data360-schema-get

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data360-schema-get Skill

Overview

This skill retrieves Data Lake Object (DLO) and Data Model Object (DMO) schema information from Salesforce Data Cloud using the SSOT REST API. It can list all DLOs or DMOs in an org, or retrieve detailed schema for a specific DLO or DMO.

When to Use

User wants to see all DLOs or DMOs in a Data Cloud org
User needs field schema for a specific DLO or DMO
User is exploring Data Cloud data structures
User needs to understand DLO or DMO field types and metadata

Prerequisites

SF CLI installed and authenticated to target org
Org has Data Cloud enabled
User has appropriate Data Cloud permissions

Skill Execution

Parameters

org_alias (required): The SF CLI org alias (e.g., 'afvibe', 'myorg')
dlo_name (optional): Specific DLO developer name (e.g., 'Employee__dll')
dmo_name (optional): Specific DMO developer name (e.g., 'Individual__dlm')

Step 1: Discover Connected Org

First, run sf org list to find out which org is connected and extract the alias to use for all subsequent calls:

bash

sf org list

Example output:

text

┌────┬───────┬──────────────────────────┬────────────────────┬───────────┐
│    │ Alias │ Username                 │ Org Id             │ Status    │
├────┼───────┼──────────────────────────┼────────────────────┼───────────┤
│ 🍁 │ myorg │ chandresh@afvidedemo.org │ 00DKZ00000b80NT2AY │ Connected │
└────┴───────┴──────────────────────────┴────────────────────┴───────────┘

Extract the Alias value (e.g., myorg) from the output and use it as the <org_alias> for all subsequent calls. Use --all to see expired and deleted scratch orgs as well.

Step 2: Validate SF CLI Authentication

Before making API calls, verify the org is connected:

bash

sf org display --target-org <org_alias> --json

If not connected, inform user to run:

bash

sf org login web --alias <org_alias>

Step 3a: Execute DLO Schema Script

The Python scripts are bundled with this skill in the scripts/ subdirectory.

To list all DLOs:

bash

python3 ./scripts/get_dlo_schema.py <org_alias>

To get specific DLO schema:

bash

python3 ./scripts/get_dlo_schema.py <org_alias> <dlo_name>

Step 3b: Execute DMO Schema Script

To list all DMOs:

bash

python3 ./scripts/get_dmo_schema.py <org_alias>

To get specific DMO schema:

bash

python3 ./scripts/get_dmo_schema.py <org_alias> <dmo_name>

Step 4: Present Results

Parse and present the results in a user-friendly format:

For DLO List:

Show DLO name, label, category, and ID
Indicate total count
Highlight DLOs with data (totalRecords > 0)

For DLO Schema:

Show basic info (name, label, category, status)
List all fields with:
Field name
Data type
Primary key indicator
Nullable status
Highlight custom fields (exclude system fields like DataSource_c, cdpsys_*)
Show record count if available

For DMO List:

Show DMO name, label, category, and ID
Indicate total count

For DMO Schema:

Show basic info (name, label, category, description)
List all fields with:
Field name
Data type
Primary key indicator
Nullable status
Show dataspace information if available

Step 5: Offer Next Steps

After displaying results, suggest relevant follow-up actions:

Query data from the DLO
Create calculated insights
Build segments
Set up data streams
Create DMO mappings

API Endpoints Used

List All DLOs

text

GET /services/data/v64.0/ssot/data-lake-objects

Response structure:

json

{
  "dataLakeObjects": [
    {
      "name": "Employee__dll",
      "label": "Employee",
      "category": "Profile",
      "id": "1dlXXXXXXXXXXXXXXX",
      "status": "ACTIVE",
      "totalRecords": 12,
      "fields": [...]
    }
  ],
  "totalSize": 5
}

Get DLO Schema

text

GET /services/data/v64.0/ssot/data-lake-objects/{dlo_name}

Response structure (same as individual object in list response, but wrapped in paginated format).

List All DMOs

text

GET /services/data/v64.0/ssot/data-model-objects

Response structure:

json

{
  "dataModelObjects": [
    {
      "name": "Individual__dlm",
      "label": "Individual",
      "category": "Profile",
      "id": "0dmXXXXXXXXXXXXXXX",
      "fields": [...]
    }
  ],
  "totalSize": 10
}

Get DMO Schema

text

GET /services/data/v64.0/ssot/data-model-objects/{dmo_name}

Response structure (same as individual object in list response, but wrapped in paginated format).

