Postgresql-Code-Review
Postgresql-Code-Review是一款code方向的AI技能,核心价值是PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。
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, functio
mkdir -p ./skills/postgresql-code-review && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/postgresql-code-review/SKILL.md -o ./skills/postgresql-code-review/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# 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_🎯 Best For
- Engineering teams doing code reviews
- Open source maintainers
- Claude users
- GitHub Copilot users
- Software engineers
💡 Use Cases
- Reviewing pull requests for security vulnerabilities
- Checking code style consistency
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Postgresql-Code-Review to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does this skill check for OWASP Top 10?
Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.
Is Postgresql-Code-Review compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Postgresql-Code-Review?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Postgresql-Code-Review?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/postgresql-code-review/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Blindly accepting AI suggestions
Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.