Quick Start Guide

Relevant source files

The following files were used as context for generating this wiki page:

• QUICKSTART.md
• DOCKER_MCP_DEPLOYMENT.md
• ENV_SETUP_GUIDE.md
• UNICORN_QUICK_START_CHECKLIST.md
• CONTRIBUTING.md
• DOCKER_README.md
• README.md

This guide provides the fastest path to deploying and running the DevSkyy Enterprise Platform, a comprehensive system featuring a multi-agent orchestration system with 54 purpose-built AI agents for the luxury fashion e-commerce sector. The primary method for a quick start is using Docker and Docker Compose, which encapsulates the entire multi-service infrastructure.

This guide focuses on the MCP (Model Context Protocol) infrastructure, which includes the main DevSkyy API, the MCP Gateway, a PostgreSQL database, and a Redis cache. For more detailed deployment, security, and development information, refer to the Deployment Guide and Contribution Guide.

Sources: DOCKER_MCP_DEPLOYMENT.md, QUICKSTART.md, ANALYSIS_INDEX.md:30-38

1. Prerequisites

Before you begin, ensure you have the following tools installed and running on your system:

• Docker: Version 20.10+
• Docker Compose: Version 2.0+

You can verify your installations by running:

docker --version
docker-compose --version

Sources: DOCKER_MCP_DEPLOYMENT.md:27-33

2. Quick Start Process Overview

The following diagram illustrates the high-level steps to get the DevSkyy platform running.

This diagram shows the streamlined process for setting up the DevSkyy environment.
Sources: DOCKER_MCP_DEPLOYMENT.md:35-68, ENV_SETUP_GUIDE.md:5-10

3. Configuration

The entire system is configured through a single .env file. This file manages API keys, database connections, and other secrets.

Sources: ENV_SETUP_GUIDE.md, DOCKER_MCP_DEPLOYMENT.md:38-42

3.1. Create the .env File

First, copy the provided template to create your local configuration file.

cp .env.example .env

Sources: DOCKER_MCP_DEPLOYMENT.md:38-39

3.2. Set Environment Variables

Next, edit the .env file and populate it with the necessary keys and secrets.

Minimum Required Variables:

Variable	Description	Example/How to Generate	Source File
SECRET_KEY	Encryption key for sessions and tokens.	python -c "import secrets; print(secrets.token_urlsafe(32))"	ENV_SETUP_GUIDE.md:20-23
ANTHROPIC_API_KEY	Powers Claude AI models for core features.	sk-ant-... (from console.anthropic.com)	ENV_SETUP_GUIDE.md:25-35
DATABASE_URL	Connection string for the database.	postgresql://devskyy:devskyy@postgres:5432/devskyy	DOCKER_MCP_DEPLOYMENT.md:52-53
REDIS_URL	Connection string for the Redis cache.	redis://redis:6379/0	DOCKER_MCP_DEPLOYMENT.md:54-55

Optional but Recommended Variables:

Variable	Description	Example	Source File
OPENAI_API_KEY	Adds support for OpenAI's GPT-4 models.	sk-...	ENV_SETUP_GUIDE.md:83-91
HUGGING_FACE_TOKEN	Required for the HuggingFace MCP server.	hf_...	DOCKER_MCP_DEPLOYMENT.md:46-47
SENTRY_DSN	For tracking errors in production.	https://...	ENV_SETUP_GUIDE.md:183-191
WORDPRESS_URL	URL for deploying generated themes.	https://your-site.com	ENV_SETUP_GUIDE.md:99-100
WORDPRESS_APP_PASSWORD	Application password for WordPress.	xxxx ...	ENV_SETUP_GUIDE.md:101-102

Sources: ENV_SETUP_GUIDE.md, DOCKER_MCP_DEPLOYMENT.md:43-55

4. Deployment with Docker Compose

The docker-compose.mcp.yml file defines and orchestrates all the necessary services for the MCP infrastructure.

4.1. Service Architecture

The Docker Compose setup launches a multi-container environment with the following services:

This diagram shows the services defined in docker-compose.mcp.yml and their interactions.
Sources: DOCKER_MCP_DEPLOYMENT.md:14-23, DOCKER_README.md:102-126

4.2. Start Services

Use the following command to build and start all services in detached mode.

