---
name: setup-datamodel
description: >-
  Creates Dataverse tables, columns, and relationships for a Power Pages site based on a data
  model proposal. Use when the user wants to set up the data model, create database tables,
  or build the Dataverse schema for their site.
user-invocable: true
allowed-tools: Read, Write, Bash, Grep, Glob, AskUserQuestion, Task, TaskCreate, TaskUpdate, TaskList, mcp__plugin_power-pages_microsoft-learn__microsoft_docs_search, mcp__plugin_power-pages_microsoft-learn__microsoft_code_sample_search, mcp__plugin_power-pages_microsoft-learn__microsoft_docs_fetch
model: opus
---

> **Plugin check**: Run `node "${CLAUDE_PLUGIN_ROOT}/scripts/check-version.js"` — if it outputs a message, show it to the user before proceeding.

# Set Up Dataverse Data Model

Guide the user through creating Dataverse tables, columns, and relationships for their Power Pages site. Follow a systematic approach: verify prerequisites, obtain a data model (via AI analysis or user-provided diagram), review and approve, then create all schema objects via OData API.

## Core Principles

- **Never create without approval**: Always present the full data model proposal and get explicit user confirmation before making any Dataverse changes.
- **Use TaskCreate/TaskUpdate**: Track all progress throughout all phases — create the todo list upfront with all phases before starting any work.
- **Resilient execution**: Refresh tokens proactively, check for existing tables before creating, and report failures without automated rollback.

**Initial request:** $ARGUMENTS

---

## Phase 1: Verify Prerequisites

**Goal**: Confirm PAC CLI authentication, acquire an Azure CLI token, and verify API access

**Actions**:

1. Create todo list with all 8 phases (see [Progress Tracking](#progress-tracking) table)
2. Follow the prerequisite steps in `${CLAUDE_PLUGIN_ROOT}/references/dataverse-prerequisites.md` to verify PAC CLI auth, acquire an Azure CLI token, and confirm API access. Store the environment URL as `$envUrl`.

**Output**: Verified PAC CLI auth, valid Azure CLI token, confirmed API access, `$envUrl` stored

---

## Phase 2: Choose Data Model Source

**Goal**: Determine whether the user will upload an existing ER diagram or let AI analyze the site

**Actions**:

1. Ask the user how they want to define the data model using the `AskUserQuestion` tool:

   **Question**: "How would you like to define the data model for your site?"

   | Option | Description |
   |--------|-------------|
   | **Upload an existing ER diagram** | Provide an image (PNG/JPG) or Mermaid diagram of your existing data model |
   | **Let the Data Model Architect figure it out** | The Data Model Architect will analyze your site's source code and propose a data model automatically |

2. Route to the appropriate path:

### Path A: Upload Existing ER Diagram

If the user chooses to upload an existing diagram:

1. Ask the user to provide their ER diagram. Supported formats:
   - **Image file** (PNG, JPG) — Use the `Read` tool to view the image and extract tables, columns, relationships, and cardinalities from it
   - **Mermaid syntax** — The user can paste Mermaid ER diagram text directly in chat
   - **Text description** — A structured list of tables, columns, and relationships

2. Parse the diagram into the same structured format used by the data-model-architect agent:
   - Publisher prefix (ask the user, or retrieve from the environment via `pac env who`)
   - Table definitions: `logicalName`, `displayName`, `status` (new/modified/reused), `columns`, `relationships`
   - Column definitions: `logicalName`, `displayName`, `type`, `required`
   - Relationship definitions: type (1:N or M:N), referenced/referencing tables

3. Query existing Dataverse tables (same as Phase 3 would) to mark each table as `new`, `modified`, or `reused`.

4. Generate a Mermaid ER diagram from the parsed data (if the user provided an image or text) for visual confirmation.

5. Proceed directly to **Phase 4: Review Proposal** with the parsed data model.

### Path B: Let the Data Model Architect Figure It Out

If the user chooses to let the Data Model Architect figure it out, proceed to **Phase 3: Invoke Data Model Architect** (the existing automated flow).

