sf-datacloud-prepare: Data Cloud Prepare Phase

Use this skill when the user needs ingestion and lake preparation work: data streams, Data Lake Objects, transforms, or DocAI-based extraction.

When This Skill Owns the Task

Use sf-datacloud-prepare when the work involves:

• sf data360 data-stream *
• sf data360 dlo *
• sf data360 transform *
• sf data360 docai *
• choosing how data should enter Data Cloud

Delegate elsewhere when the user is:

• still creating/testing source connections → sf-datacloud-connect
• mapping to DMOs or designing IR/data graphs → sf-datacloud-harmonize
• querying ingested data → sf-datacloud-retrieve

Required Context to Gather First

Ask for or infer:

• target org alias
• source connection name
• source object / dataset
• desired stream type
• DLO naming expectations
• whether the user is creating, updating, running, or deleting a stream

Core Operating Rules

• Verify the external plugin runtime before running Data Cloud commands.
• Run the shared readiness classifier before mutating ingestion assets: node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase prepare --json.
• Prefer inspecting existing streams and DLOs before creating new ingestion assets.
• Suppress linked-plugin warning noise with 2>/dev/null for normal usage.
• Treat DLO naming and field naming as Data Cloud-specific, not CRM-native.
• Confirm whether each dataset should be treated as Profile, Engagement, or Other before creating the stream.
• Hand off to Harmonize only after ingestion assets are clearly healthy.

Recommended Workflow

1. Classify readiness for prepare work

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

2. Inspect existing ingestion assets

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

3. Confirm the stream category before creation

Use these rules when suggesting categories:

When the source is ambiguous, ask the user explicitly whether the dataset should be treated as Profile, Engagement, or Other.

4. Create or inspect streams intentionally

sf data360 data-stream get -o <org> --name <stream> 2>/dev/null
sf data360 data-stream create-from-object -o <org> --object Contact --connection SalesforceDotCom_Home 2>/dev/null
sf data360 data-stream create -o <org> -f stream.json 2>/dev/null

5. Check DLO shape

sf data360 dlo get -o <org> --name Contact_Home__dll 2>/dev/null

6. Only then move into harmonization

Once the stream and DLO are healthy, hand off to sf-datacloud-harmonize.

High-Signal Gotchas

• CRM-backed stream behavior is not the same as fully custom connector-framework ingestion.
• Some external database connectors can be created via API while stream creation still requires UI flow or org-specific browser automation. Do not promise a pure CLI stream-creation path for every connector type.
• Stream deletion can also delete the associated DLO unless the delete mode says otherwise.
• DLO field naming differs from CRM field naming.
• Query DLO record counts with Data Cloud SQL instead of assuming list output is sufficient.
• CdpDataStreams means the stream module is gated for the current org/user; guide the user to provisioning/permissions review instead of retrying blindly.

Output Format

Prepare task: <stream / dlo / transform / docai>
Source: <connection + object>
Target org: <alias>
Artifacts: <stream names / dlo names / json definitions>
Verification: <passed / partial / blocked>
Next step: <harmonize or retrieve>

References

• README.md
• ../sf-datacloud/assets/definitions/data-stream.template.json
• ../sf-datacloud/references/plugin-setup.md
• ../sf-datacloud/references/feature-readiness.md