| name | sf-datacloud |
| description | 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).
|
| license | MIT |
| compatibility | Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org |
| metadata | {"version":"1.0.0","author":"Gnanasekaran Thoppae","runtime":"External sf data360 community CLI plugin"} |
sf-datacloud: Salesforce Data Cloud Orchestrator
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.
When This Skill Owns the Task
Use sf-datacloud when the work involves:
- multi-phase Data Cloud setup or remediation
- data spaces (
sf data360 data-space *)
- data kits (
sf data360 data-kit *)
- health checks (
sf data360 doctor)
- CRM-to-unified-profile pipeline design
- deciding how to move from ingestion → harmonization → segmentation → activation
- cross-phase troubleshooting where the root cause is not yet clear
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:
Required Context to Gather First
Ask for or infer:
- target org alias
- whether the plugin is already installed and linked
- whether the user wants design guidance, read-only inspection, or live mutation
- data sources involved: CRM objects, external databases, file ingestion, knowledge, etc.
- desired outcome: unified profiles, segments, activations, vector search, analytics, or troubleshooting
- whether the user is working in the default data space or a custom one
- whether the org has already been classified with
scripts/diagnose-org.mjs
- which command family is failing today, if any
If plugin availability or org readiness is uncertain, start with:
Core Operating Rules
- Use the external
sf data360 plugin runtime; do not reimplement or vendor the command layer.
- Prefer the smallest phase-specific skill once the task is localized.
- Run readiness classification before mutation-heavy work. Prefer
scripts/diagnose-org.mjs over guessing from one failing command.
- For
sf data360 commands, suppress linked-plugin warning noise with 2>/dev/null unless the stderr output is needed for debugging.
- Distinguish Data Cloud SQL from CRM SOQL.
- Do not treat
sf data360 doctor as a full-product readiness check; the current upstream command only checks the search-index surface.
- Do not treat
query describe as a universal tenant probe; only use it with a known DMO/DLO table after broader readiness is confirmed.
- Preserve Data Cloud-specific API-version workarounds when they matter.
- Prefer generic, reusable JSON definition files over org-specific workshop payloads.
Recommended Workflow
1. Verify the runtime and auth
Confirm:
sf is installed- the community Data Cloud plugin is linked
- the target org is authenticated
Recommended checks:
sf data360 man
sf org display -o <alias>
bash ~/.claude/skills/sf-datacloud/scripts/verify-plugin.sh <alias>
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.
2. Classify readiness before changing anything
Run the shared classifier first:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --json
Only use a query-plane probe after you know the table name is real:
node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase retrieve --describe-table MyDMO__dlm --json
Use the classifier to distinguish:
- empty-but-enabled modules
- feature-gated modules
- query-plane issues
- runtime/auth failures
3. Discover existing state with read-only commands
Use targeted inspection after classification:
sf 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/null
4. Localize the phase
Route the task:
- source/connector issue → Connect
- ingestion/DLO/stream issue → Prepare
- mapping/IR/unified profile issue → Harmonize
- audience or insight issue → Segment
- downstream push issue → Act
- SQL/search/index issue → Retrieve
5. Choose deterministic artifacts when possible
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.json
6. Verify after each phase
Typical verification:
- stream/DLO exists
- DMO/mapping exists
- identity resolution run completed
- unified records or segment counts look correct
- activation/search index status is healthy
High-Signal Gotchas
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.- Segment creation may need
--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.- Many long-running jobs are asynchronous in practice even when the command returns quickly.
- Some Data Cloud operations still require UI setup outside the CLI runtime.
Output Format
When finishing, report in this order:
- Task classification
- Runtime status
- Readiness classification
- Phase(s) involved
- Commands or artifacts used
- Verification result
- Next recommended step
Suggested shape:
Data 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>
Cross-Skill Integration
| 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 |
Reference Map
Start here
Phase skills
Deterministic helpers
