZE"
+
+if ! echo "$QUERY" | grep -qi "LIMIT\|TOP\|FETCH FIRST"; then
+    print_warning "No LIMIT clause found"
+    echo "         Add LIMIT to prevent large result sets"
+    echo "         SELECT ... LIMIT 100;"
+fi
+
+# Section 8: Sorting
+print_section "8. SORTING"
+
+if echo "$QUERY" | grep -qi "ORDER BY"; then
+    if ! echo "$QUERY" | grep -qi "LIMIT"; then
+        print_warning "ORDER BY without LIMIT"
+        echo "         Consider adding LIMIT to reduce sort cost"
+    fi
+fi
+
+# Summary
+echo ""
+echo "╔════════════════════════════════════════════════════════════╗"
+echo "║                  Optimization Summary                      ║"
+echo "╚════════════════════════════════════════════════════════════╝"
+echo ""
+echo -e "${GREEN}✓ Good practices:  $PASS_COUNT${NC}"
+echo -e "${YELLOW}⚠ Improvements:    $WARN_COUNT${NC}"
+echo -e "${RED}✗ Issues found:    $FAIL_COUNT${NC}"
+echo ""
+
+TOTAL=$((PASS_COUNT + FAIL_COUNT + WARN_COUNT))
+if [ $TOTAL -gt 0 ]; then
+    SCORE=$(( ((PASS_COUNT * 2 + WARN_COUNT) * 100) / (TOTAL * 2) ))
+    echo "Query Score: $SCORE%"
+    echo ""
+fi
+
+print_info "Recommended Next Steps:"
+echo "  1. Run EXPLAIN ANALYZE on this query"
+echo "  2. Create recommended indexes"
+echo "  3. Test query performance before/after"
+echo "  4. Monitor query in production"
+echo ""
+
+print_info "EXPLAIN Commands:"
+echo "  PostgreSQL: EXPLAIN ANALYZE <query>"
+echo "  MySQL:      EXPLAIN <query>"
+echo "  MongoDB:    db.collection.find().explain('executionStats')"
+echo ""
+
+[ $FAIL_COUNT -gt 0 ] && exit 1 || exit 0