Professional quality assessment framework for AI-generated applications and code — comprehensive evaluation covering context, functionality, code quality, security, and deployment readiness.
Vibe coding refers to the practice of using AI tools (Cursor, Bolt, Lovable, v0, Claude, ChatGPT, Replit Agent, Windsurf, GitHub Copilot, etc.) to generate applications through natural language prompts rather than traditional manual coding. While this approach accelerates development, it introduces unique quality considerations that traditional code review practices may not address.
This framework provides enterprise-grade review standards with weighted scoring, professional report templates, and comprehensive quality assessment criteria.
Existing vibe coding resources focus primarily on security or workflow guidance. This checklist fills a gap by providing a complete evaluation framework for QA engineers, tech leads, and consultants who need to assess the full picture:
| This Checklist | Security-Only Checklists |
|---|---|
| ✅ Context & purpose assessment | ❌ |
| ✅ Stub vs. real code detection | ❌ |
| ✅ Hallucinated package verification | ❌ |
| ✅ Code quality & maintainability | ❌ |
| ✅ Security assessment | ✅ |
| ✅ Deployment readiness | ❌ |
| ✅ Review scope definition | ❌ |
| ✅ Weighted scoring system (0-30) | ❌ |
| ✅ Professional report templates | ❌ |
| ✅ Risk level indicators (🟢🟡🟠🔴) | ❌ |
| ✅ Review workflow stages | ❌ |
| ✅ Programmatic JSON schema | ❌ |
- Incomplete implementations — UI looks complete but logic is stubbed
- Security gaps — API keys in frontend, missing auth checks
- Hallucinated dependencies — packages that don't exist or are outdated
- Missing error handling — assumes happy path everywhere
- Technical debt — works now but impossible to maintain
| Format | Description | Use Case |
|---|---|---|
| CHECKLIST.md | Interactive checklist | Step-by-step review with verification guidance |
| TEMPLATE.md | Professional report template | Enterprise-grade review documentation |
| checklist.json | Structured data (v2.0.0) | Automation, tooling, CI/CD integration |
- 📊 Weighted Scoring System — Categories weighted by importance (3-5 stars)
- ⭐ Rating Scale — Automatic rating from Excellent to Major Issues
- 🎨 Visual Indicators — Status symbols (✅
⚠️ ❌) and risk levels (🟢🟡🟠🔴) - 📝 Professional Template — Enterprise-ready review report format
- 🔄 Review Workflow — Defined stages: Draft → In Review → Final
- 🎯 Best Practices — Before/during/after review guidelines
- 📋 Common Pitfalls — Curated list of AI-generated code issues
| Category | Focus Area |
|---|---|
| Context & Purpose | Understanding what and why |
| Technical Stack | Tools, frameworks, dependencies |
| Functional State | What works vs. what doesn't |
| Code Quality | Structure, patterns, maintainability |
| Security Assessment | Vulnerabilities and data handling |
| Deployment Readiness | Production considerations |
| Review Scope | Defining feedback boundaries |
- ✅ Evaluate AI-generated prototypes before testing
- ✅ Generate professional quality reports
- ✅ Track review history and versions
- ✅ Review teammate's vibe-coded features
- ✅ Provide structured feedback
- ✅ Ensure code quality standards
- ✅ Assess POCs and prototypes for production
- ✅ Risk assessment with visual indicators
- ✅ Make data-driven decisions
- ✅ Standardized client deliverable reviews
- ✅ Professional documentation
- ✅ Consistent quality criteria
Weighted Categories — Each area rated 1-5 with importance weights:
| Category | Weight | Description |
|---|---|---|
| Context Clarity | ⭐⭐⭐ | Understanding of purpose |
| Technical Implementation | ⭐⭐⭐⭐ | Technology choices |
| Functional Completeness | ⭐⭐⭐⭐⭐ | Feature completeness |
| Code Quality | ⭐⭐⭐⭐ | Maintainability |
| Security | ⭐⭐⭐⭐⭐ | Vulnerabilities |
| Deployment Readiness | ⭐⭐⭐ | Production preparedness |
Total Score: 0-30 with automatic ratings:
- 25-30 = ⭐⭐⭐⭐⭐ Excellent (Production-ready)
- 20-24 = ⭐⭐⭐⭐ Good
- 15-19 = ⭐⭐⭐ Acceptable
- 10-14 = ⭐⭐ Needs Work
- 0-9 = ⭐ Major Issues
The checklist.json file provides enterprise-grade structured data:
Features:
- ✅ Weighted scoring categories with star ratings
- ✅ Status indicators and risk levels
- ✅ Review workflow stages
- ✅ Best practices guidelines
- ✅ Common AI pitfalls database
- ✅ Role-based review assignments
{
"version": "2.0.0",
"metadata": {
"status_indicators": {"pass": "✅", "warning": "⚠️", "fail": "❌"},
"risk_levels": {"low": "🟢", "medium": "🟡", "high": "🟠", "critical": "🔴"}
},
"scoring": {
"categories": [
{"name": "Security", "weight": 5, "icon": "⭐⭐⭐⭐⭐"}
],
"ratings": {
"excellent": {"range": "25-30", "stars": "⭐⭐⭐⭐⭐"}
}
}
}Use Cases:
- 🤖 Building review automation tools
- 🔄 CI/CD pipeline integration
- 📊 Custom review dashboards
- 📈 Programmatic report generation
- 🎯 Quality gate enforcement
This checklist complements existing vibe coding resources:
| Project | Focus |
|---|---|
| finehq/vibe-coding-checklist | Security-focused checklist with web app |
| astoj/vibe-security | Security checklist for vibe coders |
| filipecalegario/awesome-vibe-coding | Curated list of vibe coding tools |
| analyticalrohit/awesome-vibe-coding-guide | Best practices guide |
| EnzeD/vibe-coding | Step-by-step methodology |
Contributions welcome! Please read CONTRIBUTING.md for guidelines.
MIT License — see LICENSE for details.
Maintained by AI Quality Lab
Part of the AI Quality Engineer initiative — bridging AI with software quality assurance.