From jaganpro-sf-skills-7
Creates and validates OmniStudio Data Mappers for Extract, Transform, Load, Turbo Extract with Salesforce field mappings and 100-point scoring.
npx claudepluginhub jaganpro/sf-skillsThis skill uses the workspace's default tool permissions.
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.
Analyzes OmniStudio components (OmniScripts, FlexCards, Integration Procedures, Data Mappers) for namespace detection (Core/vlocity_cmt/vlocity_ins), dependencies, impact, and Mermaid diagrams.
Generates and validates SQLScript/Python transformation logic for SAP Datasphere Data/Transformation Flows. Use for complex transformations, delta logic, SCD Type 2, and performance optimization.
Guides Salesforce data migrations using Bulk API 2.0, jsforce ETL, Data Loader for org-to-org transfers, CRM imports, and validation with TypeScript examples.
Share bugs, ideas, or general feedback.
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.
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.
| 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. |
Ask the user to gather:
Then:
Glob: **/OmniDataTransform*Glob: **/omnistudio/**| 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 recordsFor Generation:
For Review:
Run Validation:
Score: XX/100 Rating
|- Design & Naming: XX/20
|- Field Mapping: XX/25
|- Data Integrity: XX/25
|- Performance: XX/15
|- Documentation: XX/15
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
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.
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:
| 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)
sf data query -q "SELECT Id,Name,Type FROM OmniDataTransform" -o <org>
sf data query -q "SELECT Id,Name,InputObjectName,OutputObjectName,LookupObjectName FROM OmniDataTransformItem WHERE OmniDataTransformationId='<id>'" -o <org>
sf project retrieve start -m OmniDataTransform:<Name> -o <org>
sf project deploy start -m OmniDataTransform:<Name> -o <org>
| 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" |
| 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 |
sf project retrieve start -m OmniDataTransform:<Name> only works for active Data Mappers. Draft DMs return "Entity cannot be found".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.OmniDataTransformItem is OmniDataTransformationId (full word "Transformation"), not OmniDataTransformId.MIT License. Copyright (c) 2026 David Ryan (weytani)