/senior-fullstack

name: "senior-fullstack"
description: Fullstack development toolkit with project scaffolding for Next.js, FastAPI, MERN, and Django stacks, code quality analysis with security and complexity scoring, and stack selection guidance. Use when the user asks to "scaffold a new project", "create a Next.js app", "set up FastAPI with React", "analyze code quality", "audit my codebase", "what stack should I use", "generate project boilerplate", or mentions fullstack development, project setup, or tech stack comparison.

Senior Fullstack

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

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Table of Contents

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

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.

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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 (see Stack Decision Matrix) 2. Scaffold project structure 3. Verify scaffold: confirm `package.json` (or `requirements.txt`) exists 4. Run initial quality check — address a