sf-industry-commoncore-integration-procedure

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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.

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.

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

• Deploy prerequisite DataRaptors/Data Mappers FIRST using sf-deploy

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

• Activate the IP in the target org (set IsActive=true)

• Verify activation via CLI query

Phase 5: Testing

Test each element individually before testing the full chain:

• Unit: Invoke each DataRaptor independently, verify Apex Remote Action responses

• Integration: Run the full IP with representative input JSON, verify output structure

• Error paths: Test with invalid input, missing records, API failures to verify error handling

• Bulk: Test with collection inputs to verify loop and batch behavior

• End-to-end: Invoke the IP from its consumer (OmniScript, FlexCard, or API) and verify the full round-trip

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

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.

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"

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

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.

License

MIT License.

Copyright (c) 2026 David Ryan (weytani)