connecting-datacloud

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Original

English

🇨🇳

Translation

Chinese

connecting-datacloud: Data Cloud Connect Phase

connecting-datacloud: Data Cloud Connect阶段

Use this skill when the user needs source connection work: connector discovery, connection metadata, connection testing, source-object browsing, connector schema inspection, or connector-specific setup payloads for external sources.

当用户需要进行源连接工作时使用此技能：连接器发现、连接元数据、连接测试、源对象浏览、连接器架构检查，或为外部源准备特定于连接器的设置负载。

When This Skill Owns the Task

此技能负责的任务场景

Use

connecting-datacloud

when the work involves:

```
sf data360 connection *
```
connector catalog inspection
connection creation, update, test, or delete
browsing source objects, fields, databases, or schemas
identifying connector types already in use
preparing connector definitions for Snowflake, SharePoint Unstructured, or Ingestion API sources

Delegate elsewhere when the user is:

creating data streams or DLOs → preparing-datacloud
creating DMOs, mappings, IR rulesets, or data graphs → harmonizing-datacloud
writing Data Cloud SQL or search-index workflows → retrieving-datacloud

当工作涉及以下内容时，使用

connecting-datacloud

：

```
sf data360 connection *
```
命令
连接器目录检查
连接的创建、更新、测试或删除
浏览源对象、字段、数据库或架构
识别已在使用的连接器类型
为Snowflake、SharePoint Unstructured或Ingestion API源准备连接器定义

当用户进行以下操作时，请转交至其他技能：

创建数据流或DLO → preparing-datacloud
创建DMO、映射、IR规则集或数据图谱 → harmonizing-datacloud
编写Data Cloud SQL或搜索索引工作流 → retrieving-datacloud

Required Context to Gather First

首先需要收集的必要上下文

Ask for or infer:

target org alias
connector type or source system
whether the user wants inspection only or live mutation
connection name or ID if one already exists
whether credentials are already configured outside the CLI
whether the user also expects stream creation right after connection setup
whether the source is a database, an unstructured document source, or an Ingestion API feed

询问或推断：

目标组织别名
连接器类型或源系统
用户仅需检查还是需要实际修改
若连接已存在，需提供连接名称或ID
凭据是否已在CLI外部配置完成
用户是否希望在连接设置完成后立即创建数据流
源是数据库、非结构化文档源还是Ingestion API馈送

Core Operating Rules

核心操作规则

Verify the plugin runtime first; see ../orchestrating-datacloud/references/plugin-setup.md.

Run the shared readiness classifier before mutating connections:

node ~/.claude/skills/orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase connect --json

Prefer read-only discovery before connection creation.
Suppress linked-plugin warning noise with
```
2>/dev/null
```
for standard usage.
Remember that
```
connection list
```
requires
```
--connector-type
```
.
For
```
connection test
```
, pass
```
--connector-type
```
when resolving a non-Salesforce connection by name.
Discover existing connector types from streams first when the org is unfamiliar.
Use curated example payloads before inventing connector-specific credentials or parameters.
For connector types outside the curated examples, inspect a known-good UI-created connection via REST before building JSON.
Do not promise API-based stream creation for every connector type just because connection creation succeeds.

首先验证插件运行时；请参阅../orchestrating-datacloud/references/plugin-setup.md。

在修改连接之前运行共享就绪分类器：

node ~/.claude/skills/orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase connect --json

。

在创建连接之前优先进行只读发现操作。
使用
```
2>/dev/null
```
抑制标准使用中的链接插件警告信息。
记住
```
connection list
```
命令需要
```
--connector-type
```
参数。
对于
```
connection test
```
命令，当通过名称解析非Salesforce连接时，需传递
```
--connector-type
```
参数。
当不熟悉组织环境时，先从数据流中发现现有连接器类型。
在自行创建连接器特定的凭据或参数之前，优先使用经过整理的示例负载。
对于整理示例之外的连接器类型，先通过REST检查一个已知可用的UI创建连接，再构建JSON。
不要仅仅因为连接创建成功，就承诺为每种连接器类型都支持基于API的数据流创建。

