
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)
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| Installs | 933 |
|---|---|
| repo stars | ★ 423 |
| Security audit | 2 / 3 scanners passed |
| Last updated | April 27, 2026 |
| Repository | jaganpro/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
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 --allwhen browsing the catalog, but use first-pagedmo listfor fast readiness checks. - Use
query describeordmo get --jsoninstead 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 --json2. Inspect the catalog
sf data360 dmo list --all -o <org> 2>/dev/null
sf data360 identity-resolution list -o <org> 2>/dev/null3. 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/null4. 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/null5. 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 listshould usually use--all.- Use
query describeordmo get --json; there is nodmo describecommand. - 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 listworks butidentity-resolution listis 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
Credits & Acknowledgments
Primary contributor: Gnanasekaran Thoppae
This skill is part of the sf-datacloud-* family. Shared attribution, upstream source mapping, and maintenance notes live in:
- ../sf-datacloud/CREDITS.md
- ../sf-datacloud/UPSTREAM.md
MIT License
Copyright (c) 2024-2025 Jag Valaiyapathy
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
sf-datacloud-harmonize
Schema harmonization and unification workflows for Salesforce Data Cloud.
Use this skill for
- DMOs
- field mappings
- relationships
- identity resolution
- unified profiles
- data graphs
- universal ID lookup
Example requests
"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
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/nullReferences
- SKILL.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/data-graph.template.json
- CREDITS.md
License
MIT License - See LICENSE.
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.