Engineering Team Skills - Claude Code Guidance

Engineering Team Skills - Claude Code Guidance

This guide covers the 14 production-ready engineering skills and their Python automation tools.

Engineering Skills Overview

Core Engineering (9 skills):

• senior-architect, senior-frontend, senior-backend, senior-fullstack
• senior-qa, senior-devops, senior-secops
• code-reviewer, senior-security

AI/ML/Data (5 skills):

• senior-data-scientist, senior-data-engineer, senior-ml-engineer
• senior-prompt-engineer, senior-computer-vision

Total Tools: 30+ Python automation tools

Core Engineering Tools

1. Project Scaffolder (senior-fullstack/scripts/project_scaffolder.py)

Purpose: Production-ready project scaffolding for modern stacks

Supported Stacks:

• Next.js + GraphQL + PostgreSQL
• React + REST + MongoDB
• Vue + GraphQL + MySQL
• Express + TypeScript + PostgreSQL

Features:

• Docker Compose configuration
• CI/CD pipeline (GitHub Actions)
• Testing infrastructure (Jest, Cypress)
• TypeScript + ESLint + Prettier
• Database migrations

Usage:

# Create new project
python senior-fullstack/scripts/project_scaffolder.py my-project --type nextjs-graphql

# Start services
cd my-project && docker-compose up -d

2. Code Quality Analyzer (senior-fullstack/scripts/code_quality_analyzer.py)

Purpose: Comprehensive code quality analysis and metrics

Features:

• Security vulnerability scanning
• Performance issue detection
• Test coverage assessment
• Documentation quality
• Dependency analysis
• Actionable recommendations

Usage:

# Analyze project
python senior-fullstack/scripts/code_quality_analyzer.py /path/to/project

# JSON output
python senior-fullstack/scripts/code_quality_analyzer.py /path/to/project --json

Output:

Code Quality Report:
- Overall Score: 85/100
- Security: 90/100 (2 medium issues)
- Performance: 80/100 (3 optimization opportunities)
- Test Coverage: 75% (target: 80%)
- Documentation: 88/100

Recommendations:
1. Update lodash to 4.17.21 (CVE-2020-8203)
2. Optimize database queries in UserService
3. Add integration tests for payment flow

3. Fullstack Scaffolder (senior-fullstack/scripts/fullstack_scaffolder.py)

Purpose: Rapid fullstack application generation

Usage:

python senior-fullstack/scripts/fullstack_scaffolder.py my-app --stack nextjs-graphql

AI/ML/Data Tools

Data Science Tools

Experiment Designer (senior-data-scientist/scripts/experiment_designer.py)

• A/B test design
• Statistical power analysis
• Sample size calculation

Feature Engineering Pipeline (senior-data-scientist/scripts/feature_engineering_pipeline.py)

• Automated feature generation
• Correlation analysis
• Feature selection

Statistical Analyzer (senior-data-scientist/scripts/statistical_analyzer.py)

• Hypothesis testing
• Causal inference
• Regression analysis

Data Engineering Tools

Pipeline Orchestrator (senior-data-engineer/scripts/pipeline_orchestrator.py)

• Airflow DAG generation
• Spark job templates
• Data quality checks

Data Quality Validator (senior-data-engineer/scripts/data_quality_validator.py)

• Schema validation
• Null check enforcement
• Anomaly detection

ETL Generator (senior-data-engineer/scripts/etl_generator.py)

• Extract-Transform-Load workflows
• CDC (Change Data Capture) patterns
• Incremental loading

ML Engineering Tools

Model Deployment Pipeline (senior-ml-engineer/scripts/model_deployment_pipeline.py)

• Containerized model serving
• REST API generation
• Load balancing config

MLOps Setup Tool (senior-ml-engineer/scripts/mlops_setup_tool.py)

• MLflow configuration
• Model versioning
• Drift monitoring

LLM Integration Builder (senior-ml-engineer/scripts/llm_integration_builder.py)

• OpenAI API integration
• Prompt templates
• Response parsing

Prompt Engineering Tools

Prompt Optimizer (senior-prompt-engineer/scripts/prompt_optimizer.py)

• Prompt A/B testing
• Token optimization
• Few-shot example generation

RAG System Builder (senior-prompt-engineer/scripts/rag_system_builder.py)

• Vector database setup
• Embedding generation
• Retrieval strategies

Agent Orchestrator (senior-prompt-engineer/scripts/agent_orchestrator.py)

• Multi-agent workflows
• Tool calling patterns
• State management

Computer Vision Tools

Vision Model Trainer (senior-computer-vision/scripts/vision_model_trainer.py)

• Object detection (YOLO, Faster R-CNN)
• Semantic segmentation
• Transfer learning

Inference Optimizer (senior-computer-vision/scripts/inference_optimizer.py)

• Model quantization
• TensorRT optimization
• ONNX export

Video Processor (senior-computer-vision/scripts/video_processor.py)

• Frame extraction
• Object tracking
• Scene detection

Tech Stack Patterns

Frontend (React/Next.js)

• TypeScript strict mode
• Component-driven architecture
• Atomic design patterns
• State management (Zustand/Jotai)
• Testing (Jest + React Testing Library)

Backend (Node.js/Express)

• Clean architecture
• Dependency injection
• Repository pattern
• Domain-driven design
• Testing (Jest + Supertest)

Fullstack Integration

• GraphQL for API layer
• REST for external services
• WebSocket for real-time
• Redis for caching
• PostgreSQL for persistence

Development Workflows

Workflow 1: New Project Setup

# 1. Scaffold project
python senior-fullstack/scripts/project_scaffolder.py my-app --type nextjs-graphql

# 2. Start services
cd my-app && docker-compose up -d

# 3. Run migrations
npm run migrate

# 4. Start development
npm run dev

Workflow 2: Code Quality Check

# 1. Analyze codebase
python senior-fullstack/scripts/code_quality_analyzer.py ./

# 2. Fix security issues
npm audit fix

# 3. Run tests
npm test

# 4. Build production
npm run build

Workflow 3: ML Model Deployment

# 1. Setup MLOps infrastructure
python senior-ml-engineer/scripts/mlops_setup_tool.py

# 2. Deploy model
python senior-ml-engineer/scripts/model_deployment_pipeline.py model.pkl

# 3. Monitor performance
# Check MLflow dashboard

Quality Standards

All engineering tools must:

• Support modern tech stacks (Next.js, React, Vue, Express)
• Generate production-ready code
• Include testing infrastructure
• Provide Docker configurations
• Support CI/CD integration

Integration Patterns

GitHub Actions CI/CD

All scaffolders generate GitHub Actions workflows:

.github/workflows/
├── test.yml          # Run tests on PR
├── build.yml         # Build and lint
└── deploy.yml        # Deploy to production

Docker Compose

Multi-service development environment:

services:
  - app (Next.js)
  - api (GraphQL)
  - db (PostgreSQL)
  - redis (Cache)

Additional Resources

• Quick Start: START_HERE.md
• Team Structure: TEAM_STRUCTURE_GUIDE.md
• Engineering Roadmap: engineering_skills_roadmap.md (if exists)
• Main Documentation: ../CLAUDE.md

Last Updated: November 5, 2025 Skills Deployed: 14/14 engineering skills production-ready Total Tools: 30+ Python automation tools across core + AI/ML/Data