# Build and start all services
docker-compose -f docker-compose.mcp.yml up -d

# View logs to monitor startup
docker-compose -f docker-compose.mcp.yml logs -f

# Check the status of running containers
docker-compose -f docker-compose.mcp.yml ps

Sources: DOCKER_MCP_DEPLOYMENT.md:58-65

5. Verification

After starting the services, verify that the deployment was successful by checking the health endpoints.

5.1. Check Service Health

Run these curl commands from your terminal:

# Check API health
curl http://localhost:8000/health

# Check MCP Gateway health
curl http://localhost:3000/health

# Check available MCP servers
curl http://localhost:3000/servers

Sources: DOCKER_MCP_DEPLOYMENT.md:67-73

5.2. Expected Output

The command to check available MCP servers should return a JSON object similar to this, confirming the gateway is active and connected to its backend servers.

{
  "servers": {
    "devskyy": {
      "type": "stdio",
      "status": "active"
    },
    "huggingface": {
      "type": "http",
      "status": "active"
    }
  }
}

Sources: DOCKER_MCP_DEPLOYMENT.md:75-87

6. Using the System

Once the system is running, you can interact with it via the MCP Orchestrator. The following examples demonstrate how to perform common tasks.

Example 1: Generate an Instagram Post

This Python snippet shows how to use the VISUAL_FOUNDRY agent team to generate an image for an Instagram post.

from agents.mcp.orchestrator import MCPOrchestrator, AgentRole

orchestrator = MCPOrchestrator()

# Create task for luxury fashion Instagram post
task = await orchestrator.create_task(
    name="Generate Instagram post for new dress collection",
    agent_role=AgentRole.VISUAL_FOUNDRY,
    tool_name="stable_diffusion",
    input_data={
        "prompt": "Elegant woman in rose gold evening dress, Skyy blue background, luxury fashion editorial style, soft lighting",
        "width": 1080,
        "height": 1350
    }
)

result = await orchestrator.execute_task(task)
print(f"Image generated: {result['success']}")

Sources: QUICKSTART.md:70-89

Example 2: Launch a New Collection

The orchestrator can execute complex, multi-agent workflows, such as a complete seasonal collection launch.

# Execute seasonal collection launch workflow
result = await orchestrator.execute_workflow(
    workflow_name="new_collection_launch",
    context={
        "collection_name": "Spring 2025 Collection",
        "theme": "Coastal Elegance",
        "key_colors": ["Skyy Blue", "Rose Gold", "Ivory"],
        "product_count": 24,
        "launch_date": "2025-03-01"
    }
)

This single call can generate lookbook images, a launch video, and an omnichannel marketing campaign.
Sources: QUICKSTART.md:91-107

7. Troubleshooting

If you encounter issues during setup, consult the following:

Issue	Solution	Source
Services won't start	Check the service logs with docker-compose -f docker-compose.mcp.yml logs. Ensure your .env file is correctly populated and has no syntax errors.	DOCKER_MCP_DEPLOYMENT.md:214-222
Database connection failed	Verify the DATABASE_URL in your .env file matches the credentials in docker-compose.mcp.yml. Test the connection from within the API container.	DOCKER_MCP_DEPLOYMENT.md:227-228
API key errors	Ensure API keys in .env are correct and do not have extra spaces or characters. Verify the key has the necessary permissions.	ENV_SETUP_GUIDE.md:241-245
Permission denied	Ensure the .env file has the correct read permissions. You can run chmod 600 .env to be safe.	ENV_SETUP_GUIDE.md:251-254

Conclusion

By following this guide, you have successfully deployed the DevSkyy Enterprise platform using Docker. You now have a running instance of the 54-agent AI orchestration system, complete with its API, database, and caching layer. The system is ready to automate fashion e-commerce tasks, from media generation to marketing campaigns.

For next steps, you can explore the detailed documentation on individual agents, customize brand configurations, or begin integrating the API into your production workflows.
Sources: QUICKSTART.md:231-248, DOCKER_MCP_DEPLOYMENT.md:315-322