What types of tasks can I perform with this skill?

You can perform schema harmonization, create mappings, manage relationships, and conduct identity resolution tasks.

When should I not use this skill?

Avoid using this skill for tasks related to data streams, segments, or SQL queries; use the appropriate skills for those scenarios.

How do I check the readiness of my data for harmonization?

You can check readiness by running the command: `node ../data360-orchestrate/scripts/diagnose-org.mjs -o --phase harmonize --json`.

What is the importance of inspecting DMO schemas?

Inspecting DMO schemas is crucial to ensure that mappings are created accurately and to avoid integration errors.

Can this skill help with data graph creation?

Yes, this skill includes functionalities for managing data graphs as part of the harmonization process.

Data Cloud Harmonize

Name: Data Cloud Harmonize
Availability: InStock
Author: forcedotcom

OfficialFree

Streamline schema harmonization and unification tasks.

by forcedotcom808 stars on forcedotcom/sf-skills

Updated Aug 10, 2026

Get this skill

Free · Opens the source repo

What Data Cloud Harmonize does

The Data Cloud Harmonize skill is designed to assist users in the schema harmonization and unification phase of data management within Salesforce Data Cloud. This skill is particularly useful for those working with Data Management Objects (DMOs), field mappings, relationships, identity resolution, unified profiles, data graphs, and universal ID lookups. By leveraging this skill, users can ensure that their data is accurately aligned and integrated across various sources, which is critical for effective data analysis and reporting.

When utilizing the Data Cloud Harmonize skill, users are guided through a structured workflow that begins with assessing the readiness of their data environment for harmonization tasks. This includes inspecting DMOs and schemas before creating mappings, which helps prevent errors and ensures that the data integration process is efficient. The skill provides specific commands for listing DMOs, inspecting schemas, and creating or reviewing mappings, all of which are essential steps in the harmonization process.

This skill is particularly beneficial for data engineers, data analysts, and Salesforce administrators who need to manage complex data relationships and ensure that their data models are unified and consistent. By automating various aspects of the harmonization process, the Data Cloud Harmonize skill allows users to focus on higher-level data strategy and decision-making rather than getting bogged down in manual data integration tasks.

However, users should be aware that this skill is not intended for tasks related to data streams, segment logic, or SQL-based retrievals. For those scenarios, other skills such as data360-prepare, data360-segment, and data360-query should be used instead. Understanding the specific use cases for the Data Cloud Harmonize skill will help users maximize its effectiveness in their data management workflows.

When to use it

Use this skill when working on tasks involving DMOs, identity resolution, or data graphs within Salesforce Data Cloud.

When not to use it

Do not use this skill for tasks focused solely on data streams, segments, or SQL queries; other skills are better suited for those tasks.

What you can build with it

Preparing for Schema Harmonization

Use the skill to assess the readiness of your data environment before starting harmonization tasks.

Creating Mappings

Leverage the skill to create and review mappings between different data objects efficiently.

Managing Identity Resolution

Utilize the skill to run identity resolution processes after ensuring mappings are trustworthy.

How to install Data Cloud Harmonize

View source

1. Install with the skills CLI

npx skills add forcedotcom/sf-skills/data360-harmonize --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by forcedotcom

data360-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 data360-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 → data360-prepare
working on segment logic or calculated insights → data360-segment
running SQL, describe, or search-index workflows → data360-query

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 ../data360-orchestrate/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 ../data360-orchestrate/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

Frequently asked questions about Data Cloud Harmonize

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