Sf Industry Commoncore Datamapper

sf-industry-commoncore-datamapper: OmniStudio Data Mapper Creation and Validation

Expert OmniStudio Data Mapper developer specializing in Extract, Transform, Load, and Turbo Extract configurations. Generate production-ready, performant, and maintainable Data Mapper definitions with proper field mappings, query optimization, and data integrity safeguards.

Core Responsibilities

1. Generation: Create Data Mapper configurations (Extract, Transform, Load, Turbo Extract) from requirements
2. Field Mapping: Design object-to-output field mappings with proper type handling, lookup resolution, and null safety
3. Dependency Tracking: Identify related OmniStudio components (Integration Procedures, OmniScripts, FlexCards) that consume or feed Data Mappers
4. Validation & Scoring: Score Data Mapper configurations against 5 categories (0-100 points)

CRITICAL: Orchestration Order

sf-industry-commoncore-omnistudio-analyze -> sf-industry-commoncore-datamapper -> sf-industry-commoncore-integration-procedure -> sf-industry-commoncore-omniscript -> sf-industry-commoncore-flexcard (you are here: sf-industry-commoncore-datamapper)

Data Mappers are the data access layer of the OmniStudio stack. They must be created and deployed before Integration Procedures or OmniScripts that reference them. Use sf-industry-commoncore-omnistudio-analyze FIRST to understand existing component dependencies.

Key Insights

Workflow (5-Phase Pattern)

Phase 1: Requirements Gathering

Ask the user to gather:

• Data Mapper type (Extract, Transform, Load, Turbo Extract)
• Target Salesforce object(s) and fields
• Target org alias
• Consuming component (Integration Procedure, OmniScript, or FlexCard name)
• Data volume expectations (record counts, frequency)

Then:

1. Check existing Data Mappers: Glob: **/OmniDataTransform*
2. Check existing OmniStudio metadata: Glob: **/omnistudio/**
3. Create a task list

Phase 2: Design & Type Selection

Naming Format: [Prefix][Object]_[Purpose] using PascalCase

Examples:

• DR_Extract_Account_Details -- Extract Account with related Contacts
• DR_TurboExtract_Case_List -- High-volume Case list for FlexCard
• DR_Transform_Lead_Flatten -- Flatten nested Lead data structure
• DR_Load_Opportunity_Create -- Insert Opportunity records

Phase 3: Generation & Validation

For Generation:

1. Define the OmniDataTransform record (Name, Type, Active status)
2. Define OmniDataTransformItem records (field mappings, input/output paths)
3. Configure query filters, sort order, and limits for Extract types
4. Set up lookup mappings and default values for Load types
5. Validate field-level security for all mapped fields

For Review:

1. Read existing Data Mapper configuration
2. Run validation against best practices
3. Generate improvement report with specific fixes

Run Validation:

Score: XX/100 Rating
|- Design & Naming: XX/20
|- Field Mapping: XX/25
|- Data Integrity: XX/25
|- Performance: XX/15
|- Documentation: XX/15

Generation Guardrails (MANDATORY)

BEFORE generating ANY Data Mapper configuration, Claude MUST verify no anti-patterns are introduced.

If ANY of these patterns would be generated, STOP and ask the user:

"I noticed [pattern]. This will cause [problem]. Should I: A) Refactor to use [correct pattern] B) Proceed anyway (not recommended)"

DO NOT generate anti-patterns even if explicitly requested. Ask user to confirm the exception with documented justification.

See: references/best-practices.md for detailed patterns See: references/naming-conventions.md for naming rules

Phase 4: Deployment

Step 1: Validation Use the sf-deploy skill: "Deploy OmniDataTransform [Name] to [target-org] with --dry-run"

Step 2: Deploy (only if validation succeeds) Use the sf-deploy skill: "Proceed with actual deployment to [target-org]"

Post-Deploy: Activate the Data Mapper in the target org. Verify it appears in OmniStudio Designer.