hr-network-analyst

Classify professional network nodes into Gladwellian archetypes using centrality metrics.

181|30|Updated Nov 16, 2025

Search Tags:#network #HR #centrality #influence #gladwell

Ready to run

npx skills add https://github.com/erichowens/some_claude_skills --skill hr-network-analyst

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hr-network-analyst
Source: https://github.com/erichowens/some_claude_skills/tree/main/.claude/skills/hr-network-analyst
Command: npx skills add https://github.com/erichowens/some_claude_skills --skill hr-network-analyst

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Applies network theory to identify connectors, mavens, and brokers to map influence and opportunities in professional ecosystems.

Core Features & Use Cases

Centrality & Archetype: Betweenness, degree, eigenvector analyses with Gladwellian archetypes.
Data-Driven Insights: Identify structural holes and leverage bridges for opportunities.

Quick Start

Build a basic network projection from multi-source data and classify nodes into connectors, mavens, and brokers.

Frequently Asked Questions about hr-network-analyst

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

FAQPage Schema

How do I identify key connectors and influencers in my professional network?▼

Network centrality analysis identifies connectors by measuring betweenness, degree, eigenvector, and PageRank metrics. This Skill applies these metrics to professional data to surface Gladwellian archetypes—superconnectors, mavens, and brokers—who bridge groups and influence opportunity flow in your organization.

What is structural hole analysis and why does it matter for HR?▼

Structural holes are gaps between disconnected groups in a network. Burt's structural hole analysis identifies brokers positioned across these gaps, revealing who can bridge departments or domains. In HR contexts, this uncovers high-value connectors for cross-domain referrals and collaboration.

Can I analyze professional networks from multiple data sources?▼

Yes. This Skill fuses multi-source professional network data and applies centrality metrics and archetype classification across merged datasets. This enables robust analysis of complex organizational ecosystems from email, collaboration platforms, and other sources combined.

How do I map influence flow and find opportunities in my organization?▼

Influence-flow mapping uses centrality metrics to track how information and opportunity propagate through professional networks. By classifying nodes as connectors, mavens, or brokers, you target high-impact individuals for referrals, introductions, and strategic positioning.

What's the difference between betweenness, degree, and eigenvector centrality?▼

Degree centrality counts direct connections; betweenness measures brokerage position between groups; eigenvector identifies nodes connected to influential peers. This Skill computes all three to classify archetypes—each metric reveals different network roles and influence pathways.

Do I need existing network data to use this analysis?▼

Yes. The Skill requires multi-source professional network data as input. You must have structured relationship data—such as collaboration records, email graphs, or organizational ties—to project and analyze the network before classification can begin.