Error Handling

Common Issues:

Org not connected

Message: "Org not connected"
Solution: Ask user to authenticate via SF CLI

DLO not found

Message: "DLO 'XYZ__dll' not found"
Solution: List all DLOs first to verify name

DMO not found

Message: "DMO 'XYZ__dlm' not found"
Solution: List all DMOs first to verify name

Permission issues

Message: HTTP 403 errors
Solution: Verify user has Data Cloud permissions

API version mismatch

Current: v64.0
Solution: Script can be updated for newer API versions

Example Usage

Example 1: List all DLOs

text

User: "Show me all DLOs in afvibe org"

Response:
1. Run sf org list to discover connected org alias
2. Authenticate to afvibe
3. Run: python3 ./scripts/get_dlo_schema.py afvibe
4. Display formatted list of DLOs

Example 2: Get specific DLO schema

text

User: "Get the schema for Employee__dll in afvibe"

Response:
1. Run sf org list to discover connected org alias
2. Authenticate to afvibe
3. Run: python3 ./scripts/get_dlo_schema.py afvibe Employee__dll
4. Display field schema with types and metadata

Example 3: Explore DLOs then get schema

text

User: "What DLOs exist in myorg and show me the schema for the Employee one"

Response:
1. Run sf org list to discover connected org alias
2. List all DLOs in myorg
3. Identify Employee__dll
4. Get detailed schema for Employee__dll
5. Present both results

Example 4: List all DMOs

text

User: "Show me all DMOs in afvibe org"

Response:
1. Run sf org list to discover connected org alias
2. Authenticate to afvibe
3. Run: python3 ./scripts/get_dmo_schema.py afvibe
4. Display formatted list of DMOs

Example 5: Get specific DMO schema

text

User: "Get the schema for Individual__dlm in afvibe"

Response:
1. Run sf org list to discover connected org alias
2. Authenticate to afvibe
3. Run: python3 ./scripts/get_dmo_schema.py afvibe Individual__dlm
4. Display field schema with types and metadata

Example 6: Explore DMOs then get schema

text

User: "What DMOs exist in myorg and show me the schema for the Individual one"

Response:
1. Run sf org list to discover connected org alias
2. List all DMOs in myorg
3. Identify Individual__dlm
4. Get detailed schema for Individual__dlm
5. Present both results

Output Format

DLO List Output

text

Found 5 DLOs in org 'afvibe':

1. DataCustomCodeLogs__dll
   Label: DataCustomCodeLogs
   Category: Engagement
   Records: 233

2. Employee__dll
   Label: Employee
   Category: Profile
   Records: 12

[...]

DLO Schema Output

yaml

DLO: Employee__dll
Label: Employee
Category: Profile
Status: ACTIVE
Records: 12

Custom Fields:
  • id__c (Text) - Primary Key
  • name__c (Text)
  • position__c (Text)
  • manager_id__c (Number)

System Fields:
  • DataSource__c (Text)
  • InternalOrganization__c (Text)
  • cdp_sys_SourceVersion__c (Text)

Next steps:
- Query data: SELECT * FROM Employee__dll LIMIT 10
- Create segment based on position field
- Set up data stream for real-time updates

DMO List Output

text

Found 10 DMOs in org 'afvibe':

1. Individual__dlm
   Label: Individual
   Category: Profile

2. ContactPointEmail__dlm
   Label: Contact Point Email
   Category: Profile

[...]

DMO Schema Output

yaml

DMO: Individual__dlm
Label: Individual
Category: Profile
Description: Represents an individual person

Fields:
  • Id__c (Text) - Primary Key
  • FirstName__c (Text)
  • LastName__c (Text)
  • BirthDate__c (DateTime)

Next steps:
- Query data: SELECT * FROM Individual__dlm LIMIT 10
- View DLO mappings to this DMO
- Create calculated insights

Notes

DLO names always end with __dll suffix
DMO names always end with __dlm suffix
Field names always end with __c suffix
System fields (DataSource_c, KQ, cdp_sys_) are automatically added
Primary key fields are required for DLO and DMO queries
API supports pagination (limit/offset) for large result sets

Related Skills

datakit_workflow: For DMO mapping operations
datakit_validation: For validating datakit configurations
Use this skill before creating DMO mappings to understand source DLO structure