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name: senior-fullstack description: Fullstack development toolkit with project scaffolding for Next.js/FastAPI/MERN/Django stacks and code quality analysis. Use when scaffolding new projects, analyzing codebase quality, or implementing fullstack architecture patterns.

Senior Fullstack

Fullstack development skill with project scaffolding and code quality analysis tools.

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Table of Contents

• Trigger Phrases
• Tools
• Workflows
• Reference Guides

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Trigger Phrases

Use this skill when you hear:

• "scaffold a new project"
• "create a Next.js app"
• "set up FastAPI with React"
• "analyze code quality"
• "check for security issues in codebase"
• "what stack should I use"
• "set up a fullstack project"
• "generate project boilerplate"

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Tools

Project Scaffolder

Generates fullstack project structures with boilerplate code.

Supported Templates:

• nextjs - Next.js 14+ with App Router, TypeScript, Tailwind CSS
• fastapi-react - FastAPI backend + React frontend + PostgreSQL
• mern - MongoDB, Express, React, Node.js with TypeScript
• django-react - Django REST Framework + React frontend

Usage:

# List available templates
python scripts/project_scaffolder.py --list-templates

# Create Next.js project
python scripts/project_scaffolder.py nextjs my-app

# Create FastAPI + React project
python scripts/project_scaffolder.py fastapi-react my-api

# Create MERN stack project
python scripts/project_scaffolder.py mern my-project

# Create Django + React project
python scripts/project_scaffolder.py django-react my-app

# Specify output directory
python scripts/project_scaffolder.py nextjs my-app --output ./projects

# JSON output
python scripts/project_scaffolder.py nextjs my-app --json

Parameters:

Output includes:

• Project structure with all necessary files
• Package configurations (package.json, requirements.txt)
• TypeScript configuration
• Docker and docker-compose setup
• Environment file templates
• Next steps for running the project

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Code Quality Analyzer

Analyzes fullstack codebases for quality issues.

Analysis Categories:

• Security vulnerabilities (hardcoded secrets, injection risks)
• Code complexity metrics (cyclomatic complexity, nesting depth)
• Dependency health (outdated packages, known CVEs)
• Test coverage estimation
• Documentation quality

Usage:

# Analyze current directory
python scripts/code_quality_analyzer.py .

# Analyze specific project
python scripts/code_quality_analyzer.py /path/to/project

# Verbose output with detailed findings
python scripts/code_quality_analyzer.py . --verbose

# JSON output
python scripts/code_quality_analyzer.py . --json

# Save report to file
python scripts/code_quality_analyzer.py . --output report.json

Parameters:

Output includes:

• Overall score (0-100) with letter grade
• Security issues by severity (critical, high, medium, low)
• High complexity files
• Vulnerable dependencies with CVE references
• Test coverage estimate
• Documentation completeness
• Prioritized recommendations

Sample Output:

============================================================
CODE QUALITY ANALYSIS REPORT
============================================================

Overall Score: 75/100 (Grade: C)
Files Analyzed: 45
Total Lines: 12,500

--- SECURITY ---
  Critical: 1
  High: 2
  Medium: 5

--- COMPLEXITY ---
  Average Complexity: 8.5
  High Complexity Files: 3

--- RECOMMENDATIONS ---
1. [P0] SECURITY
   Issue: Potential hardcoded secret detected
   Action: Remove or secure sensitive data at line 42

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Workflows

Workflow 1: Start New Project

1. Choose appropriate stack based on requirements
2. Scaffold project structure
3. Run initial quality check
4. Set up development environment

# 1. Scaffold project
python scripts/project_scaffolder.py nextjs my-saas-app

# 2. Navigate and install
cd my-saas-app
npm install

# 3. Configure environment
cp .env.example .env.local

# 4. Run quality check
python ../scripts/code_quality_analyzer.py .

# 5. Start development
npm run dev

Workflow 2: Audit Existing Codebase

1. Run code quality analysis
2. Review security findings
3. Address critical issues first
4. Plan improvements

# 1. Full analysis
python scripts/code_quality_analyzer.py /path/to/project --verbose

# 2. Generate detailed report
python scripts/code_quality_analyzer.py /path/to/project --json --output audit.json

# 3. Address P0 issues immediately
# 4. Create tickets for P1/P2 issues

Workflow 3: Stack Selection

Use the tech stack guide to evaluate options:

1. SEO Required? → Next.js with SSR
2. API-heavy backend? → Separate FastAPI or NestJS
3. Real-time features? → Add WebSocket layer
4. Team expertise → Match stack to team skills

See references/tech_stack_guide.md for detailed comparison.

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Reference Guides

Architecture Patterns (references/architecture_patterns.md)

• Frontend component architecture (Atomic Design, Container/Presentational)
• Backend patterns (Clean Architecture, Repository Pattern)
• API design (REST conventions, GraphQL schema design)
• Database patterns (connection pooling, transactions, read replicas)
• Caching strategies (cache-aside, HTTP cache headers)
• Authentication architecture (JWT + refresh tokens, sessions)

Development Workflows (references/development_workflows.md)

• Local development setup (Docker Compose, environment config)
• Git workflows (trunk-based, conventional commits)
• CI/CD pipelines (GitHub Actions examples)
• Testing strategies (unit, integration, E2E)
• Code review process (PR templates, checklists)
• Deployment strategies (blue-green, canary, feature flags)
• Monitoring and observability (logging, metrics, health checks)

Tech Stack Guide (references/tech_stack_guide.md)

• Frontend frameworks comparison (Next.js, React+Vite, Vue)
• Backend frameworks (Express, Fastify, NestJS, FastAPI, Django)
• Database selection (PostgreSQL, MongoDB, Redis)
• ORMs (Prisma, Drizzle, SQLAlchemy)
• Authentication solutions (Auth.js, Clerk, custom JWT)
• Deployment platforms (Vercel, Railway, AWS)
• Stack recommendations by use case (MVP, SaaS, Enterprise)

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Quick Reference

Stack Decision Matrix

Common Issues