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:

Parameter	Description
template	Template name (nextjs, fastapi-react, mern, django-react)
project_name	Name for the new project directory
--output, -o	Output directory (default: current directory)
--list-templates, -l	List all available templates
--json	Output in JSON format

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• Dependency health (outdated packages, known CVEs)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:

Parameter	Description
project_path	Path to project directory (default: current directory)
--verbose, -v	Show detailed findings
--json	Output in JSON format
--output, -o	Write report to file

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

Requirement	Recommendation
SEO-critical site	Next.js with SSR
Internal dashboard	React + Vite
API-first backend	FastAPI or Fastify
Enterprise scale	NestJS + PostgreSQL
Rapid prototype	Next.js API routes
Document-heavy data	MongoDB
Complex queries	PostgreSQL

Common Issues

Issue	Solution
N+1 queries	Use DataLoader or eager loading
Slow builds	Check bundle size, lazy load
Auth complexity	Use Auth.js or Clerk
Type errors	Enable strict mode in tsconfig
CORS issues	Configure middleware properly

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Troubleshooting

Problem	Cause	Solution
Scaffolder creates empty files	Template name misspelled or unsupported	Run python project_scaffolder.py --list-templates to verify available templates
Quality analyzer reports 0 files analyzed	Project path points to wrong directory or contains only non-code files	Confirm the path contains .ts, .tsx, .js, .jsx, .py, .go, .java, .rb, .php, or .cs files outside node_modules/, .git/, dist/, and other skip directories
False-positive hardcoded secret warnings	Regex matches long strings assigned to variables named password, secret, token, etc.	Review flagged lines manually; suppress by renaming variables or extracting values to .env files
Cyclomatic complexity score seems inflated	Analyzer counts all decision points (if, else, for, while, &&, ||) across the entire file, not per function	Use the score as a relative indicator; pair with --verbose to identify specific high-complexity files for refactoring
Dependency vulnerability check misses packages	Only a built-in subset of known CVEs is checked (lodash, axios, minimist, jsonwebtoken)	Supplement with npm audit or pip-audit for comprehensive CVE coverage
Docker Compose fails after scaffolding	Port 5432 already in use by a local PostgreSQL instance	Stop the local instance or remap the port in docker-compose.yml
Scaffolded Next.js project fails npm install	Node.js version below 18 or conflicting global packages	Use Node.js 18+ and run npm install in a clean shell without global next conflicts

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Success Criteria

• Quality score >= 80/100 (Grade B or higher) on the code quality analyzer for all production codebases
• Zero P0 (critical) security findings before merging to main branch
• Test file ratio >= 70% of source files (estimated coverage target reported by the analyzer)
• Average cyclomatic complexity < 15 across all analyzed files
• No high-complexity files with nesting depth > 4 without documented justification
• Scaffolded projects build and start successfully on first run after npm install / pip install
• Documentation score >= 75/100 (README, LICENSE, and either CONTRIBUTING or API docs present)

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Scope & Limitations

What this skill covers:

• Project scaffolding for Next.js, FastAPI+React, MERN, and Django+React stacks with Docker, TypeScript, and environment configuration
• Static code quality analysis including complexity metrics, security pattern detection, dependency vulnerability checks, test coverage estimation, and documentation scoring
• Stack selection guidance via the tech stack decision matrix and reference guides
• Fullstack architecture patterns (frontend component design, backend clean architecture, API design, caching, auth)

What this skill does NOT cover:

• Runtime performance profiling, load testing, or APM instrumentation -- see senior-devops for observability tooling
• Infrastructure provisioning, Terraform/Pulumi, or cloud deployment automation -- see aws-solution-architect and senior-devops
• Comprehensive CVE scanning against live vulnerability databases -- use npm audit, pip-audit, or senior-secops for deep security analysis
• Mobile or native desktop application scaffolding -- this skill targets web-based fullstack architectures only

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Integration Points

Skill	Integration	Data Flow
senior-devops	CI/CD pipeline setup for scaffolded projects	Scaffolder output directory feeds into DevOps pipeline configuration and Docker deployment workflows
senior-secops	Deep security audit after initial quality scan	Code quality analyzer P0/P1 security findings hand off to SecOps for remediation tracking and penetration testing
senior-qa	Test strategy for scaffolded projects	Test coverage estimation from the analyzer informs QA test plan gaps; scaffolded test infrastructure provides the harness
code-reviewer	Automated review of generated and existing code	Quality analyzer JSON report provides structured input for code review checklists and PR approval criteria
senior-architect	Architecture validation of stack choices	Tech stack guide recommendations feed into architecture decision records; complexity metrics validate design compliance
aws-solution-architect	Cloud deployment of scaffolded applications	Docker Compose configurations from the scaffolder translate into ECS/EKS task definitions and infrastructure blueprints

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Tool Reference

project_scaffolder.py

Purpose: Generates complete fullstack project structures with boilerplate code, configuration files, Docker setup, and environment templates for four supported stack templates.

Usage:

python scripts/project_scaffolder.py <template> <project_name> [options]
python scripts/project_scaffolder.py --list-templates

Flags:

Flag	Short	Type	Default	Description
template	--	positional	(required)	Template name: nextjs, fastapi-react, mern, or django-react
project_name	--	positional	(required)	Name for the new project directory
--output	-o	string	. (current directory)	Output directory where the project folder is created
--list-templates	-l	flag	false	List all available templates and exit
--json	--	flag	false	Output result in JSON format

Example:

# Scaffold a FastAPI + React project in a custom directory
python scripts/project_scaffolder.py fastapi-react my-api --output ./projects --json

Output Formats:

• Human-readable (default): Prints project name, template used, location on disk, file count, and numbered next steps for getting started.
• JSON (--json): Returns a structured object with keys: success, project_name, template, description, location, files_created, directories_created, next_steps. On failure, returns success: false with an error message and available templates list.

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code_quality_analyzer.py

Purpose: Performs comprehensive static analysis of fullstack codebases, reporting on security vulnerabilities, cyclomatic complexity, dependency health, test coverage estimation, documentation quality, and an overall quality score with prioritized recommendations.

Usage:

python scripts/code_quality_analyzer.py [project_path] [options]

Flags:

Flag	Short	Type	Default	Description
project_path	--	positional	. (current directory)	Path to the project directory to analyze
--verbose	-v	flag	false	Show detailed findings including individual security issue locations
--json	--	flag	false	Output full analysis in JSON format
--output	-o	string	(none)	Write the report to a file (writes JSON regardless of --json flag when used with human-readable mode)

Example:

# Full verbose analysis with JSON report saved to disk
python scripts/code_quality_analyzer.py /path/to/project --verbose --json --output audit.json

Output Formats:

• Human-readable (default): Prints a formatted report with sections for overall score/grade, language breakdown, security issue counts by severity, complexity metrics, dependency status, test coverage estimate, documentation checklist, and up to 10 prioritized recommendations. Use --verbose to expand individual security findings with file paths and line numbers.
• JSON (--json): Returns a structured object with keys: summary, languages, security (categorized by severity), complexity, code_smells, dependencies, tests, documentation, overall_score, grade, recommendations. Each recommendation includes priority (P0/P1/P2), category, issue, and action.