Agent-Skills.md

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

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UncategorizedID: jaganpro/sf-skills/sf-industry-commoncore-datamapper

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jaganpro/sf-skills
JaganproLicense: MIT
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pnpm dlx add-skill https://github.com/Jaganpro/sf-skills/tree/HEAD/skills/sf-industry-commoncore-datamapper

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skills/sf-industry-commoncore-datamapper/SKILL.md

Skill Metadata

Name
sf-industry-commoncore-datamapper
Description
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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

| 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:

  1. Check existing Data Mappers: Glob: **/OmniDataTransform*
  2. Check existing OmniStudio metadata: Glob: **/omnistudio/**
  3. 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 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)"

| 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:

  • [ ] 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

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)