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TravisLeeeeee
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Identity Graph Operator

Resolve entity records to canonical entity_ids with evidence-based matching.

110|18|Updated Mar 25, 2026
Search Tags:#identity resolution#confidence scoring#graph integrity#entity matching#PII masking#canonical ids#merge proposals
One-click install
npx skills add https://github.com/TravisLeeeeee/awesome-openclaw-personas --skill identity-graph-operator-travisleeeeee
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Identity Graph Operator
Source: https://github.com/TravisLeeeeee/awesome-openclaw-personas/tree/main/personas/specialized/identity-graph-operator
Command: npx skills add https://github.com/TravisLeeeeee/awesome-openclaw-personas --skill identity-graph-operator-travisleeeeee

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents duplicate and conflicting records by deterministically resolving incoming entity records to the correct canonical entity_id using evidence-based, field-level matching.

Core Features & Use Cases

  • Deterministic identity resolution: Produces the same canonical entity_id for the same real-world entity even across concurrent writes and multi-agent access.
  • Evidence-based fuzzy matching: Uses blocking plus field-level scoring (including nickname normalization and E.164 phone formatting) and explains results with confidence and per-field evidence.
  • Merge governance with audit trails: Proposes merges (rather than directly mutating) with per-field scores and reasoning to support multi-agent review, conflict handling, and event-history integrity.
  • Tenant isolation & privacy safety: Scopes all queries per tenant and masks PII by default, revealing it only when explicitly authorized.

Quick Start

Copy the identity-graph-operator persona into your OpenClaw workspace, then ask it to resolve a new customer record into a canonical entity_id using blocking, normalization, scoring, and evidence-backed merge proposals when needed.

Frequently Asked Questions about Identity Graph Operator

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

FAQPage Schema
How do I resolve duplicate customer records to a single canonical ID?▼

Identity resolution maps duplicate customer records to deterministic canonical entity_ids by using blocking-based candidate retrieval and field-level scoring to eliminate conflicting multi-agent decisions.

How does fuzzy entity matching handle different phone and name formats?▼

Fuzzy entity matching normalizes inputs using E.164 phone formatting and nickname normalization, then applies field-level scoring with confidence thresholds to produce evidence-backed match results.

What is the best way to prevent conflicting merges during concurrent identity reconciliation?▼

Identity reconciliation prevents conflicts by proposing merges with audit trails and optimistic locking rather than directly mutating records, ensuring event-history integrity during concurrent writes.

Can I use this identity resolution approach for multi-tenant systems with PII data?▼

Yes, identity resolution supports multi-tenant systems by scoping all queries per tenant and masking PII by default, revealing sensitive data only when explicitly authorized.

When should I use evidence-based merge proposals instead of direct record mutation?▼

Evidence-based merge proposals are necessary for multi-agent review and conflict handling, providing per-field scores and reasoning to maintain audit trails and prevent unauthorized data mutation.

Does entity matching across heterogeneous sources require blocking-based candidate retrieval?▼

Yes, entity matching across heterogeneous sources requires blocking-based candidate retrieval to efficiently narrow potential matches before applying field-level scoring and confidence thresholds.

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