# Quick Start

This guide describes the target production flow for a new developer using AIWorkspace.

The current repository still contains an active project-specific implementation. During refactor, these commands may be implemented incrementally.

## 1. Install AIWorkspace

Install the app, CLI, and local services from the reusable core repo:

```bash
git clone <team-ai-workspace-repo> ~/Developer/ai-workspace
cd ~/Developer/ai-workspace
```

The target CLI entry point is:

```bash
aiw
```

During development, commands may still be run through Python scripts.

## 2. Register Or Create A Project Context Pack

Register an existing context pack:

```bash
aiw project register ~/Developer/client-mobile-ai-context
```

Or create a new one:

```bash
aiw project create it-support --display-name "IT Support" --path ~/Developer/it-support-ai-context
```

The context pack contains project memory and project-specific connector configuration.

## 3. Connect Evidence Sources

Add only the connectors this project needs:

```bash
aiw connector add mattermost --project client-mobile
aiw connector add photos --project client-mobile
```

For another project, the connector set may be completely different:

```bash
aiw connector add tickets --project it-support
aiw connector add calendar --project it-support
```

## 4. Detect Local Repositories

AIWorkspace should help detect local repos and associate them with projects:

```bash
aiw repo scan ~/Developer --project it-support
```

For projects whose source code lives on another machine, the context pack should say so explicitly in `aiw.project.json`.

## 5. Start Context Services

Start services for the active project:

```bash
aiw services start --project client-mobile
aiw services status --project client-mobile
```

The service manager should start only capabilities configured for that project.

## 6. Configure AI Clients

Generate AI client integration files on demand:

```bash
aiw agent configure opencode --project client-mobile
aiw agent configure copilot --project client-mobile
aiw mcp config --client vscode
```

The preferred integration path is MCP, so AI clients can request current context without copying memory into product repositories.

## 7. Use AIWorkspace From Any AI Tool

The AI client should call MCP with a project id:

```json
{
  "project": "client-mobile"
}
```

Example tool calls:

```text
project_current_context(project="client-mobile")
project_search_memory(project="it-support", query="printer onboarding")
communication_latest(project="client-mobile")
```

## 8. Keep Canonical Memory Clean

Project memory lives in the context pack:

```text
<context-pack>/knowledge/
```

Raw evidence lives separately:

```text
<context-pack>/inbox/
```

Do not treat raw captures, indexes, logs, or AI chat history as canonical memory.