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Sf-skills sf-datacloud-harmonize

install
source · Clone the upstream repo
git clone https://github.com/Jaganpro/sf-skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/Jaganpro/sf-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/sf-datacloud-harmonize" ~/.claude/skills/jaganpro-sf-skills-sf-datacloud-harmonize && rm -rf "$T"
manifest: skills/sf-datacloud-harmonize/SKILL.md
source content

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:

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