sf-industry-commoncore-integration-procedure

name	sf-industry-commoncore-integration-procedure
description	OmniStudio Integration Procedure creation and validation with 110-point scoring. Use when building server-side process orchestrations that combine Data Mapper actions, Apex Remote Actions, HTTP callouts, and conditional logic. TRIGGER when: user creates Integration Procedures, adds Data Mapper steps, configures Remote Actions, or reviews existing IP configurations. DO NOT TRIGGER when: building OmniScripts (use sf-industry-commoncore-omniscript), creating Data Mappers directly (use sf-industry-commoncore-datamapper), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
license	MIT
metadata	{"version":"1.0.0","author":"David Ryan (weytani)","scoring":"110 points across 6 categories"}

sf-industry-commoncore-integration-procedure: OmniStudio Integration Procedure Creation and Validation

Expert OmniStudio Integration Procedure (IP) builder with deep knowledge of server-side process orchestration. Create production-ready IPs that combine DataRaptor/Data Mapper actions, Apex Remote Actions, HTTP callouts, conditional logic, and nested procedure calls into declarative multi-step operations.

Quick Reference

Scoring: 110 points across 6 categories. Thresholds: ✅ 90+ (Deploy) | ⚠️ 67-89 (Review) | ❌ <67 (Block - fix required)

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Core Responsibilities

1. IP Generation: Create well-structured Integration Procedures from requirements, selecting correct element types and wiring inputs/outputs
2. Element Composition: Assemble DataRaptor actions, Remote Actions, HTTP callouts, conditional blocks, loops, and nested IP calls into coherent orchestrations
3. Dependency Analysis: Validate that referenced DataRaptors, Apex classes, and nested IPs exist and are active before deployment
4. Error Handling: Enforce try/catch patterns, conditional rollback, and response validation across all data-modifying steps

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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-integration-procedure)

Data Mappers referenced by the IP must exist FIRST. Build and deploy DataRaptors/Data Mappers before the IP that calls them. The IP must be active before any OmniScript or FlexCard can invoke it.

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Key Insights

Insight	Details
Chaining	IPs call other IPs via Integration Procedure Action elements. Output of one step feeds input of the next via response mapping. Design data flow linearly where possible.
Response Mapping	Each element's output is namespaced under its element name in the response JSON. Use %elementName:keyPath% syntax to reference upstream outputs in downstream inputs.
Caching	IPs support platform cache for read-heavy orchestrations. Set cacheType and cacheTTL in the procedure's PropertySet. Avoid caching procedures that perform DML.
Versioning	Type/SubType pairs uniquely identify an IP. Use SubType for versioning (e.g., Type=AccountOnboarding, SubType=v2). Only one version can be active at a time per Type/SubType.

Core Namespace Discriminator: OmniStudio Core stores both Integration Procedures and OmniScripts in the OmniProcess table. Use IsIntegrationProcedure = true or OmniProcessType = 'Integration Procedure' to filter IPs. Without a filter, queries return mixed results.

CRITICAL — Creating IPs via Data API: When creating OmniProcess records, set IsIntegrationProcedure = true to make the record an Integration Procedure. The OmniProcessType picklist is computed from this boolean and cannot be set directly. Also, Name is a required field on OmniProcess (not documented in standard OmniStudio docs). Use sf api request rest --method POST --body @file.json for creation — the sf data create record --values flag cannot handle JSON textarea fields like PropertySetConfig.

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Workflow Design (5-Phase Pattern)

Phase 1: Requirements Gathering

Before building, evaluate alternatives: Sometimes a single DataRaptor, an Apex service, or a Flow is the better choice. IPs are optimal when you need declarative multi-step orchestration with branching, error handling, and mixed data sources.

Ask the user to gather:

• Purpose and business process being orchestrated
• Target objects and data sources (Salesforce objects, external APIs, or both)
• Type/SubType naming (e.g., Type=OrderProcessing, SubType=Standard)
• Target org alias for deployment

Then: Check existing IPs via CLI query (see CLI Commands below), identify reusable DataRaptors/Data Mappers, and review dependent components with sf-industry-commoncore-omnistudio-analyze.

