Can I use this for CRM data cleanup?

Absolutely. It is specifically designed to diagnose duplicate records and normalize provider outputs before syncing them to downstream CRMs or CDPs.

How is data lineage tracked?

The skill includes an Audit Trail component that captures lineage metadata, before-and-after snapshots, and versioned rules for full transparency.

How does this skill handle conflicting data between sources?

It utilizes a structured Conflict Handling framework that defines precedence rules, merge policies, and exception queues to manage data discrepancies.

Does this support fuzzy matching for account names?

Yes, the skill allows you to configure both deterministic keys (like CRM IDs) and fuzzy matching rules with specific weight and confidence thresholds.

Identity Resolution & Account Matching

Name: Identity Resolution & Account Matching
Author: gtmagents

bygtmagents

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Gestión de Bases de Datos

Normalizes and matches accounts, contacts, and opportunities across disparate data sources to maintain a unified source of truth.

Acerca de

This skill provides a comprehensive framework for reconciling customer and prospect data within Claude Code. It allows developers and RevOps teams to implement a structured identity resolution playbook, covering key hierarchy definition, deterministic and fuzzy matching logic, and conflict resolution policies. By automating the alignment of enrichment data, intent signals, and product telemetry with CRM IDs, it ensures data integrity across downstream systems like CDPs and CRMs while providing a clear audit trail for governance and reporting.

Características Principales

29 GitHub stars
Comprehensive data lineage and audit metadata capture
Automated conflict handling and merge precedence policies
Multi-source account and contact matching logic
Exception queue workflows for manual data review and SLAs
Deterministic and fuzzy rule configuration with confidence scoring

Casos de Uso

Reconciling third-party enrichment data with internal CRM records
Deduplicating conflicting contact records during data migrations
Aggregating intent signals from multiple providers onto a single account ID

Acerca de

Características Principales

29 GitHub stars
Comprehensive data lineage and audit metadata capture
Automated conflict handling and merge precedence policies
Multi-source account and contact matching logic
Exception queue workflows for manual data review and SLAs
Deterministic and fuzzy rule configuration with confidence scoring

Casos de Uso

Reconciling third-party enrichment data with internal CRM records
Deduplicating conflicting contact records during data migrations
Aggregating intent signals from multiple providers onto a single account ID