senior-fullstack – Coding skill for AI agents

Senior Fullstack

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

---

Table of Contents

• [Trigger Phrases](#trigger-phrases)
• [Tools](#tools)
• [Workflows](#workflows)
• [Reference Guides](#reference-guides)

---

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"

---

Tools

Decision Engine

Deterministic profile picker. Given four assumptions (team-size, cadence, user-facing, budget) plus optional traffic/sensitivity inputs, ranks the four built-in profiles and returns the matched profile with SLO floor and named approver chain. Refuses to recommend a profile without the four required inputs.

Usage:

# See all options
python scripts/fullstack_decision_engine.py --help

# Run against a sample input
python scripts/fullstack_decision_engine.py --sample

# Pick a profile from real inputs
python scripts/fullstack_decision_engine.py \
    --team-size-12mo 8 --cadence daily --user-facing true --budget 5000 \
    --traffic-p99-rps 50 --data-sensitivity pii-only

# JSON output for downstream tools
python scripts/fullstack_decision_engine.py --sample --output json

Returns: matched profile name, score, matched/violated constraints, stack recommendation, anti-recommendations, SLO floor, named-approver chain, and canon references.

The engine encodes the same matrix the conversational grill walks through — use it directly when inputs are already known, or via the cs-fullstack-engineer agent for the question-by-question grill.

---

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

---

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

---

Workflows

Workflow 1: Start New Project

1. Choose appropriate stack based on requirements (see Stack Decision Matrix)

2. Scaffold project structure

3. Verify scaffold: confirm package.json (or requirements.txt) exists

4. Run initial quality check — address any P0 issues before proceeding

5. Set up development environment

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

# 2. Verify scaffold succeeded
ls my-saas-app/package.json

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

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

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

# 6. Start development
npm run dev

Workflow 2: Audit Existing Codebase

1. Run code quality analysis

2. Review security findings — fix all P0 (critical) issues immediately

3. Re-run analyzer to confirm P0 issues are resolved

4. Create tickets for P1/P2 issues

# 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. After fixing P0 issues, re-run to verify
python scripts/code_quality_analyzer.py /path/to/project --verbose

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.

---

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)

---

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 |

---

Assumptions and Verifiable Success Criteria (Karpathy discipline)

Before this skill scaffolds, recommends, or modifies any code, the following four

… (full content is in the download package)