sf-datacloud-connect: 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 sf-datacloud-connect 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 → sf-datacloud-prepare
creating DMOs, mappings, IR rulesets, or data graphs → sf-datacloud-harmonize
writing Data Cloud SQL or search-index workflows → sf-datacloud-retrieve

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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

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Core Operating Rules

Verify the plugin runtime first; see ../sf-datacloud/references/plugin-setup.md.
Run the shared readiness classifier before mutating connections: node ~/.claude/skills/sf-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.

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Recommended Workflow

1. Classify readiness for connect work

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

2. Discover connector types

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

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

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

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:

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:

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

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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.

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Output Format

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>

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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
../sf-datacloud/references/plugin-setup.md
../sf-datacloud/references/feature-readiness.md
../sf-datacloud/UPSTREAM.md

sf-datacloud-connect