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Official agent skill

SQL Code Review

by github in github/awesome-copilot

Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle).

OfficialMITAuto-check passedDatabases

Install SQL Code Review

skills CLI
$ npx skills add github/awesome-copilot --skill sql-code-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot sql-code-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/sql-code-review .claude/skills/sql-code-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
sql-code-review
GitHub stars
40k
Used in
1 other repo
Token cost
~2.2k tokens
SKILL.md length
500 words
Files
1
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle).

  • Works in 3 steps: [Critical Security Fix]: Address SQL… → [Performance Optimization]: Add missing… → [Code Quality]: Improve naming…
  • Tasks that involve SQL
  • SKILL.md covers 🔒 Security Analysis, ⚡ Performance Optimization, 🛠️ Code Quality &… and 🗄️ Database-Specific Best…, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

SQL Code Review is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. 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.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering SQL, Code quality and Code review. It works with SQL, Microsoft SQL Server, MySQL and PostgreSQL. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • Tasks that involve SQL
  • Tasks that involve Code quality
  • Tasks that involve Code review

Example prompts

  • “/sql-code-review”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. [Critical Security Fix]: Address SQL injection vulnerabilities
  2. [Performance Optimization]: Add missing indexes or optimize queries
  3. [Code Quality]: Improve naming conventions and documentation

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are sql).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SQL Code Review loads about 2.2k tokens when it runs. Until then it costs about 92 tokens; SKILL.md has 500 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 500 words, ~2,220 tokens.

Download SKILL.mdSave it as .claude/skills/sql-code-review/SKILL.md (or your agent's skills folder).
name
sql-code-review
description
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.

SQL Code Review

Perform a thorough SQL code review of ${selection} (or entire project if no selection) focusing on security, performance, maintainability, and database best practices.

🔒 Security Analysis

SQL Injection Prevention
sql
-- ❌ CRITICAL: SQL Injection vulnerability
query = "SELECT * FROM users WHERE id = " + userInput;
query = f"DELETE FROM orders WHERE user_id = {user_id}";

-- ✅ SECURE: Parameterized queries
-- PostgreSQL/MySQL
PREPARE stmt FROM 'SELECT * FROM users WHERE id = ?';
EXECUTE stmt USING @user_id;

-- SQL Server
EXEC sp_executesql N'SELECT * FROM users WHERE id = @id', N'@id INT', @id = @user_id;
Access Control & Permissions
  • Principle of Least Privilege: Grant minimum required permissions
  • Role-Based Access: Use database roles instead of direct user permissions
  • Schema Security: Proper schema ownership and access controls
  • Function/Procedure Security: Review DEFINER vs INVOKER rights
Data Protection
  • Sensitive Data Exposure: Avoid SELECT * on tables with sensitive columns
  • Audit Logging: Ensure sensitive operations are logged
  • Data Masking: Use views or functions to mask sensitive data
  • Encryption: Verify encrypted storage for sensitive data

⚡ Performance Optimization

Query Structure Analysis
sql
-- ❌ 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';
Index Strategy Review
  • Missing Indexes: Identify columns that need indexing
  • Over-Indexing: Find unused or redundant indexes
  • Composite Indexes: Multi-column indexes for complex queries
  • Index Maintenance: Check for fragmented or outdated indexes
Join Optimization
  • Join Types: Verify appropriate join types (INNER vs LEFT vs EXISTS)
  • Join Order: Optimize for smaller result sets first
  • Cartesian Products: Identify and fix missing join conditions
  • Subquery vs JOIN: Choose the most efficient approach
Aggregate and Window Functions
sql
-- ❌ 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;

🛠️ Code Quality & Maintainability

SQL Style & Formatting
sql
-- ❌ 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';
Naming Conventions
  • Consistent Naming: Tables, columns, constraints follow consistent patterns
  • Descriptive Names: Clear, meaningful names for database objects
  • Reserved Words: Avoid using database reserved words as identifiers
  • Case Sensitivity: Consistent case usage across schema
Schema Design Review
  • Normalization: Appropriate normalization level (avoid over/under-normalization)
  • Data Types: Optimal data type choices for storage and performance
  • Constraints: Proper use of PRIMARY KEY, FOREIGN KEY, CHECK, NOT NULL
  • Default Values: Appropriate default values for columns

🗄️ Database-Specific Best Practices

PostgreSQL
sql
-- 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[]
);
MySQL
sql
-- 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);
SQL Server
sql
-- 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;
Oracle
sql
-- 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
);

🧪 Testing & Validation

Data Integrity Checks
sql
-- 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;
Performance Testing
  • Execution Plans: Review query execution plans
  • Load Testing: Test queries with realistic data volumes
  • Stress Testing: Verify performance under concurrent load
  • Regression Testing: Ensure optimizations don't break functionality
Show full SKILL.md (200 more words)Show less

