Harmonizing Datacloud

# Credits & Acknowledgments

This skill is part of the `*-datacloud` family of skills covering the Data Cloud lifecycle: ingestion, harmonization, segmentation, and retrieval.


# harmonizing-datacloud

Schema harmonization and unification workflows for Salesforce Data Cloud.

## Use this skill for

- DMOs (Data Model Objects)
- Field mappings
- Relationships
- Identity resolution
- Unified profiles
- Data graphs
- Universal ID lookup

## Example requests

```text
"Map this DLO to ssot__Individual__dlm"
"Help me create an identity resolution ruleset"
"Why are unified profiles not appearing?"
"Show me the DMO fields before I create mappings"
```

## Common commands

```bash
sf data360 dmo list --all -o myorg 2>/dev/null
sf data360 query describe -o myorg --table ssot__Individual__dlm 2>/dev/null
sf data360 dmo mapping-list -o myorg --source Contact_Home__dll --target ssot__Individual__dlm 2>/dev/null
sf data360 identity-resolution list -o myorg 2>/dev/null
```


---
name: harmonizing-datacloud
description: "Salesforce Data Cloud Harmonize phase. Use this skill when the user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use preparing-datacloud), segments/insights (use segmenting-datacloud), retrieval/search (use retrieving-datacloud), or STDM/session tracing (use observing-agentforce)."
license: MIT
compatibility: "Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org"
metadata:
  version: "1.0"
---

# harmonizing-datacloud: 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 `harmonizing-datacloud` 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 → [preparing-datacloud](../preparing-datacloud/SKILL.md)
- working on segment logic or calculated insights → [segmenting-datacloud](../segmenting-datacloud/SKILL.md)
- running SQL, describe, or search-index workflows → [retrieving-datacloud](../retrieving-datacloud/SKILL.md)

---

## 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/orchestrating-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
```bash
node ~/.claude/skills/orchestrating-datacloud/scripts/diagnose-org.mjs -o <org> --phase harmonize --json
```

### 2. Inspect the catalog
```bash
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
```bash
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 inten