Salesforce Data Cloud Prepare phase. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use sf-datacloud-connect), DMOs and identity resolutio

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](../sf datacloud connect/SKILL.md) mapping to DMOs or designing IR/data graphs → [sf datacloud harmonize](../sf datacloud harmonize/SKILL.md) querying ingested data → [sf datacloud retrieve](../sf datacloud retrieve/SKILL.md) 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 2. Inspect existing ingestion assets 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 5. Check DLO shape 6. Choose the right refresh mechanism Use the smaller refresh scope that matches the user goal: 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: 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: 1. create the connector in [sf datacloud connect](../sf datacloud connect/SKILL.md) 2. upload the schema with sf data360 connection schema upsert 3. create the stream in the UI when required 4. send records with the local example in examples/ingestion api/ 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](../sf datacloud harmonize/SKILL.md). 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 References [README.md](README.md) [examples/ingestion api/README.md](examples/ingestion api/README.md) [../sf datacloud/assets/definitions/data stream.template.json](../sf datacloud/assets/definitions/data stream.template.json) [../sf datacloud/references/plugin setup.md](../sf datacloud/references/plugin setup.md) [../sf datacloud/references/feature readiness.md](../sf datacloud/references/feature readiness.md)

sf-datacloud-prepare