📊 Common Anti-Patterns

N+1 Query Problem
sql
-- ❌ 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;
Overuse of DISTINCT
sql
-- ❌ 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;
Function Misuse in WHERE Clauses
sql
-- ❌ 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';

📋 SQL Review Checklist

Security
  • All user inputs are parameterized
  • No dynamic SQL construction with string concatenation
  • Appropriate access controls and permissions
  • Sensitive data is properly protected
  • SQL injection attack vectors are eliminated
Performance
  • Indexes exist for frequently queried columns
  • No unnecessary SELECT * statements
  • JOINs are optimized and use appropriate types
  • WHERE clauses are selective and use indexes
  • Subqueries are optimized or converted to JOINs
Code Quality
  • Consistent naming conventions
  • Proper formatting and indentation
  • Meaningful comments for complex logic
  • Appropriate data types are used
  • Error handling is implemented
Schema Design
  • Tables are properly normalized
  • Constraints enforce data integrity
  • Indexes support query patterns
  • Foreign key relationships are defined
  • Default values are appropriate

🎯 Review Output Format

Issue Template
## [PRIORITY] [CATEGORY]: [Brief Description]

**Location**: [Table/View/Procedure name and line number if applicable]
**Issue**: [Detailed explanation of the problem]
**Security Risk**: [If applicable - injection risk, data exposure, etc.]
**Performance Impact**: [Query cost, execution time impact]
**Recommendation**: [Specific fix with code example]

**Before**:
```sql
-- Problematic SQL
```

**After**:
```sql
-- Improved SQL
```

**Expected Improvement**: [Performance gain, security benefit]
Summary Assessment
  • Security Score: [1-10] - SQL injection protection, access controls
  • Performance Score: [1-10] - Query efficiency, index usage
  • Maintainability Score: [1-10] - Code quality, documentation
  • Schema Quality Score: [1-10] - Design patterns, normalization
Top 3 Priority Actions
  1. [Critical Security Fix]: Address SQL injection vulnerabilities
  2. [Performance Optimization]: Add missing indexes or optimize queries
  3. [Code Quality]: Improve naming conventions and documentation

Focus on providing actionable, database-agnostic recommendations while highlighting platform-specific optimizations and best practices.

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/sql-code-review of github/awesome-copilot.

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

SQL Code Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

SQL Code Review compared with similar skills
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SQL Code Review this skillgithub/awesome-copilot40k1 repos~2.2kAutomated safety check: PassMIT
SQL Code Reviewtotvs/engpro-advpl-tlpp-skills141—~3.5kAutomated safety check: PassMIT
Database Migrations SQL Migrationsrmyndharis/antigravity-skills1.7k2 repos~577Automated safety check: NotesMIT
SQL ProJeffallan/claude-skills12k—~1.3kAutomated safety check: PassMIT
Query Builderthalysjuvenal/advpl-specialist185—~594Automated safety check: PassMIT
Cloud SQL Basicsgoogle/skills21k—~973Automated safety check: PassApache-2.0

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Works with

Categories

Questions about SQL Code Review

What does SQL Code Review do?

Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle). SQL Code Review is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Universal SQL code review assistant that performs comprehensive security, maintainability, and code quality analysis across all SQL databases (MySQL, PostgreSQL, SQL Server, Oracle).

When should I use SQL Code Review?

SQL Code Review fits situations like: tasks that involve SQL; tasks that involve Code quality; tasks that involve Code review.

How do I install SQL Code Review in Claude Code?

Run `npx skills add github/awesome-copilot --skill sql-code-review -a claude-code`. Or copy the skill folder (skills/sql-code-review in github/awesome-copilot) into .claude/skills/sql-code-review in your project. Claude Code loads it when a task matches its description.

How do I install SQL Code Review in Codex?

Run `npx skills add github/awesome-copilot --skill sql-code-review -a codex`. Or copy the skill folder (skills/sql-code-review in github/awesome-copilot) into .agents/skills/sql-code-review in your project. Codex loads it when a task matches its description.

Can I use SQL Code Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill sql-code-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/sql-code-review, .gemini/skills/sql-code-review, .github/skills/sql-code-review and .opencode/skills/sql-code-review in your project.

What does SQL Code Review need to run?

SKILL.md names no scripts, command-line tools or credentials: SQL Code Review is instructions for the agent only.

Does SQL Code Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is SQL Code Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does SQL Code Review use?

SQL Code Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does SQL Code Review use?

About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to SQL Code Review?

Skills that share tags, products or a category with SQL Code Review: SQL Code Review (totvs/engpro-advpl-tlpp-skills, 141 stars), Database Migrations SQL Migrations (rmyndharis/antigravity-skills, 1.7k stars), SQL Pro (Jeffallan/claude-skills, 12k stars) and Query Builder (thalysjuvenal/advpl-specialist, 185 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains SQL Code Review?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.