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Skills/Backend & APIs
github avatar

Postgresql Code Review

github/awesome-copilotOfficial

The postgresql-code-review skill performs expert code reviews specifically for PostgreSQL projects, evaluating code against PostgreSQL best practices and identifying anti-patterns in areas like JSONB operations, array usage, schema design, custom types, and security features such as Row Level Security. It targets PostgreSQL developers and database architects who need to ensure their code adheres to PostgreSQL-specific quality standards rather than generic SQL conventions. The skill solves the problem of applying database-agnostic review practices to PostgreSQL codebases, where PostgreSQL-exclusive features and optimization techniques are often overlooked.

installs
12.6K
GitHub stars
40.5K
last updated
Aug 21, 2026
View on GitHubSave

Install

$ npx -y skills add github/awesome-copilot --skill postgresql-code-review --agent claude-code

Installs into .claude/skills of the current project.

Files

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');

🛡️ PostgreSQL Security Review

Row Level Security (RLS)

-- ✅ GOOD: Implementing RLS
ALTER TABLE sensitive_data ENABLE ROW LEVEL SECURITY;

CREATE POLICY user_data_policy ON sensitive_data
    FOR ALL TO application_role
    USING (user_id = current_setting('app.current_user_id')::INTEGER);

Privilege Management

-- ❌ BAD: Overly broad permissions
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA public TO app_user;

-- ✅ GOOD: Granular permissions
GRANT SELECT, INSERT, UPDATE ON specific_table TO app_user;
GRANT USAGE ON SEQUENCE specific_table_id_seq TO app_user;

🎯 PostgreSQL Code Quality Checklist

Schema Design

  • Using appropriate PostgreSQL data types (CITEXT, JSONB, arrays)
  • Leveraging ENUM types for constrained values
  • Implementing proper CHECK constraints
  • Using TIMESTAMPTZ instead of TIMESTAMP
  • Defining custom domains for reusable constraints

Performance Considerations

  • Appropriate index types (GIN for JSONB/arrays, GiST for ranges)
  • JSONB queries using containment operators (@>, ?)
  • Array operations using PostgreSQL-specific operators
  • Proper use of window functions and CTEs
  • Efficient use of PostgreSQL-specific functions

PostgreSQL Features Utilization

  • Using extensions where appropriate
  • Implementing stored procedures in PL/pgSQL when beneficial
  • Leveraging PostgreSQL's advanced SQL features
  • Using PostgreSQL-specific optimization techniques
  • Implementing proper error handling in functions

Security and Compliance

  • Row Level Security (RLS) implementation where needed
  • Proper role and privilege management
  • Using PostgreSQL's built-in encryption functions
  • Implementing audit trails with PostgreSQL features

📝 PostgreSQL-Specific Review Guidelines

  1. Data Type Optimization: Ensure PostgreSQL-specific types are used appropriately
  2. Index Strategy: Review index types and ensure PostgreSQL-specific indexes are utilized
  3. JSONB Structure: Validate JSONB schema design and query patterns
  4. Function Quality: Review PL/pgSQL functions for efficiency and best practices
  5. Extension Usage: Verify appropriate use of PostgreSQL extensions
  6. Performance Features: Check utilization of PostgreSQL's advanced features
  7. Security Implementation: Review PostgreSQL-specific security features

Focus on PostgreSQL's unique capabilities and ensure the code leverages what makes PostgreSQL special rather than treating it as a generic SQL database.

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Details

Repository
github.com/github/awesome-copilot
Categories
Backend & APIs, Security, Git & Pull Requests, Code Review & Quality, AI & Agent Building, Design & UI/UX, Databases

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