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data360-harmonize

data360-harmonize

github.com/forcedotcomData & databases1.1k3.8k installsUpdated 1d ago
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About data360-harmonize

data360-harmonize is a Salesforce Data Cloud skill for aligning schemas and unifying data through DMOs, mappings, identity resolution, and data graphs. It helps operators inspect schemas and mappings before running identity-resolution jobs.

Key features

  1. 01Inspect DMO schemas and catalogs
  2. 02Review DLO-to-DMO mappings
  3. 03Create and run identity-resolution rulesets
  4. 04Work with data graphs and unified profiles
  5. 05Check readiness before changes

Use cases

  1. 01Map an ingested DLO to a target DMO
  2. 02Review an existing identity-resolution ruleset
  3. 03Prepare unified profiles from mapped data

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/forcedotcom/sf-skills --skill data360-harmonize

After installing, type/data360-harmonizein Claude Code to confirm it loaded.

Bundled filesREADME.md

Details

From the project README

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:

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

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