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identity-graph-operator

Manage customer identity resolution, consent propagation, and data clean room collaboration.

2|Updated Jun 30, 2026
Search Tags:#data privacy#identity resolution#consent management#fraud detection#customer identity
One-click install
npx skills add https://github.com/Canhada-Labs/ceo-orchestration --skill identity-graph-operator-canhada-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: identity-graph-operator
Source: https://github.com/Canhada-Labs/ceo-orchestration/tree/main/.claude/skills/domains/identity-systems/skills/identity-graph-operator
Command: npx skills add https://github.com/Canhada-Labs/ceo-orchestration --skill identity-graph-operator-canhada-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines and secures customer identity graph operations, ensuring compliance and accuracy in data handling.

Core Features & Use Cases

  • Identity Resolution: Handles deterministic and probabilistic match strategies for customer identity resolution.
  • Data Clean Room Collaboration: Enables secure multi-party data collaboration without raw data exchange.
  • Consent Propagation: Manages consent records and ensures compliance with LGPD, GDPR, CCPA, and DMA regulations.
  • Fraud Detection: Detects synthetic identities and bot traffic to protect against fraud.
  • Use Case: For a marketing team, use this Skill to construct a customer identity graph, integrate with a CDP, and ensure all data handling complies with data privacy regulations.

Quick Start

Activate the identity-graph-operator skill to begin processing customer identity data and ensure compliance with data privacy regulations.

Frequently Asked Questions about identity-graph-operator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does identity resolution work for matching customer records?▼

Identity resolution processes customer records by applying deterministic and probabilistic match strategies to accurately link identities. This approach handles both exact matches and statistical correlations to build a cohesive customer profile.

How do I ensure consent propagation complies with GDPR and CCPA regulations?▼

Consent propagation complies with GDPR, CCPA, LGPD, and DMA regulations by systematically managing consent records across the customer identity graph. This ensures all data handling respects user privacy preferences.

Can I use Python and pandas for data clean room collaboration?▼

Yes, data clean room collaboration requires Python libraries including pandas and numpy. These dependencies enable secure multi-party data collaboration and analysis without exposing raw underlying data.

What is the best way to detect synthetic identities and bot traffic for fraud detection?▼

Fraud detection identifies synthetic identities and bot traffic by analyzing patterns within the customer identity graph. This protects data integrity by filtering out fraudulent entries before they are integrated into CDP systems.

Do I need scikit-learn for probabilistic matching in customer identity graphs?▼

Yes, scikit-learn is required alongside pandas and numpy to perform probabilistic matching and customer identity graph operations. These Python libraries provide the necessary statistical modeling and data processing capabilities.

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