sf-datacloud-prepare

Original：🇺🇸 English
Translated

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 resolution (use sf-datacloud-harmonize), or query/search work (use sf-datacloud-retrieve).

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Sourcejaganpro/sf-skills
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NPX Install

npx skill4agent add jaganpro/sf-skills sf-datacloud-prepare

Tags

Translated version includes tags in frontmatter
salesforce-dataclouddata-ingestiondata-lake-preparationdocai-integrationdlo-management

SKILL.md Content

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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.
  • Hand off to Harmonize only after ingestion assets are clearly healthy.

Recommended Workflow

1. Classify readiness for prepare work

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

2. Inspect existing ingestion assets

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

3. Create or inspect streams intentionally

bash
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

4. Check DLO shape

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

5. 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.
  • 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

text
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