npx skills add ...
npx skills add github/awesome-copilot --skill sql-code-review
npx skills add github/awesome-copilot --skill sql-code-review
Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). Focuses on SQL injection prevention, access control, code standards, and anti-pattern detection. Complements SQL optimization prompt for complete development coverage.
Perform a thorough SQL code review of ${selection} (or entire project if no selection) focusing on security, performance, maintainability, and database best practices.
After:
Expected Improvement: [Performance gain, security benefit]
-- ❌ BAD: Inefficient query patterns
SELECT DISTINCT u.*
FROM users u, orders o, products p
WHERE u.id = o.user_id
AND o.product_id = p.id
AND YEAR(o.order_date) = 2024;
-- ✅ GOOD: Optimized structure
SELECT u.id, u.name, u.email
FROM users u
INNER JOIN orders o ON u.id = o.user_id
WHERE o.order_date >= '2024-01-01'
AND o.order_date < '2025-01-01';-- ❌ BAD: Inefficient aggregation
SELECT user_id,
(SELECT COUNT(*) FROM orders o2 WHERE o2.user_id = o1.user_id) as order_count
FROM orders o1
GROUP BY user_id;
-- ✅ GOOD: Efficient aggregation
SELECT user_id, COUNT(*) as order_count
FROM orders
GROUP BY user_id;-- ❌ BAD: Poor formatting and style
select u.id,u.name,o.total from users u left join orders o on u.id=o.user_id where u.status='active' and o.order_date>='2024-01-01';
-- ✅ GOOD: Clean, readable formatting
SELECT u.id,
u.name,
o.total
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.status = 'active'
AND o.order_date >= '2024-01-01';-- Use JSONB for JSON data
CREATE TABLE events (
id SERIAL PRIMARY KEY,
data JSONB NOT NULL,
created_at TIMESTAMPTZ DEFAULT NOW()
);
-- GIN index for JSONB queries
CREATE INDEX idx_events_data ON events USING gin(data);
-- Array types for multi-value columns
CREATE TABLE tags (
post_id INT,
tag_names TEXT[]
);-- Use appropriate storage engines
CREATE TABLE sessions (
id VARCHAR(128) PRIMARY KEY,
data TEXT,
expires TIMESTAMP
) ENGINE=InnoDB;
-- Optimize for InnoDB
ALTER TABLE large_table
ADD INDEX idx_covering (status, created_at, id);-- Use appropriate data types
CREATE TABLE products (
id BIGINT IDENTITY(1,1) PRIMARY KEY,
name NVARCHAR(255) NOT NULL,
price DECIMAL(10,2) NOT NULL,
created_at DATETIME2 DEFAULT GETUTCDATE()
);
-- Columnstore indexes for analytics
CREATE COLUMNSTORE INDEX idx_sales_cs ON sales;-- Use sequences for auto-increment
CREATE SEQUENCE user_id_seq START WITH 1 INCREMENT BY 1;
CREATE TABLE users (
id NUMBER DEFAULT user_id_seq.NEXTVAL PRIMARY KEY,
name VARCHAR2(255) NOT NULL
);-- Verify referential integrity
SELECT o.user_id
FROM orders o
LEFT JOIN users u ON o.user_id = u.id
WHERE u.id IS NULL;
-- Check for data consistency
SELECT COUNT(*) as inconsistent_records
FROM products
WHERE price < 0 OR stock_quantity < 0;-- ❌ BAD: N+1 queries in application code
for user in users:
orders = query("SELECT * FROM orders WHERE user_id = ?", user.id)
-- ✅ GOOD: Single optimized query
SELECT u.*, o.*
FROM users u
LEFT JOIN orders o ON u.id = o.user_id;-- ❌ BAD: DISTINCT masking join issues
SELECT DISTINCT u.name
FROM users u, orders o
WHERE u.id = o.user_id;
-- ✅ GOOD: Proper join without DISTINCT
SELECT u.name
FROM users u
INNER JOIN orders o ON u.id = o.user_id
GROUP BY u.name;-- ❌ BAD: Functions prevent index usage
SELECT * FROM orders
WHERE YEAR(order_date) = 2024;
-- ✅ GOOD: Range conditions use indexes
SELECT * FROM orders
WHERE order_date >= '2024-01-01'
AND order_date < '2025-01-01';