Recommended Workflow

推荐工作流

1. Classify readiness for connect work

1. 分类连接工作的就绪状态

bash

node ~/.claude/skills/orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase connect --json

bash

node ~/.claude/skills/orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase connect --json

2. Discover connector types

2. 发现连接器类型

bash

sf data360 connection connector-list -o <org> 2>/dev/null
sf data360 data-stream list -o <org> 2>/dev/null

bash

sf data360 connection connector-list -o <org> 2>/dev/null
sf data360 data-stream list -o <org> 2>/dev/null

3. Inspect connections by type

3. 按类型检查连接

bash

sf data360 connection list -o <org> --connector-type SalesforceDotCom 2>/dev/null
sf data360 connection list -o <org> --connector-type REDSHIFT 2>/dev/null
sf data360 connection list -o <org> --connector-type SNOWFLAKE 2>/dev/null

bash

sf data360 connection list -o <org> --connector-type SalesforceDotCom 2>/dev/null
sf data360 connection list -o <org> --connector-type REDSHIFT 2>/dev/null
sf data360 connection list -o <org> --connector-type SNOWFLAKE 2>/dev/null

4. Inspect a specific connection or uploaded schema

4. 检查特定连接或已上传的架构

bash

sf data360 connection get -o <org> --name <connection> 2>/dev/null
sf data360 connection objects -o <org> --name <connection> 2>/dev/null
sf data360 connection fields -o <org> --name <connection> 2>/dev/null
sf data360 connection schema-get -o <org> --name <connection-id> 2>/dev/null

bash

sf data360 connection get -o <org> --name <connection> 2>/dev/null
sf data360 connection objects -o <org> --name <connection> 2>/dev/null
sf data360 connection fields -o <org> --name <connection> 2>/dev/null
sf data360 connection schema-get -o <org> --name <connection-id> 2>/dev/null

5. Test or create only after discovery

5. 仅在发现后进行测试或创建

bash

sf data360 connection test -o <org> --name <connection> --connector-type <type> 2>/dev/null
sf data360 connection create -o <org> -f connection.json 2>/dev/null

bash

sf data360 connection test -o <org> --name <connection> --connector-type <type> 2>/dev/null
sf data360 connection create -o <org> -f connection.json 2>/dev/null

6. Start from curated example payloads for external connectors

6. 从外部连接器的整理示例负载开始

Use the phase-owned examples before inventing a payload from scratch:

examples/connections/heroku-postgres.json

```
examples/connections/redshift.json
```

examples/connections/sharepoint-unstructured.json

examples/connections/snowflake-connection.json

examples/connections/ingest-api-connection.json

examples/connections/ingest-api-schema.json

Typical Ingestion API setup flow:

bash

sf data360 connection create -o <org> -f examples/connections/ingest-api-connection.json 2>/dev/null
sf data360 connection schema-upsert -o <org> --name <connector-id> -f examples/connections/ingest-api-schema.json 2>/dev/null
sf data360 connection schema-get -o <org> --name <connector-id> 2>/dev/null

在自行创建负载之前，先使用本阶段提供的示例：

examples/connections/heroku-postgres.json

```
examples/connections/redshift.json
```

examples/connections/sharepoint-unstructured.json

examples/connections/snowflake-connection.json

examples/connections/ingest-api-connection.json

examples/connections/ingest-api-schema.json

典型的Ingestion API设置流程：

bash

sf data360 connection create -o <org> -f examples/connections/ingest-api-connection.json 2>/dev/null
sf data360 connection schema-upsert -o <org> --name <connector-id> -f examples/connections/ingest-api-schema.json 2>/dev/null
sf data360 connection schema-get -o <org> --name <connector-id> 2>/dev/null

