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 (DLOs), transforms, Document AI, unstructured ingestion, or the handoff from connector setup into a live stream.

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
rerunning or rescanning ingestion after a source update
preparing Ingestion API-backed streams after connector setup is complete

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 / document source
desired stream type
DLO naming expectations
whether the user is creating, updating, running, or deleting a stream
whether the source is CRM, a database connector, an unstructured file source, or an Ingestion API feed

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.
Distinguish stream-level refresh from connection-level reruns when working with unstructured sources.
Use UI setup intentionally when initial stream or unstructured asset creation is platform-gated.
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
sf data360 data-stream run -o <org> --name <stream> 2>/dev/null

5. Check DLO shape

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

6. Choose the right refresh mechanism

Use the smaller refresh scope that matches the user goal:

sf data360 data-stream run -o <org> --name <stream> 2>/dev/null
sf data360 connection run-existing -o <org> --name <connection-id> 2>/dev/null

data-stream run is the closest match to a stream-level refresh or re-scan.
connection run-existing runs at the connection level and can be useful for some connector workflows, but it is not a reliable replacement for stream refresh on unstructured sources.
For unstructured document connectors, prefer data-stream run when the goal is to re-scan newly added or changed files.

7. Handle unstructured sources deliberately

For SharePoint-style document ingestion, a minimal unstructured DLO payload can look like:

{
  "name": "my_udlo",
  "label": "My UDLO",
  "category": "Directory_Table",
  "dataSource": {
    "sourceType": "SF_DRIVE",
    "directoryAndFilesDetails": [
      {
        "dirName": "SPUnstructuredDocument/<CONNECTION_ID>/<SITE_ID>",
        "fileName": "*"
      }
    ],
    "sourceConfig": {
      "reservedPrefix": "$dcf_content$"
    }
  }
}

Use the UI for the first-time unstructured setup when the user needs the richer end-to-end pipeline. The UI path can seed additional document metadata fields and downstream assets that a bare CLI DLO create flow may not provision automatically.

8. Use the local Ingestion API example for send-data workflows

For external systems pushing records into Data Cloud:

create the connector in sf-datacloud-connect
upload the schema with sf data360 connection schema-upsert
create the stream in the UI when required
send records with the local example in examples/ingestion-api/

cd examples/ingestion-api
cp .env.example .env
python3 send-data.py

Key details:

auth is a staged flow: JWT → Salesforce token → Data Cloud token
the ingestion endpoint uses the tenant URL, not the Salesforce instance URL
202 means the payload was accepted for processing, not that records are queryable immediately
validation failures often surface in the Problem Records DLO family

9. 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.
sf data360 data-stream run and sf data360 connection run-existing are not interchangeable; prefer stream-level refresh for unstructured rescans.
SFDC streams sync on a platform-managed schedule; data-stream run is not the general control path for CRM connector refresh.
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.
Initial SharePoint-style unstructured setup can be richer in the UI than in a minimal CLI DLO create flow.
Stream deletion can also delete the associated DLO unless the delete mode says otherwise.
DLO field naming differs from CRM field naming, including __c → _c transformations.
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