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Orchestrating Datacloud

  • 2k installs
  • 763 repo stars
  • Updated July 24, 2026
  • forcedotcom/sf-skills

orchestrating-datacloud is an agent skill that Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the .

About

orchestrating 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 Use orchestrating 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

  • description: "Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this s
  • compatibility: "Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org"
  • Use this skill when the user needs **product-level Data Cloud workflow guidance** rather than a single isolated command
  • Follow orchestrating-datacloud SKILL.md steps and documented constraints.
  • Follow orchestrating-datacloud SKILL.md steps and documented constraints.

Orchestrating Datacloud by the numbers

  • 2,004 all-time installs (skills.sh)
  • +6 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #585 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
  • Security screen: HIGH risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

orchestrating-datacloud capabilities & compatibility

Capabilities
description: "salesforce data cloud product orch · compatibility: "requires an external community s · use this skill when the user needs **product lev · follow orchestrating datacloud skill.md steps an
Use cases
orchestration
From the docs

What orchestrating-datacloud says it does

description: "Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troublesh
SKILL.md
compatibility: "Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org"
SKILL.md
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
SKILL.md
npx skills add https://github.com/forcedotcom/sf-skills --skill orchestrating-datacloud

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Installs2k
repo stars763
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Last updatedJuly 24, 2026
Repositoryforcedotcom/sf-skills

When should an agent use orchestrating-datacloud and what problem does it solve?

Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or dat

Who is it for?

Developers invoking orchestrating-datacloud as documented in the skill source.

Skip if: Skip when requirements fall outside orchestrating-datacloud documented scope.

When should I use this skill?

Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or dat

What you get

Outputs aligned with the orchestrating-datacloud SKILL.md workflow and stated deliverables.

  • Cross-phase pipeline architecture guidance
  • Data space and data kit management plan

Files

SKILL.mdMarkdownGitHub ↗

orchestrating-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 orchestrating-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:

PhaseUse this skillTypical scope
Connectconnecting-datacloudconnections, connectors, source discovery
Preparepreparing-dataclouddata streams, DLOs, transforms, DocAI
Harmonizeharmonizing-datacloudDMOs, mappings, identity resolution, data graphs
Segmentsegmenting-datacloudsegments, calculated insights
Actactivating-datacloudactivations, activation targets, data actions
Retrieveretrieving-datacloudSQL, search indexes, vector search, async query

Delegate outside the family when the user is:

  • extracting Session Tracing / STDM telemetry → observing-agentforce
  • writing CRM SOQL only → querying-soql
  • loading CRM source data → handling-sf-data
  • creating missing CRM schema → generating-custom-object or generating-custom-field
  • implementing downstream Apex or Flow logic → generating-apex, generating-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 ./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 ./scripts/diagnose-org.mjs -o <org> --json

Only use a query-plane probe after you know the table name is real:

node ./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: 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:

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

NeedDelegate toReason
load or clean CRM source datahandling-sf-dataseed or fix source records before ingestion
create missing CRM schemagenerating-custom-object, generating-custom-fieldData Cloud expects existing objects/fields
deploy permissions or bundlesdeploying-metadataenvironment preparation
write Apex against Data Cloud outputsgenerating-apexcode implementation
Flow automation after segmentation/activationgenerating-flowdeclarative orchestration
session tracing / STDM / parquet analysisobserving-agentforcedifferent Data Cloud use case

---

Reference Map

Start here

  • README.md
  • references/plugin-setup.md
  • references/feature-readiness.md
  • UPSTREAM.md

Phase skills

  • connecting-datacloud
  • preparing-datacloud
  • harmonizing-datacloud
  • segmenting-datacloud
  • activating-datacloud
  • retrieving-datacloud

Deterministic helpers

  • scripts/bootstrap-plugin.sh
  • scripts/verify-plugin.sh
  • scripts/diagnose-org.mjs
  • assets/definitions/

Related skills

Forks & variants (1)

Orchestrating Datacloud has 1 known copy in the catalog totaling 519 installs. They canonicalize to this original listing.

How it compares

Use orchestrating-datacloud for cross-phase Data Cloud pipeline design; switch to phase-specific sf-skills when work stays within a single Connect, Prepare, or Segment step.

FAQ

What is orchestrating-datacloud?

Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase tro

When should I use orchestrating-datacloud?

Salesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase tro

Is orchestrating-datacloud safe to install?

Review the Security Audits panel on this page before production use.

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