Phase 2: Design & Element Selection

Element Type	Use Case	PropertySet Key
DataRaptor Extract Action	Read Salesforce data	bundle
DataRaptor Load Action	Write Salesforce data	bundle
DataRaptor Transform Action	Data shaping/mapping	bundle
Remote Action	Call Apex class method	remoteClass, remoteMethod
Integration Procedure Action	Call nested IP	ipMethod (format: Type_SubType)
HTTP Action	External API callout	path, method
Conditional Block	Branching logic	--
Loop Block	Iterate over collections	--
Set Values	Assign variables/constants	--

Naming Convention: [Type]_[SubType] using PascalCase. Element names within the IP should describe their action clearly (e.g., GetAccountDetails, ValidateInput, CreateOrderRecord).

Data Flow: Design the element chain so each step's output feeds naturally into the next step's input. Map outputs explicitly rather than relying on implicit namespace merging.

Phase 3: Generation & Validation

Build the IP definition with:

• Correct Type/SubType assignment
• Ordered element chain with explicit input/output mappings
• Error handling on all data-modifying elements
• Conditional blocks for branching logic

Validation (STRICT MODE):

• BLOCK: Missing Type/SubType, circular IP calls, DML without error handling, references to nonexistent DataRaptors/Apex classes
• WARN: Unbounded extracts without LIMIT, missing caching on read-only IPs, hardcoded IDs in PropertySetConfig, unused elements, missing element descriptions

Validation Report Format (6-Category Scoring 0-110):

Score: 95/110  Very Good
|- Design & Structure: 18/20 (90%)
|- Data Operations: 23/25 (92%)
|- Error Handling: 18/20 (90%)
|- Performance: 18/20 (90%)
|- Security: 13/15 (87%)
|- Documentation: 5/10 (50%)

Generation Guardrails (MANDATORY)

Anti-Pattern	Impact	Correct Pattern
Circular IP calls (A calls B calls A)	Infinite loop / stack overflow	Map dependency graph; no cycles allowed
DML without error handling	Silent data corruption	Wrap DataRaptor Load in try/catch or conditional error check
Unbounded DataRaptor Extract	Governor limits / timeout	Set LIMIT on extracts; paginate large datasets
Hardcoded Salesforce IDs in PropertySetConfig	Deployment failure across orgs	Use input variables, Custom Settings, or Custom Metadata
Sequential calls that could be parallel	Unnecessary latency	Group independent elements; no serial dependency needed
Missing response validation	Downstream null reference errors	Check element response before passing to next step

DO NOT generate anti-patterns even if explicitly requested.

Phase 4: Deployment

1. Deploy prerequisite DataRaptors/Data Mappers FIRST using sf-deploy
2. Deploy the Integration Procedure: sf project deploy start -m OmniIntegrationProcedure:<Name> -o <org>
3. Activate the IP in the target org (set IsActive=true)
4. Verify activation via CLI query

Phase 5: Testing

Test each element individually before testing the full chain:

1. Unit: Invoke each DataRaptor independently, verify Apex Remote Action responses
2. Integration: Run the full IP with representative input JSON, verify output structure
3. Error paths: Test with invalid input, missing records, API failures to verify error handling
4. Bulk: Test with collection inputs to verify loop and batch behavior
5. End-to-end: Invoke the IP from its consumer (OmniScript, FlexCard, or API) and verify the full round-trip

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Scoring Breakdown

110 points across 6 categories:

Design & Structure (20 points)

Criterion	Points	Description
Type/SubType naming	5	Follows convention, descriptive, versioned appropriately
Element naming	5	Clear, action-oriented names on all elements
Data flow clarity	5	Linear or well-documented branching; explicit input/output mapping
Element ordering	5	Logical execution sequence; no unnecessary dependencies

Data Operations (25 points)

Criterion	Points	Description
DataRaptor references valid	5	All referenced bundles exist and are active
Extract operations bounded	5	LIMIT set on all extracts; pagination for large datasets
Load operations validated	5	Input data validated before DML; required fields checked
Response mapping correct	5	Outputs correctly mapped between elements
Data transformation accuracy	5	Transform actions produce expected output structure

Error Handling (20 points)

Criterion	Points	Description
DML error handling	8	All DataRaptor Load actions have error handling
HTTP error handling	4	All HTTP actions check status codes and handle failures
Remote Action error handling	4	Apex exceptions caught and surfaced
Rollback strategy	4	Multi-step DML has conditional rollback or compensating actions

Performance (20 points)

Criterion	Points	Description
No unbounded queries	5	All extracts have reasonable LIMIT values
Caching applied	5	Read-only procedures use platform cache where appropriate
Parallel execution	5	Independent elements not serialized unnecessarily
No redundant calls	5	Same data not fetched multiple times across elements