**Output**: Data model source chosen and, for Path A, parsed data model ready for review

---

## Phase 3: Invoke Data Model Architect

**Goal**: Spawn the data-model-architect agent to autonomously analyze the site and propose a data model

**Actions**:

1. Use the `Task` tool to spawn the `data-model-architect` agent. This agent autonomously:
   - Analyzes the site's source code to infer data requirements
   - Queries existing Dataverse tables via OData GET requests
   - Identifies reuse opportunities (reuse, extend, or create new)
   - Proposes a complete data model with an ER diagram

2. Spawn the agent:

   ```
   Task tool:
     subagent_type: general-purpose
     prompt: |
       You are the data-model-architect agent. Follow the instructions in
       the agent definition file at:
       ${CLAUDE_PLUGIN_ROOT}/agents/data-model-architect.md

       Analyze the current project and Dataverse environment, then propose
       a complete data model. Return:
       1. Publisher prefix
       2. Table definitions (logicalName, displayName, status, columns, relationships)
       3. Mermaid ER diagram
   ```

3. Wait for the agent to return its structured proposal before proceeding.

**Output**: Structured data model proposal from the agent (publisher prefix, table definitions, ER diagram)

---

## Phase 4: Review Proposal

**Goal**: Present the data model proposal to the user and get explicit approval before creating anything

**Actions**:

### 4.1 Present Proposal

Present the data model proposal directly to the user as a formatted message, including:

- Publisher prefix
- All proposed tables with columns (logical names + display names)
- Relationship descriptions
- Mermaid ER diagram
- Which tables are new vs. modified vs. reused

### 4.2 Get User Approval

Use `AskUserQuestion` to get approval:

| Question | Header | Options |
|----------|--------|---------|
| Does this data model look correct? | Data Model Proposal | Approve and create tables (Recommended), Request changes, Cancel |

- **If "Approve and create tables (Recommended)"**: Proceed to Phase 5
- **If "Request changes"**: Ask what they want changed, modify the proposal, and re-present for approval
- **If "Cancel"**: Stop the skill

Only proceed to creation after explicit user approval.

**Output**: User-approved data model proposal

---

## Phase 5: Pre-Creation Checks

**Goal**: Refresh the token, verify what already exists in Dataverse, and build the creation plan to avoid duplicates

**Actions**:

### 5.1 Refresh Token

Re-acquire the auth token (tokens expire after ~60 minutes):

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/verify-dataverse-access.js" <envUrl>
```

### 5.2 Query Existing Tables

For each table in the approved proposal marked as `new`, check whether it already exists:

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET "api/data/v9.2/EntityDefinitions(LogicalName='<table_logical_name>')"
```

- **If 404**: Table does not exist, proceed to create it
- **If 200**: Table already exists — skip creation, warn the user

For tables marked as `modified`, verify the table exists (it should) and check which columns are missing.

### 5.3 Build Creation Plan

From the pre-creation checks, build a list of:

- Tables to create (new tables that don't exist yet)
- Columns to add (new columns on existing/modified tables)
- Relationships to create
- Tables/columns to skip (already exist)

Inform the user of any skipped items.

**Output**: Finalized creation plan with tables, columns, and relationships to create or skip

---

## Phase 6: Create Tables & Columns

**Goal**: Create each approved table and its columns using the Dataverse OData Web API

**Actions**:

Refer to `references/odata-api-patterns.md` for full JSON body templates.

### 6.1 Create Tables

For each new table, POST to the EntityDefinitions endpoint:

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/EntityDefinitions" --body '<JSON body from references/odata-api-patterns.md>'
```

Use the deep-insert pattern to create the table and its columns in a single POST request. See `references/odata-api-patterns.md` for the complete JSON structure.

### 6.2 Add Columns to Existing Tables

For tables marked as `modified`, add new columns one at a time:

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/EntityDefinitions(LogicalName='<table>')/Attributes" --body '<column JSON from references/odata-api-patterns.md>'
```

### 6.3 Track Progress

Track each creation attempt and its result (success/failure/skipped). Do NOT attempt automated rollback on failure — report failures and continue with remaining items.

### 6.4 Refresh Token if Needed

If creating many tables, the `dataverse-request.js` script handles 401 token refresh automatically. No manual refresh is needed between batches.

**Output**: All approved tables and columns created (or failures reported)

---

## Phase 7: Create Relationships

**Goal**: Create all relationships between the newly created and existing tables

**Actions**:

### 7.1 One-to-Many Relationships

Create lookup columns that establish 1:N relationships:

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/RelationshipDefinitions" --body '<relationship JSON from references/odata-api-patterns.md>'
```

### 7.2 Many-to-Many Relationships

Create M:N relationships (intersect tables are created automatically):

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/RelationshipDefinitions" --body '<M:N relationship JSON from references/odata-api-patterns.md>'
```

### 7.3 Track Relationship Creation

Track each relationship creation attempt. Report failures without rolling back.

