| name | sf-industry-commoncore-datamapper |
| description | OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-industry-commoncore-integration-procedure), authoring OmniScripts (use sf-industry-commoncore-omniscript), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
|
| license | MIT |
| metadata | {"version":"1.0.0","author":"David Ryan (weytani)","scoring":"100 points across 5 categories"} |
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
- Generation: Create Data Mapper configurations (Extract, Transform, Load, Turbo Extract) from requirements
- Field Mapping: Design object-to-output field mappings with proper type handling, lookup resolution, and null safety
- Dependency Tracking: Identify related OmniStudio components (Integration Procedures, OmniScripts, FlexCards) that consume or feed Data Mappers
- 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
| 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. |
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:
- Check existing Data Mappers:
Glob: **/OmniDataTransform*
- Check existing OmniStudio metadata:
Glob: **/omnistudio/**
- Create a task list
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 ContactsDR_TurboExtract_Case_List -- High-volume Case list for FlexCardDR_Transform_Lead_Flatten -- Flatten nested Lead data structureDR_Load_Opportunity_Create -- Insert Opportunity records
Phase 3: Generation & Validation
For Generation:
- Define the OmniDataTransform record (Name, Type, Active status)
- Define OmniDataTransformItem records (field mappings, input/output paths)
- Configure query filters, sort order, and limits for Extract types
- Set up lookup mappings and default values for Load types
- Validate field-level security for all mapped fields
For Review:
- Read existing Data Mapper configuration
- Run validation against best practices
- 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)"
| 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
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.
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:
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)
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>
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" |
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 |
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.
License
MIT License.
Copyright (c) 2026 David Ryan (weytani)
