sf-datacloud-prepare: Data Cloud Prepare Phase Skill

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:

| Category | Use for | Typical requirement | |---|---|---| | Profile | person/entity records | primary key | | Engagement | time-based events or interactions | primary key + event time field | | Other | reference/configuration/supporting datasets | primary key |

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>

Agent Skills: sf-datacloud-prepare: Data Cloud Prepare Phase

Install this agent skill to your local

Skill Files