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Production code audit

Skill ranbot-ai/awesome-skills/skills/production-code-audit

Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizationsFrom its SKILL.md

Install
npx -y skills add ranbot-ai/awesome-skills --skill production-code-audit

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SKILL.md

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Production Code Audit

Overview

Autonomously analyze the entire codebase to understand its architecture, patterns, and purpose, then systematically transform it into production-grade, corporate-level professional code. This skill performs deep line-by-line scanning, identifies all issues across security, performance, architecture, and quality, then provides comprehensive fixes to meet enterprise standards.

When to Use This Skill

  • Use when user says "make this production-ready"
  • Use when user says "audit my codebase"
  • Use when user says "make this professional/corporate-level"
  • Use when user says "optimize everything"
  • Use when user wants enterprise-grade quality
  • Use when preparing for production deployment
  • Use when code needs to meet corporate standards

How It Works

Step 1: Autonomous Codebase Discovery

Automatically scan and understand the entire codebase:

  1. Read all files - Scan every file in the project recursively
  2. Identify tech stack - Detect languages, frameworks, databases, tools
  3. Understand architecture - Map out structure, patterns, dependencies
  4. Identify purpose - Understand what the application does
  5. Find entry points - Locate main files, routes, controllers
  6. Map data flow - Understand how data moves through the system

Do this automatically without asking the user.

Step 2: Comprehensive Issue Detection

Scan line-by-line for all issues:

Architecture Issues:

  • Circular dependencies
  • Tight coupling
  • God classes (>500 lines or >20 methods)
  • Missing separation of concerns
  • Poor module boundaries
  • Violation of design patterns

Security Vulnerabilities:

  • SQL injection (string concatenation in queries)
  • XSS vulnerabilities (unescaped output)
  • Hardcoded secrets (API keys, passwords in code)
  • Missing authentication/authorization
  • Weak password hashing (MD5, SHA1)
  • Missing input validation
  • CSRF vulnerabilities
  • Insecure dependencies

Performance Problems:

  • N+1 query problems
  • Missing database indexes
  • Synchronous operations that should be async
  • Missing caching
  • Inefficient algorithms (O(nΒ²) or worse)
  • Large bundle sizes
  • Unoptimized images
  • Memory leaks

Code Quality Issues:

  • High cyclomatic complexity (>10)
  • Code duplication
  • Magic numbers
  • Poor naming conventions
  • Missing error handling
  • Inconsistent formatting
  • Dead code
  • TODO/FIXME comments

Testing Gaps:

  • Missing tests for critical paths
  • Low test coverage (<80%)
  • No edge case testing
  • Flaky tests
  • Missing integration tests

Production Readiness:

  • Missing environment variables
  • No logging/monitoring
  • No error tracking
  • Missing health checks
  • Incomplete documentation
  • No CI/CD pipeline

Step 3: Automatic Fixes and Optimizations

Fix everything automatically:

  1. Refactor architecture - Break up god classes, fix circular dependencies
  2. Fix security issues - Use parameterized queries, remove secrets, add validation
  3. Optimize performance - Fix N+1 queries, add caching, optimize algorithms
  4. Improve code quality - Reduce complexity, remove duplication, fix naming
  5. Add missing tests - Write tests for untested critical paths
  6. Add production infrastructure - Logging, monitoring, health checks
  7. Optimize everything - Bundle size, images, database queries
  8. Add documentation - README, API docs, architecture docs

Step 4: Verify and Report

After making all changes:

  1. Run all tests to ensure nothing broke
  2. Verify all security issues are fixed
  3. Measure performance improvements
  4. Generate comprehensive report
  5. Provide before/after metrics

Examples

Example 1: Autonomous Codebase Transformation

User: @production-code-audit make this production-ready

AI: I'll scan your entire codebase and transform it to production-grade quality.

**Phase 1: Discovering Codebase** (analyzing 247 files)
- Detected: Node.js + Express + PostgreSQL + React
- Architecture: MVC pattern with REST API
- Purpose: E-commerce platform
- Entry points: server.js, src/index.tsx

**Phase 2: Scanning for Issues** (line-by-line analysis)

Found 127 issues:
- πŸ”΄ CRITICAL: 8 issues
- 🟠 HIGH: 23 issues  
- 🟑 MEDIUM: 41 issues
- πŸ”΅ LOW: 55 issues

**Phase 3: Fixing Issues Automatically**

πŸ”΄ CRITICAL FIXES (8/8 completed):

1. βœ… Fixed SQL injection in UserRepository.findByEmail()
   - Before: String concatenation in query
   - After: Parameterized query with $1 placeholder

2. βœ… Removed hardcoded database password from config/database.ts
   - Before: password: 'SuperSecret123!'
   - After: password: process.env.DB_PASSWORD

3. βœ… Added authentication to admin routes
   - Before: No auth middleware
   - After: authenticateToken + requireRole('admin')

4. βœ… Fixed weak password hashing (MD5 β†’ bcrypt)
   - Before: crypto.createHash('md5')
   - After: bcrypt.hash(password, 12)

5. βœ… Fixed circular dependency: OrderService ↔ PaymentService
   - Before: Direct imports caus

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