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:

extracting Session Tracing / STDM telemetry → sf-ai-agentforce-observability
writing CRM SOQL only → sf-soql
loading CRM source data → sf-data
creating missing CRM schema → sf-metadata
implementing downstream Apex or Flow logic → sf-apex, sf-flow

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:

references/plugin-setup.md
references/feature-readiness.md
scripts/verify-plugin.sh
scripts/diagnose-org.mjs
scripts/bootstrap-plugin.sh

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.json
assets/definitions/dmo.template.json
assets/definitions/mapping.template.json
assets/definitions/relationship.template.json
assets/definitions/identity-resolution.template.json
assets/definitions/data-graph.template.json
assets/definitions/calculated-insight.template.json
assets/definitions/segment.template.json
assets/definitions/activation-target.template.json
assets/definitions/activation.template.json
assets/definitions/data-action-target.template.json
assets/definitions/data-action.template.json
assets/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

sf-datacloud