Security (15 points)

Criterion	Points	Description
No hardcoded IDs	5	IDs passed as input variables or from metadata
No hardcoded credentials	5	API keys/tokens use Named Credentials or Custom Settings
Input validation	5	User-supplied input sanitized before use in queries or DML

Documentation (10 points)

Criterion	Points	Description
Procedure description	3	Clear description of purpose and business context
Element descriptions	4	Each element has a description explaining its role
Input/output documentation	3	Expected input JSON and output JSON structure documented

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CLI Commands

# Query active Integration Procedures
sf data query -q "SELECT Id,Name,Type,SubType,IsActive FROM OmniProcess WHERE IsActive=true AND IsIntegrationProcedure=true" -o <org>

# Query all Integration Procedures (including inactive)
sf data query -q "SELECT Id,Name,Type,SubType,IsActive,LastModifiedDate FROM OmniProcess WHERE IsIntegrationProcedure=true ORDER BY LastModifiedDate DESC" -o <org>

# Retrieve an Integration Procedure
sf project retrieve start -m OmniIntegrationProcedure:<Name> -o <org>

# Deploy an Integration Procedure
sf project deploy start -m OmniIntegrationProcedure:<Name> -o <org>

# Deploy with dry-run validation first
sf project deploy start -m OmniIntegrationProcedure:<Name> -o <org> --dry-run

Core Namespace Note: The IsIntegrationProcedure=true filter is REQUIRED (or equivalently OmniProcessType='Integration Procedure'). OmniScript and Integration Procedure records share the OmniProcess sObject. Without this filter, queries return both types and produce misleading results.

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Cross-Skill Integration

From Skill	To sf-industry-commoncore-integration-procedure	When
sf-industry-commoncore-omnistudio-analyze	-> sf-industry-commoncore-integration-procedure	"Analyze dependencies before building IP"
sf-industry-commoncore-datamapper	-> sf-industry-commoncore-integration-procedure	"DataRaptor/Data Mapper is ready, wire it into IP"
sf-apex	-> sf-industry-commoncore-integration-procedure	"Apex Remote Action class deployed, configure in IP"

From sf-industry-commoncore-integration-procedure	To Skill	When
sf-industry-commoncore-integration-procedure	-> sf-deploy	"Deploy IP to target org"
sf-industry-commoncore-integration-procedure	-> sf-industry-commoncore-omniscript	"IP is active, build OmniScript that calls it"
sf-industry-commoncore-integration-procedure	-> sf-industry-commoncore-flexcard	"IP is active, build FlexCard data source"
sf-industry-commoncore-integration-procedure	-> sf-industry-commoncore-omnistudio-analyze	"Verify IP dependency graph before deployment"

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Edge Cases

Scenario	Solution
IP calls itself (direct recursion)	Block at design time; circular dependency check is mandatory
IP calls IP that calls original (indirect recursion)	Map full call graph; sf-industry-commoncore-omnistudio-analyze detects cycles
DataRaptor not yet deployed	Deploy DataRaptors first; IP deployment will fail on missing references
External API timeout	Set timeout values on HTTP Action elements; implement retry logic or graceful degradation
Large collection input to Loop Block	Set batch size; test with realistic data volumes to avoid CPU timeout
Type/SubType collision with existing IP	Query existing IPs before creating; SubType versioning avoids collisions
Mixed namespace (Vlocity vs Core)	Confirm org namespace; element property names differ between packages

Debug: IP not executing -> check IsActive flag + Type/SubType match | Elements skipped -> verify conditional block logic + input data shape | Timeout -> check DataRaptor query scope + HTTP timeout settings | Deployment failure -> verify all referenced components deployed and active

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Notes

Dependencies (optional): sf-deploy, sf-industry-commoncore-datamapper, sf-industry-commoncore-omnistudio-analyze | API: 66.0 | Mode: Strict (warnings block) | Scoring: Block deployment if score < 67 | See references/best-practices.md and references/element-types.md for detailed guidance.

Creating IPs programmatically: Use REST API (sf api request rest --method POST --body @file.json). Required fields: Name, Type, SubType, Language, VersionNumber, IsIntegrationProcedure=true. Then create OmniProcessElement child records for each action step (also via REST API for JSON PropertySetConfig). Activate by setting IsActive=true after all elements are created.

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License

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