connecting-datacloud

Original：🇺🇸 English

Translated

Salesforce Data Cloud Connect phase. Use this skill when the user manages Data Cloud connections, connectors, or sets up a new source system. TRIGGER when: user manages Data Cloud connections, connectors, connector metadata, tests a connection, browses source objects or databases, or sets up a new source system. DO NOT TRIGGER when: the task is about data streams or DLOs (use preparing-datacloud), DMOs or identity resolution (use harmonizing-datacloud), retrieval/search (use retrieving-datacloud), or STDM telemetry (use observing-agentforce).

3installs

Sourceforcedotcom/afv-library

Added on2026-05-15

NPX Install

npx skill4agent add forcedotcom/afv-library connecting-datacloud

SKILL.md Content

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connecting-datacloud: Data Cloud Connect Phase

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.

When This Skill Owns the Task

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

Required Context to Gather First

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

Core Operating Rules

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.

Recommended Workflow

1. Classify readiness for connect work

bash

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

2. Discover connector types

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

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

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

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

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

7. Discover payload fields for unknown connector types

Create one in the UI, then inspect it directly:

bash

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

High-Signal Gotchas

```
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.

Output Format

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>

References

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

connecting-datacloud

NPX Install

Tags

SKILL.md Content

connecting-datacloud: Data Cloud Connect Phase

When This Skill Owns the Task

Required Context to Gather First

Core Operating Rules

Recommended Workflow

1. Classify readiness for connect work

2. Discover connector types

3. Inspect connections by type

4. Inspect a specific connection or uploaded schema

5. Test or create only after discovery

6. Start from curated example payloads for external connectors

7. Discover payload fields for unknown connector types

High-Signal Gotchas

Output Format

References