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SKILL.md
npx skills add jaganpro/sf-skills --skill sf-datacloud
Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
npx skills add jaganpro/sf-skills --skill sf-datacloud
Use this skill when the user needs product-level Data Cloud workflow guidance rather than a single isolated command family: pipeline setup, cross-phase troubleshooting, data spaces, data kits, or deciding whether a task belongs in Connect, Prepare, Harmonize, Segment, Act, or Retrieve.
This skill intentionally follows sf-skills house style while using the external sf data360 command surface as the runtime. The plugin is not vendored into this repo.
Use sf-datacloud when the work involves:
sf data360 data-space *)sf data360 data-kit *)sf data360 doctor)Delegate to a phase-specific skill when the user is focused on one area:
| Phase | Use this skill | Typical scope |
|---|---|---|
| Connect | sf-datacloud-connect | connections, connectors, source discovery |
| Prepare | sf-datacloud-prepare | data streams, DLOs, transforms, DocAI |
| Harmonize | sf-datacloud-harmonize | DMOs, mappings, identity resolution, data graphs |
| Segment | sf-datacloud-segment | segments, calculated insights |
| Act | sf-datacloud-act | activations, activation targets, data actions |
| Retrieve | sf-datacloud-retrieve | SQL, search indexes, vector search, async query |
Delegate outside the family when the user is:
Ask for or infer:
scripts/diagnose-org.mjsIf plugin availability or org readiness is uncertain, start with:
scripts/verify-plugin.shscripts/diagnose-org.mjsscripts/bootstrap-plugin.shsf data360 plugin runtime; do not reimplement or vendor the command layer.scripts/diagnose-org.mjs over guessing from one failing command.sf data360 commands, suppress linked-plugin warning noise with 2>/dev/null unless the stderr output is needed for debugging.sf data360 doctor as a full-product readiness check; the current upstream command only checks the search-index surface.query describe as a universal tenant probe; only use it with a known DMO/DLO table after broader readiness is confirmed.Confirm:
sf is installedRecommended checks:
Treat sf data360 doctor as a broad health signal, not the sole gate. On partially provisioned orgs it can fail even when read-only command families like connectors, DMOs, or segments still work.
Run the shared classifier first:
Only use a query-plane probe after you know the table name is real:
Use the classifier to distinguish:
Use targeted inspection after classification:
Route the task:
Prefer JSON definition files and repeatable scripts over one-off manual steps. Generic templates live in:
assets/definitions/data-stream.template.jsonassets/definitions/dmo.template.jsonassets/definitions/mapping.template.jsonassets/definitions/relationship.template.jsonassets/definitions/identity-resolution.template.jsonassets/definitions/data-graph.template.jsonassets/definitions/calculated-insight.template.jsonassets/definitions/segment.template.jsonassets/definitions/activation-target.template.jsonassets/definitions/activation.template.jsonassets/definitions/data-action-target.template.jsonassets/definitions/data-action.template.jsonassets/definitions/search-index.template.jsonTypical verification:
connection list requires --connector-type.dmo list --all is useful when you need the full catalog, but first-page dmo list is often enough for readiness checks and much faster.--api-version 64.0.segment members returns opaque IDs; use SQL joins for human-readable details.sf data360 doctor can fail on partially provisioned orgs even when some read-only commands still work; fall back to targeted smoke checks.query describe errors such as Couldn't find CDP tenant ID or DataModelEntity ... not found are query-plane clues, not automatic proof that the whole product is disabled.When finishing, report in this order:
Suggested shape:
| Need | Delegate to | Reason |
|---|---|---|
| load or clean CRM source data | sf-data | seed or fix source records before ingestion |
| create missing CRM schema | sf-metadata | Data Cloud expects existing objects/fields |
| deploy permissions or bundles | sf-deploy | environment preparation |
| write Apex against Data Cloud outputs | sf-apex | code implementation |
| Flow automation after segmentation/activation | sf-flow | declarative orchestration |
| session tracing / STDM / parquet analysis | sf-ai-agentforce-observability | different Data Cloud use case |
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --jsonnode ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase retrieve --describe-table MyDMO__dlm --jsonsf data360 doctor -o <org> 2>/dev/null
sf data360 data-space list -o <org> 2>/dev/null
sf data360 data-stream list -o <org> 2>/dev/null
sf data360 dmo list -o <org> 2>/dev/null
sf data360 identity-resolution list -o <org> 2>/dev/null
sf data360 segment list -o <org> 2>/dev/null
sf data360 activation platforms -o <org> 2>/dev/nullData Cloud task: <setup / inspect / troubleshoot / migrate>
Runtime: <plugin ready / missing / partially verified>
Readiness: <ready / ready_empty / partial / feature_gated / blocked>
Phases: <connect / prepare / harmonize / segment / act / retrieve>
Artifacts: <json files, commands, scripts>
Verification: <passed / partial / blocked>
Next step: <next phase, setup guidance, or cross-skill handoff>