Data360 Connect

data360-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 data360-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 → data360-prepare
• creating DMOs, mappings, IR rulesets, or data graphs → data360-harmonize
• writing Data Cloud SQL or search-index workflows → data360-query

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 ../data360-orchestrate/references/plugin-setup.md.
• Run the shared readiness classifier before mutating connections: node ../data360-orchestrate/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

node ../data360-orchestrate/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>

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

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

• 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
• ../data360-orchestrate/references/plugin-setup.md
• ../data360-orchestrate/references/feature-readiness.md