7. Discover payload fields for unknown connector types

7. 为未知连接器类型发现负载字段

Create one in the UI, then inspect it directly:

bash

sf api request rest "/services/data/v66.0/ssot/connections/<id>" -o <org>

先在UI中创建一个连接，然后直接检查它：

bash

sf api request rest "/services/data/v66.0/ssot/connections/<id>" -o <org>

High-Signal Gotchas

高信号注意事项

```
connection list
```
has no true global "list all" mode; query by connector type.
The connector catalog name and connection connector type are not always the same label.
```
connection test
```
may need
```
--connector-type
```
for name resolution when the source is not a default Salesforce connector.
An empty connection list usually means "enabled but not configured yet", not "feature disabled".
Heroku Postgres, Redshift, Snowflake, SharePoint Unstructured, and Ingestion API all use different credential and parameter shapes; reuse the curated examples instead of guessing.
SharePoint Unstructured uses
```
clientId
```
,
```
clientSecret
```
, and
```
tokenEndpoint
```
in the
```
credentials
```
array and does not require a
```
parameters
```
array.
Snowflake uses key-pair auth and can often be created through the API, but downstream stream creation can still remain UI-only.
Ingestion API connector setup is incomplete until
```
connection schema-upsert
```
has uploaded the object schema.
Some external connector credential setup still depends on UI-side configuration or external-system permissions.

```
connection list
```
没有真正的全局“列出全部”模式；需按连接器类型查询。
连接器目录名称和连接连接器类型并不总是相同的标签。
当源不是默认Salesforce连接器时，
```
connection test
```
可能需要
```
--connector-type
```
来进行名称解析。
空的连接列表通常意味着“已启用但尚未配置”，而非“功能已禁用”。
Heroku Postgres、Redshift、Snowflake、SharePoint Unstructured和Ingestion API都使用不同的凭据和参数结构；请复用整理好的示例，不要自行猜测。
SharePoint Unstructured在
```
credentials
```
数组中使用
```
clientId
```
、
```
clientSecret
```
和
```
tokenEndpoint
```
，且不需要
```
parameters
```
数组。
Snowflake使用密钥对认证，通常可以通过API创建，但下游数据流创建可能仍需通过UI完成。
直到
```
connection schema-upsert
```
上传了对象架构，Ingestion API连接器的设置才算完成。
某些外部连接器的凭据设置仍然依赖于UI端配置或外部系统权限。

Output Format

输出格式

text

Connect task: <inspect / create / test / update>
Connector type: <SalesforceDotCom / REDSHIFT / SNOWFLAKE / SPUnstructuredDocument / IngestApi / ...>
Target org: <alias>
Commands: <key commands run>
Verification: <passed / partial / blocked>
Next step: <prepare phase or connector follow-up>

text

连接任务: <检查 / 创建 / 测试 / 更新>
连接器类型: <SalesforceDotCom / REDSHIFT / SNOWFLAKE / SPUnstructuredDocument / IngestApi / ...>
目标组织: <别名>
执行命令: <已运行的关键命令>
验证结果: <通过 / 部分通过 / 受阻>
下一步: <准备阶段或连接器后续操作>

References

参考资料

README.md
examples/connections/heroku-postgres.json
examples/connections/redshift.json
examples/connections/sharepoint-unstructured.json
examples/connections/snowflake-connection.json
examples/connections/ingest-api-connection.json
examples/connections/ingest-api-schema.json
../orchestrating-datacloud/references/plugin-setup.md
../orchestrating-datacloud/references/feature-readiness.md
../orchestrating-datacloud/UPSTREAM.md

README.md
examples/connections/heroku-postgres.json
examples/connections/redshift.json
examples/connections/sharepoint-unstructured.json
examples/connections/snowflake-connection.json
examples/connections/ingest-api-connection.json
examples/connections/ingest-api-schema.json
../orchestrating-datacloud/references/plugin-setup.md
../orchestrating-datacloud/references/feature-readiness.md
../orchestrating-datacloud/UPSTREAM.md