production-code-audit
Audits entire codebases line-by-line to fix architectural, security, and performance issues.
Install
mkdir -p .claude/skills/production-code-audit && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4030" && unzip -o skill.zip -d .claude/skills/production-code-audit && rm skill.zipInstalls to .claude/skills/production-code-audit
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizationsKey capabilities
- →Perform recursive codebase scanning to map architecture and dependencies
- →Identify security vulnerabilities including SQL injection and hardcoded secrets
- →Detect performance bottlenecks like N+1 queries and inefficient algorithms
- →Refactor code to improve maintainability and reduce cyclomatic complexity
- →Generate production infrastructure including logging, monitoring, and CI/CD pipelines
- →Produce a detailed audit report with before and after metrics
How it works
The skill autonomously scans the project to identify architectural, security, and performance issues before applying systematic code transformations and infrastructure additions. It verifies the results by running tests and calculating performance improvements.
Inputs & outputs
When to use production-code-audit
- →Prepare codebase for deployment
- →Audit code for security and performance
- →Refactor messy code to production standards
About this skill
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:
- Read all files - Scan every file in the project recursively
- Identify tech stack - Detect languages, frameworks, databases, tools
- Understand architecture - Map out structure, patterns, dependencies
- Identify purpose - Understand what the application does
- Find entry points - Locate main files, routes, controllers
- 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(n²) 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:
- Refactor architecture - Break up god classes, fix circular dependencies
- Fix security issues - Use parameterized queries, remove secrets, add validation
- Optimize performance - Fix N+1 queries, add caching, optimize algorithms
- Improve code quality - Reduce complexity, remove duplication, fix naming
- Add missing tests - Write tests for untested critical paths
- Add production infrastructure - Logging, monitoring, health checks
- Optimize everything - Bundle size, images, database queries
- Add documentation - README, API docs, architecture docs
Step 4: Verify and Report
After making all changes:
- Run all tests to ensure nothing broke
- Verify all security issues are fixed
- Measure performance improvements
- Generate comprehensive report
- 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:
- 🔴 CRITICAL: 8 issues
- 🟠 HIGH: 23 issues
- 🟡 MEDIUM: 41 issues
- 🔵 LOW: 55 issues
**Phase 3: Fixing Issues Automatically**
🔴 CRITICAL FIXES (8/8 completed):
1. ✅ Fixed SQL injection in UserRepository.findByEmail()
- Before: String concatenation in query
- After: Parameterized query with $1 placeholder
2. ✅ Removed hardcoded database password from config/database.ts
- Before: password: 'SuperSecret123!'
- After: password: process.env.DB_PASSWORD
3. ✅ Added authentication to admin routes
- Before: No auth middleware
- After: authenticateToken + requireRole('admin')
4. ✅ Fixed weak password hashing (MD5 → bcrypt)
- Before: crypto.createHash('md5')
- After: bcrypt.hash(password, 12)
5. ✅ Fixed circular dependency: OrderService ↔ PaymentService
- Before: Direct imports causing cycle
- After: Event-based decoupling with EventBus
6. ✅ Added input validation to all API endpoints
- Before: No validation
- After: Zod schemas validating all inputs
7. ✅ Fixed authentication bypass in auth middleware
- Before: next() called on missing token
- After: Returns 401 error
8. ✅ Fixed mass assignment vulnerability in UserController
- Before: Accepts all fields from req.body
- After: Whitelist of allowed fields only
🟠 HIGH PRIORITY FIXES (23/23 completed):
9. ✅ Fixed N+1 query in OrderService.getOrdersWithItems()
- Before: Loop with individual queries (2.8s)
- After: Single query with joins (45ms)
- Performance: 98% improvement
10. ✅ Refactored UserService god class (850 lines → 4 focused services)
- UserAuthenticationService (login, logout, password)
- UserProfileService (CRUD operations)
- UserNotificationService (email, SMS)
- UserPermissionService (roles, permissions)
11. ✅ Added Redis caching for product queries
- Before: Database query every request
- After: Cache with 1-hour TTL
- Performance: 95% faster
12. ✅ Optimized bundle size
- Before: 850KB (gzipped: 280KB)
- After: 380KB (gzipped: 120KB)
- Improvement: 55% reduction
13. ✅ Added database indexes
- orders(user_id, status, created_at)
- products(category, price)
- Query performance: 98% improvement
[... 11 more high-priority fixes ...]
🟡 MEDIUM PRIORITY FIXES (41/41 completed):
[... all medium fixes ...]
🔵 LOW PRIORITY IMPROVEMENTS (55/55 completed):
[... all low improvements ...]