**Output**: All approved relationships created (or failures reported)

---

## Phase 8: Publish & Verify

**Goal**: Publish all customizations, verify tables exist, write the manifest, and present a summary

**Actions**:

### 8.1 Publish Customizations

Publish all customizations so the new tables and columns become available:

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/PublishXml" --body '{"ParameterXml":"<importexportxml><entities><entity>cr123_project</entity><entity>cr123_task</entity></entities></importexportxml>"}'
```

See `references/odata-api-patterns.md` for the full PublishXml pattern.

### 8.2 Verify Tables Exist

For each created table, run a verification query:

```
node "${CLAUDE_PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET "api/data/v9.2/EntityDefinitions(LogicalName='<table>')?$select=LogicalName,DisplayName"
```

### 8.3 Write Manifest

After successful verification, write `.datamodel-manifest.json` to the project root. This file records which tables and columns were verified to exist, and is used by the validation hook.

```json
{
  "environmentUrl": "https://org12345.crm.dynamics.com",
  "tables": [
    {
      "logicalName": "cr123_project",
      "displayName": "Project",
      "status": "new",
      "columns": [
        { "logicalName": "cr123_name", "type": "String" },
        { "logicalName": "cr123_description", "type": "Memo" }
      ]
    }
  ]
}
```

Use the `Write` tool to create this file at `<PROJECT_ROOT>/.datamodel-manifest.json`. Only include tables and columns that were confirmed to exist in Step 8.2. See `${CLAUDE_PLUGIN_ROOT}/references/datamodel-manifest-schema.md` for the full schema specification.

### 8.4 Record Skill Usage

> Reference: `${CLAUDE_PLUGIN_ROOT}/references/skill-tracking-reference.md`

Follow the skill tracking instructions in the reference to record this skill's usage. Use `--skillName "SetupDatamodel"`.

### 8.5 Present Summary

Present a summary to the user:

| Table | Status | Columns | Relationships |
|-------|--------|---------|---------------|
| `cr123_project` (Project) | Created | 5 columns | 2 relationships |
| `contact` (Contact) | Reused | 1 column added | — |
| `cr123_task` (Task) | Created | 4 columns | 1 relationship |

Include:

- Total tables created/modified/reused/failed
- Total columns created/skipped/failed
- Total relationships created/failed
- Any errors encountered with details
- Location of the manifest file (`.datamodel-manifest.json`)

### 8.6 Suggest Next Steps

After the summary, suggest:

- Review created tables in the Power Pages maker portal
- Populate tables with sample data for testing: `/add-sample-data`
- Integrate tables with your site's frontend via Web API: `/integrate-webapi`
- If the site is not yet built: `/create-site`
- If the site is ready to deploy: `/deploy-site`

**Output**: Published customizations, verified tables, manifest written, summary presented

---

## Important Notes

### Throughout All Phases

- **Use TaskCreate/TaskUpdate** to track progress at every phase
- **Ask for user confirmation** at key decision points (see list below)
- **Token refresh is automatic** — the `dataverse-request.js` script handles 401 token refresh and 429/5xx retry internally
- **Report failures without rollback** — track each creation attempt and continue with remaining items on failure

### Key Decision Points (Wait for User)

1. After Phase 2: Data model source chosen (upload vs. AI)
2. After Phase 4: Approve data model proposal before any creation
3. After Phase 5: Acknowledge any skipped items before proceeding
4. After Phase 8: Review summary and choose next steps

### Progress Tracking

Before starting Phase 1, create a task list with all phases using `TaskCreate`:

| Task subject | activeForm | Description |
|-------------|------------|-------------|
| Verify prerequisites | Verifying prerequisites | Confirm PAC CLI auth, acquire Azure CLI token, verify API access |
| Choose data model source | Choosing data model source | Ask user to upload ER diagram or let AI analyze the site |
| Invoke data model architect | Invoking data model architect | Spawn agent to analyze site and propose data model |
| Review and approve proposal | Reviewing proposal | Present data model proposal to user, get explicit approval |
| Pre-creation checks | Running pre-creation checks | Refresh token, query existing tables, build creation plan |
| Create tables and columns | Creating tables and columns | POST to OData API to create tables and columns |
| Create relationships | Creating relationships | POST to OData API to create 1:N and M:N relationships |
| Publish and verify | Publishing and verifying | Publish customizations, verify tables, write manifest, present summary |

Mark each task `in_progress` when starting it and `completed` when done via `TaskUpdate`. This gives the user visibility into progress and keeps the workflow deterministic.

---

**Begin with Phase 1: Verify Prerequisites**