Agent skill

Production Code Audit

by davila7 in davila7/claude-code-templates

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

MITAuto-check: warningsSecurity

Install Production Code Audit

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add davila7/claude-code-templates --skill production-code-audit -a claude-code

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

GitHub CLI
$ gh skill install davila7/claude-code-templates production-code-audit --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/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/development/production-code-audit .claude/skills/production-code-audit && 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
production-code-audit
GitHub stars
32k
Used in
6 other repos
Token cost
~3.9k tokens
SKILL.md length
910 words
Files
1
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 4 steps: Autonomous Codebase Discovery → Comprehensive Issue Detection → Automatic Fixes and Optimizations → …
  • Security work in your project
  • SKILL.md covers Overview, When to Use This Skill, How It Works and Examples, plus 7 more sections
  • Needs JWT_SECRET and DB_PASSWORD

What it does

Production Code Audit is an agent skill from davila7/claude-code-templates. Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

Its SKILL.md is about 3.9k 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 Security. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Security work in your project

Example prompts

  • “/production-code-audit”

Requirements

  • Node.js
  • A credential in JWT_SECRET

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Autonomous Codebase Discovery
  2. Comprehensive Issue Detection
  3. Automatic Fixes and Optimizations
  4. Verify and Report

What it can do on your machine

Read from SKILL.md and the folder at commit 4c82aba. 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 markdown).

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

  • Network

    Links to these hosts (documentation or services it may open):

    • owasp.org
    • google.github.io
    • docs.sonarqube.org
    • amazon.com

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • JWT_SECRET
    • DB_PASSWORD

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

Context cost

Production Code Audit loads about 3.9k tokens when it runs. Until then it costs about 56 tokens; SKILL.md has 910 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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: warnings

The automated check found patterns that need a careful read before installing.

  • WarningTells the agent its actions are pre-authorized / not to stop for confirmationSKILL.md:35
    **Do this automatically without asking the user.**

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 davila7/claude-code-templates at commit 4c82aba, republished under its MIT licence (© davila7). 910 words, ~3,921 tokens.

Download SKILL.mdSave it as .claude/skills/production-code-audit/SKILL.md (or your agent's skills folder).
name
production-code-audit
description
Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

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(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:

  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
markdown
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
markdown
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
markdown
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 bundle size (850KB → 380KB - 55% smaller)
✅ Optimized images (28MB → 3.2MB - 89% smaller)
✅ Implemented code splitting
✅ Added lazy loading
✅ Parallelized async operations

**Performance Results:**

| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| API Response | 3.2s | 180ms | 94% |
| Page Load | 8.5s | 1.8s | 79% |
| Bundle Size | 850KB | 380KB | 55% |
| Image Size | 28MB | 3.2MB | 89% |
| Lighthouse | 42 | 94 | +52 points |

🚀 Performance optimized to production standards!

Best Practices

✅ Do This
  • Scan Everything - Read all files, understand entire codebase
  • Fix Automatically - Don't just report, actually fix issues
  • Prioritize Critical - Security and data loss issues first
  • Measure Impact - Show before/after metrics
  • Verify Changes - Run tests after making changes
  • Be Comprehensive - Cover architecture, security, performance, testing
  • Optimize Everything - Bundle size, queries, algorithms, images
  • Add Infrastructure - Logging, monitoring, error tracking
  • Document Changes - Explain what was fixed and why
Show full SKILL.md (373 more words)Show less
❌ Don't Do This
  • Don't Ask Questions - Understand the codebase autonomously
  • Don't Wait for Instructions - Scan and fix automatically
  • Don't Report Only - Actually make the fixes
  • Don't Skip Files - Scan every file in the project
  • Don't Ignore Context - Understand what the code does
  • Don't Break Things - Verify tests pass after changes
  • Don't Be Partial - Fix all issues, not just some

Autonomous Scanning Instructions

When this skill is invoked, automatically:

  1. Discover the codebase:

    • Use listDirectory to find all files recursively
    • Use readFile to read every source file
    • Identify tech stack from package.json, requirements.txt, etc.
    • Map out architecture and structure
  2. Scan line-by-line for issues:

    • Check every line for security vulnerabilities
    • Identify performance bottlenecks
    • Find code quality issues
    • Detect architectural problems
    • Find missing tests
  3. Fix everything automatically:

    • Use strReplace to fix issues in files
    • Add missing files (tests, configs, docs)
    • Refactor problematic code
    • Add production infrastructure
    • Optimize performance
  4. Verify and report:

    • Run tests to ensure nothing broke
    • Measure improvements
    • Generate comprehensive report
    • Show before/after metrics

Do all of this without asking the user for input.

