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Orchestrating Datacloud

  • 519 installs
  • 787 repo stars
  • Updated August 5, 2026
  • forcedotcom/afv-library

This is a copy of orchestrating-datacloud by forcedotcom - installs and ranking accrue to the original listing.

Orchestrating Data Cloud is a Salesforce Agentforce skill that guides multi-step Data Cloud pipelines across connect, prepare, harmonize, segment, and act workflows for developers who need cross-phase orchestration inste

About

Orchestrating Data Cloud is the product orchestrator skill in forcedotcom/afv-library for Salesforce Data Cloud. It walks developers through connect→prepare→harmonize→segment→act pipelines, cross-phase troubleshooting, and data space and data kit management using sf data360 workflows. Use Orchestrating Data Cloud when a task spans multiple Data Cloud phases rather than a single ingestion, mapping, or segment step. The skill defers isolated phase work to matching phase-specific skills and excludes STDM session tracing, standard CRM SOQL, and Apex-only tasks. It targets integrators building end-to-end customer data platforms inside Data Cloud-enabled orgs.

  • Orchestrates full connect→prepare→harmonize→segment→act Data Cloud workflows
  • Handles cross-phase troubleshooting and data space / data kit management
  • Decides correct phase for any Data Cloud task before execution
  • Follows sf-skills house style while calling external sf data360 CLI
  • Requires community sf data360 plugin and Data Cloud-enabled org

Orchestrating Datacloud by the numbers

  • 519 all-time installs (skills.sh)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/forcedotcom/afv-library --skill orchestrating-datacloud

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Listed on Skillselion
Installs519
repo stars787
Last updatedAugust 5, 2026
Repositoryforcedotcom/afv-library

How do you orchestrate multi-phase Salesforce Data Cloud pipelines?

Get guided orchestration across Salesforce Data Cloud pipelines instead of jumping between isolated commands.

Who is it for?

Salesforce developers running end-to-end Data Cloud implementations that cross ingestion, harmonization, segmentation, and activation phases.

Skip if: Developers with isolated single-phase tasks such as only creating a data stream or only writing a segment SQL query.

When should I use this skill?

The user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management across sf data360 workflows.

What you get

Cross-phase Data Cloud pipeline plans, data space configurations, data kit workflows, and coordinated sf data360 command sequences.

  • cross-phase pipeline plans
  • data space and data kit configurations

By the numbers

  • Spans five Data Cloud workflow phases: connect, prepare, harmonize, segment, and act

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

How it compares

Use Orchestrating Data Cloud for end-to-end pipeline coordination; switch to phase-specific skills when the task stays inside one Data Cloud phase.

FAQ

What phases does Orchestrating Data Cloud cover?

Orchestrating Data Cloud spans connect, prepare, harmonize, segment, and act workflows in Salesforce Data Cloud. It coordinates cross-phase sf data360 commands, data space setup, and data kit management rather than one isolated step.

When should I use a phase-specific Data Cloud skill instead?

Orchestrating Data Cloud is for multi-step pipelines and cross-phase troubleshooting. Use phase-specific skills when work stays in one area, such as only ingestion, only DMO mapping, or only segment SQL.

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