**Phase 4: Adding Production Infrastructure**
✅ Added structured logging (Winston)
✅ Added error tracking (Sentry)
✅ Added health check endpoints (/health, /ready)
✅ Added monitoring (Prometheus metrics)
✅ Added rate limiting (Redis-based)
✅ Added API documentation (Swagger/OpenAPI)
✅ Added comprehensive README
✅ Added deployment guide
✅ Added CI/CD pipeline (.github/workflows)
**Phase 5: Verification**
✅ All tests passing (1,247 tests)
✅ Test coverage: 42% → 87%
✅ Security scan: 0 vulnerabilities
✅ Performance: API response time 3.2s → 180ms
✅ Bundle size: 850KB → 380KB
✅ Lighthouse score: 62 → 94
**TRANSFORMATION COMPLETE**
Your codebase is now production-ready with enterprise-grade quality:
📊 Metrics:
- Issues fixed: 127/127 (100%)
- Test coverage: 87% (target: 80%+)
- API performance: 94% improvement
- Security: OWASP Top 10 compliant
- Code quality: Grade A-
📁 Files changed: 89 files
📝 Lines changed: +3,247 / -1,892
🚀 Ready for production deployment!
Example 2: Automatic Security Hardening
User: @production-code-audit secure this codebase
AI: Scanning for security vulnerabilities...
**Security Scan Complete**
Found 15 security issues:
- 🔴 CRITICAL: 5
- 🟠 HIGH: 7
- 🟡 MEDIUM: 3
**Fixing all security issues automatically...**
🔴 CRITICAL (5/5 fixed):
1. ✅ SQL Injection in UserRepository
\`\`\`typescript
// Before (VULNERABLE)
const query = \`SELECT * FROM users WHERE email = '\${email}'\`;
// After (SECURE)
const query = 'SELECT * FROM users WHERE email = $1';
await db.query(query, [email]);
\`\`\`
2. ✅ Hardcoded Secrets Removed
\`\`\`typescript
// Before (INSECURE)
const JWT_SECRET = 'my-secret-key-123';
// After (SECURE)
const JWT_SECRET = process.env.JWT_SECRET;
if (!JWT_SECRET) throw new Error('JWT_SECRET required');
\`\`\`
3. ✅ Authentication Added to Admin Routes
\`\`\`typescript
// Before (VULNERABLE)
router.delete('/api/admin/users/:id', deleteUser);
// After (SECURE)
router.delete('/api/admin/users/:id',
authenticateToken,
requireRole('admin'),
deleteUser
);
\`\`\`
4. ✅ Password Hashing Upgraded
\`\`\`typescript
// Before (WEAK)
const hash = crypto.createHash('md5').update(password).digest('hex');
// After (STRONG)
const hash = await bcrypt.hash(password, 12);
\`\`\`
5. ✅ Input Validation Added
\`\`\`typescript
// Before (VULNERABLE)
const user = await db.user.create({ data: req.body });
// After (SECURE)
const schema = z.object({
email: z.string().email(),
password: z.string().min(12),
name: z.string().min(2).max(100)
});
const validated = schema.parse(req.body);
const user = await db.user.create({ data: validated });
\`\`\`
**Security Status:**
- ✅ OWASP Top 10: Compliant
- ✅ SQL Injection: Protected
- ✅ XSS: Protected
- ✅ CSRF: Protected
- ✅ Authentication: Secure
- ✅ Secrets: Environment variables
- ✅ Dependencies: No vulnerabilities
🔒 Codebase is now secure and production-ready!
Example 3: Performance Optimization
User: @production-code-audit optimize performance
AI: Analyzing performance bottlenecks...
**Performance Analysis Complete**
Current metrics:
- API response time: 3.2s (target: <200ms)
- Page load time: 8.5s (target: <2s)
- Bundle size: 850KB (target: <200KB)
**Optimizing automatically...**
✅ Fixed N+1 queries (3.2s → 180ms - 94% faster)
✅ Added Redis caching (95% cache hit rate)
✅ Optimized database indexes (98% faster queries)
✅ Reduced b
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*Content truncated.*
When not to use it
- →When the codebase is too large to process within a single session
- →When the project requires manual verification of automated changes
- →When the team prefers to manage technical debt through manual sprint planning
Limitations
- →May produce false positives that require manual developer verification
- →Can generate a high volume of issues that may overwhelm small teams
How it compares
Unlike manual code reviews, this skill performs autonomous line-by-line scanning and applies fixes directly to the codebase.
Compared to similar skills
production-code-audit side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| production-code-audit (this skill) | 1 | 6mo | Review | Advanced |
| audit-project | 0 | 4mo | Review | Advanced |
| contrib-pr-review | 1 | 26d | Review | Intermediate |
| tech-debt | 1 | 2mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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