data360-schema-get — скилл для Claude Code: схема DLO и DMO
Достаёт из Salesforce Data Cloud список объектов Data Lake и Data Model и поля конкретного объекта. Пригодится, когда надо посмотреть структуру данных перед запросом.
npx skills add forcedotcom/sf-skills --skill data360-schema-get -a claude-codeЧто делает data360-schema-get
Скилл обращается к SSOT REST API Data Cloud и возвращает либо перечень всех DLO или DMO в организации, либо подробную схему одного объекта: имена полей, типы, признак первичного ключа, nullable. Список DLO показывает категорию, статус и число записей, так что сразу видно, где лежат данные, а где пусто.
Внутри два Python-скрипта в подкаталоге scripts/: get_dlo_schema.py и get_dmo_schema.py. Первым аргументом идёт alias организации из SF CLI, вторым — необязательное developer name объекта вроде Employee__dll или Individual__dlm. Перед вызовом скилл сам прогоняет sf org list и sf org display --target-org <org_alias> --json, чтобы убедиться, что организация подключена.
Нужны sf CLI версии 2.0.0 и выше, python3 от 3.10.0 и pip от 23.0.0, а также включённый Data Cloud и права на него в целевой организации. Если объект не найден, скрипт скажет об этом прямо — тогда сначала запросите полный список и сверьте developer name. Ошибка 403 означает, что прав на Data Cloud у пользователя нет, и доступа к схеме не будет, пока их не выдадут.
Какие задачи решает
- посмотреть все DLO в организации Data Cloud
- вытащить типы полей конкретного DMO
- найти первичный ключ и nullable-поля объекта
- отличить кастомные поля от системных вроде
cdp_sys_* - проверить, в каком DLO есть записи, а в каком ноль
Примеры запросов агенту
- Покажи все DLO в организации myorg
- Дай схему полей Employee__dll в afvibe
- Какие DMO есть в myorg и какие поля у Individual__dlm
Как установить data360-schema-get
1Через Skills CLI (Vercel)
npx skills add forcedotcom/sf-skills --skill data360-schema-get -a claude-code2Вручную: положить файлы
~/.claude/skills/data360-schema-get/SKILL.mdПапка скилла целиком; для одного проекта — .claude/skills/data360-schema-get/
1Через Skills CLI (Vercel)
npx skills add forcedotcom/sf-skills --skill data360-schema-get -a codex2Вручную: положить файлы
.agents/skills/data360-schema-get/SKILL.mdДля всех проектов — ~/.agents/skills/
1Через Skills CLI (Vercel)
npx skills add forcedotcom/sf-skills --skill data360-schema-get -a cursor2Вручную: положить файлы
.cursor/skills/data360-schema-get/SKILL.mdCursor читает и .agents/skills/, и .claude/skills/
1GitHub CLI
gh skill install forcedotcom/sf-skills data360-schema-get2Вручную: положить файлы
.github/skills/data360-schema-get/SKILL.md1Через Skills CLI (Vercel)
npx skills add forcedotcom/sf-skills --skill data360-schema-get -a gemini-cli2Командой платформы
gemini skills install https://github.com/forcedotcom/sf-skills --path skills/data360-schema-getВручную: положить файлы
.devin/skills/data360-schema-get/SKILL.mdСтарый путь .windsurf/skills/ тоже читается
Через Skills CLI (Vercel)
npx skills add forcedotcom/sf-skills --skill data360-schema-get -a opencodeClaude.ai
Упакуйте папку в zip и загрузите в Settings → Capabilities → Skills
По шагам, с командами для каждой платформы: Как установить скилл в Claude Code: пять способов
Вопросы о data360-schema-get
Как установить data360-schema-get в Claude?
Выполните npx skills add forcedotcom/sf-skills --skill data360-schema-get -a claude-code. Скилл встанет в ~/.claude/skills/data360-schema-get/SKILL.md.
Что делает data360-schema-get?
Через SSOT REST API Data Cloud получает список DLO или DMO и схему полей конкретного объекта: тип, первичный ключ, nullable, число записей.
Что нужно, чтобы скилл заработал?
Установленный и авторизованный sf CLI, python3 от 3.10.0, pip от 23.0.0, включённый Data Cloud и права на него. Без авторизации в организации скрипты не отработают.
Исходный текст
SKILL.md · Apache-2.0
---
name: data360-schema-get
description: "Retrieve Data Lake Object (DLO) and Data Model Object (DMO) schema information from Salesforce Data Cloud using REST APIs. Use this skill when you need to inspect DLO or DMO field definitions, data types, or metadata. Takes org alias and optional DLO/DMO name as parameters."
metadata:
version: "1.0"
domains: ["Data 360"]
cliTools:
- tool: ["pip"]
semver: ">=23.0.0"
- tool: ["python3"]
semver: ">=3.10.0"
- tool: ["sf"]
semver: ">=2.0.0"
---
# 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
1. **org_alias** (required): The SF CLI org alias (e.g., 'afvibe', 'myorg')
2. **dlo_name** (optional): Specific DLO developer name (e.g., 'Employee__dll')
3. **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 │ [email protected] │ 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, cdp_sys_*)
- 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:**
1. **Org not connected**
- Message: "Org not connected"
- Solution: Ask user to authenticate via SF CLI
2. **DLO not found**
- Message: "DLO 'XYZ__dll' not found"
- Solution: List all DLOs first to verify name
5. **DMO not found**
- Message: "DMO 'XYZ__dlm' not found"
- Solution: List all DMOs first to verify name
3. **Permission issues**
- Message: HTTP 403 errors
- Solution: Verify user has Data Cloud permissions
4. **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
© forcedotcom · Apache-2.0 · forcedotcom/sf-skills