postgresql-code-review
Lets your agent review PostgreSQL code, focusing on best practices and patterns. It helps ensure the database is efficient and secure.
What it does
This tool analyzes your PostgreSQL SQL code, checking if it follows best practices and avoids common PostgreSQL-specific pitfalls. It focuses on how you use data types like JSONB and arrays, your database design, and security.
When to use it
- ✓When writing new SQL queries or functions for PostgreSQL.
- ✓When you want to optimize your PostgreSQL database performance.
- ✓To ensure your PostgreSQL code is secure and follows standards.
Example of what to ask your AI
“Review this PostgreSQL SQL code for optimization and best practices: [your SQL code here]”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool compares your code against examples of best practices and common PostgreSQL-specific errors. It identifies areas like index usage, data types (JSONB, arrays, ENUMs), and table design to suggest improvements.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill postgresql-code-reviewOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/postgresql-code-review/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
PostgreSQL Code Review Assistant
Expert PostgreSQL code review for ${selection} (or entire project if no selection). Focus on PostgreSQL-specific best practices, anti-patterns, and quality standards that are unique to PostgreSQL.
🎯 PostgreSQL-Specific Review Areas
JSONB Best Practices
-- ❌ BAD: Inefficient JSONB usage
SELECT * FROM orders WHERE data->>'status' = 'shipped'; -- No index support
-- ✅ GOOD: Indexable JSONB queries
CREATE INDEX idx_orders_status ON orders USING gin((data->'status'));
SELECT * FROM orders WHERE data @> '{"status": "shipped"}';
-- ❌ BAD: Deep nesting without consideration
UPDATE orders SET data = data || '{"shipping":{"tracking":{"number":"123"}}}';
-- ✅ GOOD: Structured JSONB with validation
ALTER TABLE orders ADD CONSTRAINT valid_status
CHECK (data->>'status' IN ('pending', 'shipped', 'delivered'));
Array Operations Review
-- ❌ BAD: Inefficient array operations
SELECT * FROM products WHERE 'electronics' = ANY(categories); -- No index
-- ✅ GOOD: GIN indexed array queries
CREATE INDEX idx_products_categories ON products USING gin(categories);
SELECT * FROM products WHERE categories @> ARRAY['electronics'];
-- ❌ BAD: Array concatenation in loops
-- This would be inefficient in a function/procedure
-- ✅ GOOD: Bulk array operations
UPDATE products SET categories = categories || ARRAY['new_category']
WHERE id IN (SELECT id FROM products WHERE condition);
PostgreSQL Schema Design Review
-- ❌ BAD: Not using PostgreSQL features
CREATE TABLE users (
id INTEGER,
email VARCHAR(255),
created_at TIMESTAMP
);
-- ✅ GOOD: PostgreSQL-optimized schema
CREATE TABLE users (
id BIGSERIAL PRIMARY KEY,
email CITEXT UNIQUE NOT NULL, -- Case-insensitive email
created_at TIMESTAMPTZ DEFAULT NOW(),
metadata JSONB DEFAULT '{}',
CONSTRAINT valid_email CHECK (email ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$')
);
-- Add JSONB GIN index for metadata queries
CREATE INDEX idx_users_metadata ON users USING gin(metadata);
Custom Types and Domains
-- ❌ BAD: Using generic types for specific data
CREATE TABLE transactions (
amount DECIMAL(10,2),
currency VARCHAR(3),
status VARCHAR(20)
);
-- ✅ GOOD: PostgreSQL custom types
CREATE TYPE currency_code AS ENUM ('USD', 'EUR', 'GBP', 'JPY');
CREATE TYPE transaction_status AS ENUM ('pending', 'completed', 'failed', 'cancelled');
CREATE DOMAIN positive_amount AS DECIMAL(10,2) CHECK (VALUE > 0);
CREATE TABLE transactions (
amount positive_amount NOT NULL,
currency currency_code NOT NULL,
status transaction_status DEFAULT 'pending'
);
🔍 PostgreSQL-Specific Anti-Patterns
Performance Anti-Patterns
- Avoiding PostgreSQL-specific indexes: Not using GIN/GiST for appropriate data types
- Misusing JSONB: Treating JSONB like a simple string field
- Ignoring array operators: Using inefficient array operations
- Poor partition key selection: Not leveraging PostgreSQL partitioning effectively
Schema Design Issues
- Not using ENUM types: Using VARCHAR for limited value sets
- Ignoring constraints: Missing CHECK constraints for data validation
- Wrong data types: Using VARCHAR instead of TEXT or CITEXT
- Missing JSONB structure: Unstructured JSONB without validation
Function and Trigger Issues
-- ❌ BAD: Inefficient trigger function
CREATE OR REPLACE FUNCTION update_modified_time()
RETURNS TRIGGER AS $$
BEGIN
NEW.updated_at = NOW(); -- Should use TIMESTAMPTZ
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
-- ✅ GOOD: Optimized trigger function
CREATE OR REPLACE FUNCTION update_modified_time()
RETURNS TRIGGER AS $$
BEGIN
NEW.updated_at = CURRENT_TIMESTAMP;
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
-- Set trigger to fire only when needed
CREATE TRIGGER update_modified_time_trigger
BEFORE UPDATE ON table_name
FOR EACH ROW
WHEN (OLD.* IS DISTINCT FROM NEW.*)
EXECUTE FUNCTION update_modified_time();
📊 PostgreSQL Extension Usage Review
Extension Best Practices
-- ✅ Check if extension exists before creating
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE EXTENSION IF NOT EXISTS "pgcrypto";
CREATE EXTENSION IF NOT EXISTS "pg_trgm";
-- ✅ Use extensions appropriately
-- UUID generation
SELECT uuid_generate_v4();
-- Password hashing
SELECT crypt('password', gen_salt('bf'));
-- Fuzzy text matching
SELECT word_similarity('postgres', 'postgre');
…
FAQ
What are 'anti-patterns'?
Anti-patterns are common solutions to problems that seem to work but actually cause more trouble later on, like poor performance or difficulty in maintenance.
What is JSONB?
JSONB is a data type in PostgreSQL that stores data in JSON format, but in a way that's more efficient for querying and indexing, making it ideal for semi-structured data.
What is RLS (Row Level Security)?
RLS is a PostgreSQL security feature that allows you to control which rows in a table a user can see or modify, based on specific rules.
Details
Original description by the author
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #5274
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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