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Sf Datacloud Harmonize

  • 933 installs
  • 423 repo stars
  • Updated April 27, 2026
  • jaganpro/sf-skills

sf-datacloud-harmonize is a Salesforce Data Cloud skill that maps, cleans, and unifies mismatched schemas between Data Cloud objects and downstream systems for developers building unified customer data pipelines.

About

sf-datacloud-harmonize is a jaganpro/sf-skills package in the sf-datacloud family that automatically maps, cleans, and unifies mismatched schemas between Salesforce Data Cloud objects and downstream systems or AI agents. Shared attribution and upstream mapping live alongside sibling sf-datacloud skills in CREDITS.md and UPSTREAM.md. Developers reach for sf-datacloud-harmonize when Data Cloud ingests heterogeneous sources and field names, types, or entities conflict before activation or agent consumption. The skill targets harmonization logic rather than one-off SOQL queries, helping teams deliver consistent unified profiles and event objects across CRM, warehouse, and agent toolchains under MIT licensing.

  • Automates schema harmonization across Salesforce Data Cloud sources
  • Handles field mapping, type coercion, and conflict resolution
  • Produces unified canonical schemas ready for agent consumption
  • Part of the sf-datacloud family with shared upstream logic
  • MIT-licensed and designed for repeatable integration workflows

Sf Datacloud Harmonize by the numbers

  • 933 all-time installs (skills.sh)
  • +4 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #423 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/jaganpro/sf-skills --skill sf-datacloud-harmonize

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Listed on Skillselion
Installs933
repo stars423
Security audit2 / 3 scanners passed
Last updatedApril 27, 2026
Repositoryjaganpro/sf-skills

How do you harmonize Salesforce Data Cloud schemas?

Automatically map, clean, and unify mismatched schemas between Salesforce Data Cloud objects and downstream systems or AI agents.

Who is it for?

Salesforce developers integrating Data Cloud with warehouses, apps, or AI agents facing schema drift across sources.

Skip if: Teams without Salesforce Data Cloud or projects needing only standard CRM record CRUD.

When should I use this skill?

Data Cloud objects have conflicting schemas with downstream targets or agents need unified field mappings.

What you get

Unified Data Cloud object mappings, cleaned field alignments, and harmonized schemas for downstream systems.

  • Harmonized schema mappings
  • Unified Data Cloud object definitions

Files

SKILL.mdMarkdownGitHub ↗

sf-datacloud-harmonize: Data Cloud Harmonize Phase

Use this skill when the user needs schema harmonization and unification work: DMOs, field mappings, relationships, identity resolution, unified profiles, data graphs, or universal ID lookup.

When This Skill Owns the Task

Use sf-datacloud-harmonize when the work involves:

  • sf data360 dmo *
  • sf data360 identity-resolution *
  • sf data360 data-graph *
  • sf data360 profile *
  • sf data360 universal-id lookup

Delegate elsewhere when the user is:

  • still ingesting streams or building DLOs → sf-datacloud-prepare
  • working on segment logic or calculated insights → sf-datacloud-segment
  • running SQL, describe, or search-index workflows → sf-datacloud-retrieve

---

Required Context to Gather First

Ask for or infer:

  • source DLO and target DMO names
  • whether the task is schema creation, mapping, IR, or graph-related
  • target org alias
  • whether a ruleset already exists
  • the user’s desired unified entity model

---

Core Operating Rules

  • Inspect DMO schema before creating mappings.
  • Run the shared readiness classifier before mutating harmonization assets: node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase harmonize --json.
  • Prefer dmo list --all when browsing the catalog, but use first-page dmo list for fast readiness checks.
  • Use query describe or dmo get --json instead of inventing unsupported describe flows.
  • Treat identity resolution runs as asynchronous and verify results after execution.
  • Keep unified-profile work separate from STDM/session tracing work.

---

Recommended Workflow

1. Classify readiness for harmonize work

node ~/.claude/skills/sf-datacloud/scripts/diagnose-org.mjs -o <org> --phase harmonize --json

2. Inspect the catalog

sf data360 dmo list --all -o <org> 2>/dev/null
sf data360 identity-resolution list -o <org> 2>/dev/null

3. Inspect schema before mapping

sf data360 query describe -o <org> --table ssot__Individual__dlm 2>/dev/null
sf data360 dmo get -o <org> --name ssot__Individual__dlm --json 2>/dev/null

4. Create or review mappings intentionally

sf data360 dmo mapping-list -o <org> --source Contact_Home__dll --target ssot__Individual__dlm 2>/dev/null
sf data360 dmo map-to-canonical -o <org> --dlo Contact_Home__dll --dmo ssot__Individual__dlm --dry-run 2>/dev/null

5. Run IR only after mappings are trustworthy

sf data360 identity-resolution create -o <org> -f ir-ruleset.json 2>/dev/null
sf data360 identity-resolution run -o <org> --name Main 2>/dev/null

---

High-Signal Gotchas

  • dmo list should usually use --all.
  • Use query describe or dmo get --json; there is no dmo describe command.
  • Mapping and related commands can be sensitive to API-version differences.
  • Unified DMO names are ruleset-specific rather than generic.
  • Data graph definitions are sensitive to field selection and relationship shape.
  • If dmo list works but identity-resolution list is gated, treat that as a phase-specific gap rather than a full Data Cloud outage.

---

Output Format

Harmonize task: <dmo / mapping / relationship / ir / data-graph>
Source/target: <dlo → dmo or ruleset/graph names>
Target org: <alias>
Artifacts: <json files / commands>
Verification: <passed / partial / blocked>
Next step: <segment / retrieve / follow-up>

---

References

  • README.md
  • ../sf-datacloud/assets/definitions/dmo.template.json
  • ../sf-datacloud/assets/definitions/mapping.template.json
  • ../sf-datacloud/assets/definitions/relationship.template.json
  • ../sf-datacloud/assets/definitions/identity-resolution.template.json
  • ../sf-datacloud/assets/definitions/data-graph.template.json
  • ../sf-datacloud/references/feature-readiness.md

Related skills

How it compares

Choose sf-datacloud-harmonize over generic ETL skills when schema conflicts specifically involve Salesforce Data Cloud unified profiles.

FAQ

What does sf-datacloud-harmonize automate?

sf-datacloud-harmonize automates mapping, cleaning, and unification of mismatched schemas between Salesforce Data Cloud objects and downstream systems or AI agents.

How does sf-datacloud-harmonize relate to other sf-skills?

sf-datacloud-harmonize belongs to the sf-datacloud family in jaganpro/sf-skills, sharing attribution and upstream notes documented in sibling CREDITS.md and UPSTREAM.md files.

Is Sf Datacloud Harmonize safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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