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)

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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.

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Key Insights

Insight	Details
Extract vs Turbo Extract	Extract uses standard SOQL with relationship queries. Turbo Extract uses server-side compiled queries for read-heavy, high-volume scenarios (10x+ faster). Turbo Extract does not support formula fields, related lists, or write operations.
Transform is in-memory	Transform Data Mappers operate entirely in memory with no DML or SOQL. They reshape data structures between steps in an Integration Procedure. Use for JSON-to-JSON transformations, field renaming, and data flattening.
Load = DML	Load Data Mappers perform insert, update, upsert, or delete operations. They require proper FLS checks and error handling. Always validate field-level security before deploying Load Data Mappers to production.
OmniDataTransform metadata	Data Mappers are stored as OmniDataTransform and OmniDataTransformItem records. Retrieve and deploy using these metadata type names, not the legacy DataRaptor API names.

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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

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Phase 2: Design & Type Selection

Type	Use Case	Naming Prefix	Supports DML	Supports SOQL
Extract	Read data from one or more objects with relationship queries	DR_Extract_	No	Yes
Turbo Extract	High-volume read-only queries, server-side compiled	DR_TurboExtract_	No	Yes (compiled)
Transform	In-memory data reshaping between procedure steps	DR_Transform_	No	No
Load	Write data (insert, update, upsert, delete)	DR_Load_	Yes	No

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

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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

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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)"

Anti-Pattern	Detection	Impact
Extracting all fields	No field list specified, wildcard selection	Performance degradation, excessive data transfer
Missing lookup mappings	Load references lookup field without resolution	DML failure, null foreign key
Writing without FLS check	Load Data Mapper with no security validation	Security violation, data corruption in restricted profiles
Unbounded Extract query	No LIMIT or filter on Extract	Governor limit failure, timeout on large objects
Transform with side effects	Transform attempting DML or callout	Runtime error, Transform is in-memory only
Hardcoded record IDs	15/18-char ID literal in filter or mapping	Deployment failure across environments
Nested relationship depth >3	Extract with deeply nested parent traversal	Query performance degradation, SOQL complexity limits
Load without error handling	No upsert key or duplicate rule consideration	Silent data corruption, duplicate records

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

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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.

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Phase 5: Testing & Documentation

Completion Summary:

Data Mapper Complete: [Name]
  Type: [Extract|Transform|Load|Turbo Extract]
  Target Object(s): [Object1, Object2]
  Field Count: [N mapped fields]
  Validation: PASSED (Score: XX/100)

Next Steps: Test in Integration Procedure, verify data output, monitor performance

Testing Checklist:

• Preview data output in OmniStudio Designer
• Verify field mappings produce expected JSON structure
• Test with representative data volume (not just 1 record)
• Validate FLS enforcement with restricted profile user
• Confirm consuming Integration Procedure/OmniScript receives correct data shape

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Best Practices (100-Point Scoring)

Category	Points	Key Rules
Design & Naming	20	Correct type selection; naming follows DR_[Type]_[Object]_[Purpose] convention; single responsibility per Data Mapper
Field Mapping	25	Explicit field list (no wildcards); correct input/output paths; proper type conversions; null-safe default values
Data Integrity	25	FLS validation on all fields; lookup resolution for Load types; upsert keys defined; duplicate handling configured
Performance	15	Bounded queries with LIMIT/filters; Turbo Extract for read-heavy scenarios; minimal relationship depth; indexed filter fields
Documentation	15	Description on OmniDataTransform record; field mapping rationale documented; consuming components identified

Thresholds: ✅ 90+ (Deploy) | ⚠️ 67-89 (Review) | ❌ <67 (Block - fix required)

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CLI Commands

Query Existing Data Mappers

sf data query -q "SELECT Id,Name,Type FROM OmniDataTransform" -o <org>

Query Data Mapper Field Mappings

sf data query -q "SELECT Id,Name,InputObjectName,OutputObjectName,LookupObjectName FROM OmniDataTransformItem WHERE OmniDataTransformationId='<id>'" -o <org>

Retrieve Data Mapper Metadata

sf project retrieve start -m OmniDataTransform:<Name> -o <org>

Deploy Data Mapper Metadata

sf project deploy start -m OmniDataTransform:<Name> -o <org>

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Cross-Skill Integration

From Skill	To sf-industry-commoncore-datamapper	When
sf-industry-commoncore-omnistudio-analyze	-> sf-industry-commoncore-datamapper	"Analyze dependencies before creating Data Mapper"
sf-metadata	-> sf-industry-commoncore-datamapper	"Describe target object fields before mapping"
sf-soql	-> sf-industry-commoncore-datamapper	"Validate Extract query logic"

From sf-industry-commoncore-datamapper	To Skill	When
sf-industry-commoncore-datamapper	-> sf-industry-commoncore-integration-procedure	"Create Integration Procedure that calls this Data Mapper"
sf-industry-commoncore-datamapper	-> sf-deploy	"Deploy Data Mapper to target org"
sf-industry-commoncore-datamapper	-> sf-industry-commoncore-omniscript	"Wire Data Mapper output into OmniScript"
sf-industry-commoncore-datamapper	-> sf-industry-commoncore-flexcard	"Display Data Mapper Extract results in FlexCard"

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Edge Cases

Scenario	Solution
Large data volume (>10K records)	Use Turbo Extract; add pagination via Integration Procedure; warn about heap limits
Polymorphic lookup fields	Specify the concrete object type in the mapping; test each type separately
Formula fields in Extract	Standard Extract supports formula fields; Turbo Extract does not -- fall back to standard Extract
Cross-object Load (master-detail)	Insert parent records first, then child records in a separate Load step; use Integration Procedure to orchestrate sequence
Namespace-prefixed fields	Include namespace prefix in field paths (e.g., ns__Field__c); verify prefix matches target org
Multi-currency orgs	Map CurrencyIsoCode explicitly; do not rely on default currency assumption
RecordType-dependent mappings	Filter by RecordType in Extract; set RecordTypeId in Load; document which RecordTypes are supported

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Notes

• Metadata Type: OmniDataTransform (not DataRaptor -- legacy name deprecated)
• API Version: Requires OmniStudio managed package or Industries Cloud
• Scoring: Block deployment if score < 67
• Dependencies (optional): sf-deploy, sf-metadata, sf-industry-commoncore-omnistudio-analyze, sf-industry-commoncore-integration-procedure
• Turbo Extract Limitations: No formula fields, no related lists, no aggregate queries, no polymorphic fields
• Activation: Data Mappers must be activated after deployment to be callable from Integration Procedures
• Draft DMs can't be retrieved: sf project retrieve start -m OmniDataTransform:<Name> only works for active Data Mappers. Draft DMs return "Entity cannot be found".
• Creating via Data API: Use sf api request rest --method POST --body @file.json to create OmniDataTransform and OmniDataTransformItem records. The sf data create record --values flag cannot handle JSON in textarea fields. Write the JSON body to a temp file first.
• Foreign key field name: The parent lookup on OmniDataTransformItem is OmniDataTransformationId (full word "Transformation"), not OmniDataTransformId.

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License

MIT License. Copyright (c) 2026 David Ryan (weytani)