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 T

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](../sf datacloud connect/SKILL.md) connections, connectors, source discovery Prepare [sf datacloud prepare](../sf datacloud prepare/SKILL.md) data streams, DLOs, transforms, DocAI Harmonize [sf datacloud harmonize](../sf datacloud harmonize/SKILL.md) DMOs, mappings, identity resolution, data graphs Segment [sf datacloud segment](../sf datacloud segment/SKILL.md) segments, calculated insights Act [sf datacloud act](../sf datacloud act/SKILL.md) activations, activation targets, data actions Retrieve [sf datacloud retrieve](../sf datacloud retrieve/SKILL.md) SQL, search indexes, vector search, async query Delegate outside the family when the user is: extracting Session Tracing / STDM telemetry → [sf ai agentforce observability](../sf ai agentforce observability/SKILL.md) writing CRM SOQL only → [sf soql](../sf soql/SKILL.md) loading CRM source data → [sf data](../sf data/SKILL.md) creating missing CRM schema → [sf metadata](../sf metadata/SKILL.md) implementing downstream Apex or Flow logic → [sf apex](../sf apex/SKILL.md), [sf flow](../sf flow/SKILL.md) 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/plugin setup.md) [references/feature readiness.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: 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: Only use a query plane probe after you know the table name is real: 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: 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: 1. Task classification 2. Runtime status 3. Readiness classification 4. Phase(s) involved 5. Commands or artifacts used 6. Verification result 7. Next recommended step Suggested shape: Cross Skill Integration Need Delegate to Reason load or clean CRM source data [sf data](../sf data/SKILL.md) seed or fix source records before ingestion create missing CRM schema [sf metadata](../sf metadata/SKILL.md) Data Cloud expects existing objects/fields deploy permissions or bundles [sf deploy](../sf deploy/SKILL.md) environment preparation write Apex against Data Cloud outputs [sf apex](../sf apex/SKILL.md) code implementation Flow automation after segmentation/activation [sf flow](../sf flow/SKILL.md) declarative orchestration session tracing / STDM / parquet analysis [sf ai agentforce observability](../sf ai agentforce observability/SKILL.md) different Data Cloud use case Reference Map Start here [README.md](README.md) [references/plugin setup.md](references/plugin setup.md) [references/feature readiness.md](references/feature readiness.md) [UPSTREAM.md](UPSTREAM.md) Phase skills [sf datacloud connect](../sf datacloud connect/SKILL.md) [sf datacloud prepare](../sf datacloud prepare/SKILL.md) [sf datacloud harmonize](../sf datacloud harmonize/SKILL.md) [sf datacloud segment](../sf datacloud segment/SKILL.md) [sf datacloud act](../sf datacloud act/SKILL.md) [sf datacloud retrieve](../sf datacloud retrieve/SKILL.md) Deterministic helpers [scripts/bootstrap plugin.sh](scripts/bootstrap plugin.sh) [scripts/verify plugin.sh](scripts/verify plugin.sh) [scripts/diagnose org.mjs](scripts/diagnose org.mjs) [assets/definitions/](assets/definitions/)

sf-datacloud