Shell Baseline Integration Skill

This skill helps bootstrap new projects from predefined shell baselines, providing a head start with pre-configured architecture, patterns, and tooling.

When to Use This Skill

• •Starting a new greenfield project
• •Bootstrapping from an established baseline
• •Setting up a new .NET or Python project with best practices
• •Integrating with EmeaAppGbb shell repositories

Available Shell Baselines

1. shell-dotnet

Repository: https://github.com/EmeaAppGbb/shell-dotnet

A production-ready .NET 8 web application shell with:

• •Clean Architecture structure
• •ASP.NET Core Web API
• •Entity Framework Core
• •Docker support
• •GitHub Actions CI/CD

2. agentic-shell-dotnet

Repository: https://github.com/EmeaAppGbb/agentic-shell-dotnet

An AI-agent-ready .NET shell with:

• •All features of shell-dotnet
• •Semantic Kernel integration
• •Azure OpenAI configuration
• •Agent orchestration patterns
• •MCP server support

3. agentic-shell-python

Repository: https://github.com/EmeaAppGbb/agentic-shell-python

A Python-based agentic application shell with:

• •FastAPI backend
• •LangChain integration
• •Azure OpenAI support
• •Async patterns
• •Poetry for dependency management

Integration Workflow

Step 1: Select Shell Baseline

Choose based on project requirements:

Step 2: Clone and Configure

# Clone the selected shell
git clone https://github.com/EmeaAppGbb/[shell-name].git my-project
cd my-project

# Remove git history to start fresh
rm -rf .git
git init

Step 3: Customize Configuration

1. •Update project name in configuration files
2. •Configure environment variables
3. •Set up Azure resources (if needed)
4. •Update README with project-specific information

Step 4: Add Spec2Cloud

# Install spec2cloud agents and prompts
curl -sSL https://github.com/henrybravo/spec2cloud-agentskills/releases/latest/download/install.sh | sh

Step 5: Define Requirements

Use spec2cloud workflow:

1. •Create PRD with /prd command
2. •Break down into FRDs with /frd
3. •Let agents implement the gaps

Shell Structure

Common Structure (All Shells)

project/
├── .github/
│   └── workflows/       # CI/CD pipelines
├── src/
│   └── [application]    # Main application code
├── tests/               # Test projects
├── docs/                # Documentation
├── scripts/             # Utility scripts
├── .devcontainer/       # Dev container config
├── docker-compose.yml   # Local development
├── README.md
└── LICENSE

.NET Shell Specifics

src/
├── Api/                 # Controllers, DTOs
├── Application/         # Business logic, CQRS
├── Domain/             # Entities, value objects
└── Infrastructure/     # Data access, external services

Python Shell Specifics

src/
├── api/                 # FastAPI routes
├── services/           # Business logic
├── models/             # Pydantic models
└── agents/             # AI agent implementations

Configuration Templates

See templates/ for configuration examples:

• •dotnet-shell-config.md - .NET configuration guide
• •python-shell-config.md - Python configuration guide

Best Practices

1. •Start with shell - Don't build from scratch
2. •Keep shell patterns - Follow established architecture
3. •Update dependencies - Check for newer versions
4. •Configure early - Set up environment before coding
5. •Use spec2cloud - Let agents fill in the gaps

Integration with Spec2Cloud Workflow

1. •Clone shell baseline
2. •Install spec2cloud
3. •Run /prd to create requirements
4. •Run /frd to break down features
5. •Run /plan to create implementation tasks
6. •Run /implement to build features
7. •Run /deploy to deploy to Azure

The shell provides the foundation; spec2cloud agents fill in the business logic.