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social-graph-ranker

Rank mutuals by bridge value into a weighted target set for warm introductions.

Updated Sep 13, 2025
Search Tags:#network analysis#ranking#social graph#warm intros#lead discovery#bridge scoring#relationship engagement
One-click install
npx skills add https://github.com/llmh333/employee_management_spring --skill social-graph-ranker-llmh333
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: social-graph-ranker
Source: https://github.com/llmh333/employee_management_spring/tree/main/.gemini/skills/social-graph-ranker
Command: npx skills add https://github.com/llmh333/employee_management_spring --skill social-graph-ranker-llmh333

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Social Graph Ranker helps you find the most valuable people in your existing network to introduce you to specific targets by scoring direct and bridge-based paths across first- and second-order connections.

Core Features & Use Cases

  • Warm-intro ranking engine: Computes bridge value to prioritize which mutuals/connections are best positioned to make intros.
  • Bridge and network-gap analysis: Quantifies how much value comes through one-hop and second-order paths, highlighting where warm paths don’t exist.
  • Output tailored to outreach decisions: Produces top bridges, conditional paths, and recommendations for direct outreach when no warm path is available.
  • Graph- math transparency: Uses weighted decay scoring with engagement adjustment so the ranking is understandable and tunable.

Quick Start

Ask the skill to rank your mutuals for a target list by using your current X/LinkedIn graph and your chosen decay and engagement parameters.

Frequently Asked Questions about social-graph-ranker

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

FAQPage Schema
How do I rank warm intros from my social graph to reach specific targets?▼

Warm intro ranking computes bridge value across first and second-order connections. It applies a weighted decay model with engagement adjustment to score mutuals, returning top bridges and conditional paths for outreach.

What's the best way to identify network gaps when mapping warm paths to an ICP?▼

Network gap analysis quantifies one-hop and second-order path values to highlight where warm paths do not exist. It identifies missing connections and returns no-warm-path recommendations for direct outreach.

How does bridge scoring work for lead discovery through mutual connections?▼

Bridge scoring uses a weighted shortest-path decay model with second-order expansion. It adjusts final rankings based on relationship engagement to determine the best candidates for warm introductions.

Can I use this to prioritize intro asks versus conditional bridge attempts?▼

Yes, the ranking engine outputs structured recommendations for prioritizing direct intro asks against conditional bridge attempts. It evaluates bridge value to guide your relationship engagement strategy.

What data do I need to calculate bridge value for lead discovery?▼

You need your current X or LinkedIn graph data, plus chosen decay and engagement parameters. The skill processes this connection data to map weighted target sets and rank warm intro candidates.

When should I expect no-warm-path recommendations from the social graph ranker?▼

No-warm-path recommendations are returned when the analysis identifies network gaps where no viable warm paths exist. This occurs when second-order expansion finds no bridge value to the target list.

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