Common Pitfalls

Problem: Too Many Issues

Symptoms: Team paralyzed by 200+ issues Solution: Focus on critical/high priority only, create sprints

Problem: False Positives

Symptoms: Flagging non-issues Solution: Understand context, verify manually, ask developers

Problem: No Follow-Up

Symptoms: Audit report ignored Solution: Create GitHub issues, assign owners, track in standups

Production Audit Checklist

Security
  • No SQL injection vulnerabilities
  • No hardcoded secrets
  • Authentication on protected routes
  • Authorization checks implemented
  • Input validation on all endpoints
  • Password hashing with bcrypt (10+ rounds)
  • HTTPS enforced
  • Dependencies have no vulnerabilities
Performance
  • No N+1 query problems
  • Database indexes on foreign keys
  • Caching implemented
  • API response time < 200ms
  • Bundle size < 200KB (gzipped)
Testing
  • Test coverage > 80%
  • Critical paths tested
  • Edge cases covered
  • No flaky tests
  • Tests run in CI/CD
Production Readiness
  • Environment variables configured
  • Error tracking setup (Sentry)
  • Structured logging implemented
  • Health check endpoints
  • Monitoring and alerting
  • Documentation complete

Audit Report Template

markdown
# Production Audit Report

**Project:** [Name]
**Date:** [Date]
**Overall Grade:** [A-F]

## Executive Summary
[2-3 sentences on overall status]

**Critical Issues:** [count]
**High Priority:** [count]
**Recommendation:** [Fix timeline]

## Findings by Category

### Architecture (Grade: [A-F])
- Issue 1: [Description]
- Issue 2: [Description]

### Security (Grade: [A-F])
- Issue 1: [Description + Fix]
- Issue 2: [Description + Fix]

### Performance (Grade: [A-F])
- Issue 1: [Description + Fix]

### Testing (Grade: [A-F])
- Coverage: [%]
- Issues: [List]

## Priority Actions
1. [Critical issue] - [Timeline]
2. [High priority] - [Timeline]
3. [High priority] - [Timeline]

## Timeline
- Critical fixes: [X weeks]
- High priority: [X weeks]
- Production ready: [X weeks]
  • @code-review-checklist - Code review guidelines
  • @api-security-best-practices - API security patterns
  • @web-performance-optimization - Performance optimization
  • @systematic-debugging - Debug production issues
  • @senior-architect - Architecture patterns

Additional Resources


Pro Tip: Schedule regular audits (quarterly) to maintain code quality. Prevention is cheaper than fixing production bugs!

© davila7, 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 cli-tool/components/skills/development/production-code-audit of davila7/claude-code-templates.

Open the folder on GitHubat commit 4c82aba

Used in 6 other repositories

We found 15 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 6 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Production Code Audit 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.

Production Code Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Production Code Audit this skilldavila7/claude-code-templates32k6 repos~3.9kAutomated safety check: WarnMIT
Fla Ascend Performancefla-org/flash-linear-attention5.8k—~5.6kAutomated safety check: PassMIT
Deepsec Documentation Guidevercel-labs/deepsec8.1k—~956Automated safety check: PassApache-2.0
Skill Scannergetsentry/skills1k4 repos~2.5kAutomated safety check: WarnApache-2.0
Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit4791 repos~3.3kAutomated safety check: PassNone
Security Alert Triageelastic/agent-skills5921 repos~3.5kAutomated safety check: NotesApache-2.0

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Categories

Questions about Production Code Audit

What does Production Code Audit do?

Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations. Production Code Audit is an agent skill from davila7/claude-code-templates.

When should I use Production Code Audit?

Production Code Audit fits situations like: security work in your project.

How do I install Production Code Audit in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill production-code-audit -a claude-code`. Or copy the skill folder (cli-tool/components/skills/development/production-code-audit in davila7/claude-code-templates) into .claude/skills/production-code-audit in your project. Claude Code loads it when a task matches its description.

How do I install Production Code Audit in Codex?

Run `npx skills add davila7/claude-code-templates --skill production-code-audit -a codex`. Or copy the skill folder (cli-tool/components/skills/development/production-code-audit in davila7/claude-code-templates) into .agents/skills/production-code-audit in your project. Codex loads it when a task matches its description.

Can I use Production Code Audit 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 davila7/claude-code-templates --skill production-code-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/production-code-audit, .gemini/skills/production-code-audit, .github/skills/production-code-audit and .opencode/skills/production-code-audit in your project.

What does Production Code Audit need to run?

Going by SKILL.md and its folder, Production Code Audit needs credentials named JWT_SECRET and DB_PASSWORD. Our summary lists: Node.js; A credential in JWT_SECRET.

Does Production Code Audit access the network?

SKILL.md names 4 domains. As links in the text: owasp.org, google.github.io, docs.sonarqube.org and amazon.com. This is read from the text; nothing was executed.

Is Production Code Audit safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Production Code Audit use?

Production Code Audit 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 Production Code Audit use?

About 3.9k tokens (SKILL.md is roughly 16k 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 Production Code Audit?

Skills that share tags, products or a category with Production Code Audit: Fla Ascend Performance (fla-org/flash-linear-attention, 5.8k stars), Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars) and Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 479 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Production Code Audit?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,432 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 7, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.