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triage-pregraph-data

Audit pre-graph datasets and access rules for data-quality issues.

7|Updated May 18, 2026
Search Tags:#auditing#triage#graph#data-quality#datasets#nql#access-rules
One-click install
npx skills add https://github.com/narrative-io/narrative-skills-marketplace --skill triage-pregraph-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: triage-pregraph-data
Source: https://github.com/narrative-io/narrative-skills-marketplace/tree/main/plugins/narrative-identity/skills/triage-pregraph-data
Command: npx skills add https://github.com/narrative-io/narrative-skills-marketplace --skill triage-pregraph-data

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Audit a pre-graph dataset or access rule to identify data-quality issues before it joins an identity-graph.

Core Features & Use Cases

  • Detects hub identifiers and high-degree nodes to surface structural quality risks that threaten graph integrity.
  • Generates falsifiable hypotheses and coordinates parallel analyses to quantify impact and propose mitigations.
  • Outputs a validated clean-view NQL or a clear "no materialization required" result to feed the graph-build workflow.

Quick Start

Provide the dataset or access rule to begin the pre-graph audit and obtain a report with findings, thresholds, and a validated clean-view NQL if needed.

Frequently Asked Questions about triage-pregraph-data

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

FAQPage Schema
How do I audit dataset quality before building an identity graph?▼

To audit dataset quality before building an identity graph, you can use this Skill to frame a pre-graph audit, generate testable hypotheses, and coordinate parallel analysis against your source data to quantify potential structural risks.

What causes high-degree nodes and hub identifiers to degrade identity graph quality?▼

High-degree nodes and hub identifiers degrade identity graph quality by creating structural risks that skew entity resolution. This Skill detects these anomalies during a pre-graph audit to prevent them from compromising your final graph integrity.

How do I generate a clean-view NQL for a pre-graph dataset?▼

You generate a clean-view NQL by providing your pre-graph dataset or access rule to this Skill, which analyzes the data for quality issues and outputs a validated NQL payload to feed directly into your graph-build workflow.

Can I audit an access rule for data-quality issues before materialization?▼

Yes, you can audit an access rule for data-quality issues before materialization. This Skill evaluates the rule to identify structural risks and either delivers a validated clean-view NQL or confirms that no materialization is required.

What happens if no data-quality issues are found during a pre-graph audit?▼

If no data-quality issues are found during a pre-graph audit, the Skill outputs a clear result indicating that no materialization is required, allowing you to proceed directly to the identity-graph building phase without generating a clean-view NQL.

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