Is the matching logic configurable?

Yes, users can configure fuzzy thresholds, enable or disable domain-based matching, and toggle cross-object deduplication settings via JSON configuration.

Can this skill handle bulk data imports?

Yes, it includes a dedicated Data Import Manager specifically designed for handling operations involving more than 100 records from CSV or JSON files.

Does it support Lead conversion workflows?

Yes, it automates the conversion of qualified Leads into Contact and Account records, including diagnostic tools to analyze conversion blockers.

What happens if a Salesforce upsert fails?

The skill features an automated error queue and retry policy that uses progressive backoff (1, 5, and 15 minutes) before escalating persistent failures for manual review.

How does this skill prevent duplicate records in Salesforce?

It employs a 7-tier matching priority waterfall, checking Salesforce IDs, external IDs, and normalized email addresses before utilizing fuzzy name and domain matching logic.

Salesforce Upsert Orchestrator

Name: Salesforce Upsert Orchestrator
Author: RevPalSFDC

byRevPalSFDC

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Gestión de Bases de Datos

Orchestrates complex Salesforce record management through intelligent matching, lead conversion, and error-resilient data upsert workflows.

The Salesforce Upsert Orchestrator provides a standardized 8-phase methodology for managing data integrity within Salesforce environments. It excels at importing Leads, Contacts, and Accounts by leveraging a 7-tier matching priority waterfall that prevents duplicates using everything from 18-character IDs to fuzzy domain matching. Designed for RevOps teams and developers, this skill automates the entire lifecycle of a record—from pre-flight validation and external data enrichment to automated Lead conversion and robust error handling with exponential backoff retries.

Características Principales

018-Phase structured data processing workflow

027-Tier intelligent matching priority waterfall

03Automated error queue with backoff retry policies

04Automated Lead-to-Account/Contact conversion

05Built-in idempotency to prevent duplicate operations

061 GitHub stars

Casos de Uso

01Automating the conversion of qualified leads into existing account structures

02Importing high-volume Lead or Account data from CSV/JSON without creating duplicates

03Synchronizing external marketing data with Salesforce using high-confidence matching

Características Principales

018-Phase structured data processing workflow

027-Tier intelligent matching priority waterfall

03Automated error queue with backoff retry policies

04Automated Lead-to-Account/Contact conversion

05Built-in idempotency to prevent duplicate operations

061 GitHub stars

Casos de Uso

01Automating the conversion of qualified leads into existing account structures

02Importing high-volume Lead or Account data from CSV/JSON without creating duplicates

03Synchronizing external marketing data with Salesforce using high-confidence matching