Senior Fullstack

Senior Fullstack

Complete toolkit for senior fullstack with modern tools and best practices.

Quick Start

Main Capabilities

This skill provides three core capabilities through automated scripts:

# Script 1: Fullstack Scaffolder
python scripts/fullstack_scaffolder.py [options]

# Script 2: Project Scaffolder
python scripts/project_scaffolder.py [options]

# Script 3: Code Quality Analyzer
python scripts/code_quality_analyzer.py [options]

Core Capabilities

1. Fullstack Scaffolder

Automated tool for fullstack scaffolder tasks.

Features:

• Automated scaffolding
• Best practices built-in
• Configurable templates
• Quality checks

Usage:

python scripts/fullstack_scaffolder.py <project-path> [options]

2. Project Scaffolder

Comprehensive analysis and optimization tool.

Features:

• Deep analysis
• Performance metrics
• Recommendations
• Automated fixes

Usage:

python scripts/project_scaffolder.py <target-path> [--verbose]

3. Code Quality Analyzer

Advanced tooling for specialized tasks.

Features:

• Expert-level automation
• Custom configurations
• Integration ready
• Production-grade output

Usage:

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

Reference Documentation

Tech Stack Guide

Comprehensive guide available in references/tech_stack_guide.md:

• Detailed patterns and practices
• Code examples
• Best practices
• Anti-patterns to avoid
• Real-world scenarios

Architecture Patterns

Complete workflow documentation in references/architecture_patterns.md:

• Step-by-step processes
• Optimization strategies
• Tool integrations
• Performance tuning
• Troubleshooting guide

Development Workflows

Technical reference guide in references/development_workflows.md:

• Technology stack details
• Configuration examples
• Integration patterns
• Security considerations
• Scalability guidelines

Tech Stack

Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure

Development Workflow

1. Setup and Configuration

# Install dependencies
npm install
# or
pip install -r requirements.txt

# Configure environment
cp .env.example .env

2. Run Quality Checks

# Use the analyzer script
python scripts/project_scaffolder.py .

# Review recommendations
# Apply fixes

3. Implement Best Practices

Follow the patterns and practices documented in:

• references/tech_stack_guide.md
• references/architecture_patterns.md
• references/development_workflows.md

Best Practices Summary

Code Quality

• Follow established patterns
• Write comprehensive tests
• Document decisions
• Review regularly

Performance

• Measure before optimizing
• Use appropriate caching
• Optimize critical paths
• Monitor in production

Security

• Validate all inputs
• Use parameterized queries
• Implement proper authentication
• Keep dependencies updated

Maintainability

• Write clear code
• Use consistent naming
• Add helpful comments
• Keep it simple

Common Commands

# Development
npm run dev
npm run build
npm run test
npm run lint

# Analysis
python scripts/project_scaffolder.py .
python scripts/code_quality_analyzer.py --analyze

# Deployment
docker build -t app:latest .
docker-compose up -d
kubectl apply -f k8s/

Troubleshooting

Common Issues

Check the comprehensive troubleshooting section in references/development_workflows.md.

Getting Help

• Review reference documentation
• Check script output messages
• Consult tech stack documentation
• Review error logs

Resources

• Pattern Reference: references/tech_stack_guide.md
• Workflow Guide: references/architecture_patterns.md
• Technical Guide: references/development_workflows.md
• Tool Scripts: scripts/ directory

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior design decisions (color palettes, typography, spacing scales) to maintain visual consistency across sessions. Cache generated design tokens.

# Check for prior frontend/design context before starting
python3 execution/memory_manager.py auto --query "design system decisions and component patterns for Senior Fullstack"

Storing Results

After completing work, store frontend/design decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Design system: adopted 8px grid, Inter font family, HSL color tokens with dark mode support" \
  --type decision --project <project> \
  --tags senior-fullstack frontend

Multi-Agent Collaboration

Share design decisions with backend agents (API contract changes) and QA agents (visual regression baselines).

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Implemented UI components — new design system with accessibility compliance (WCAG 2.1 AA)" \
  --project <project>

Design Memory Persistence

Store design system tokens and component decisions in Qdrant so any agent on any platform (Claude, Gemini, Cursor) can retrieve and apply consistent styling.