social-graph-ranker

The social-graph-ranker skill provides a weighted graph-ranking engine for analyzing professional and social networks across X and LinkedIn. It is designed to help users identify the most valuable warm introduction paths within their existing network by calculating bridge strength, connection proximity, and relationship quality. The skill solves the problem of inefficient outreach by determining which mutual connections are best positioned to introduce the user to high-priority targets, while also identifying when direct outreach is more appropriate due to weak or nonexistent graph paths.

The skill supports weighted target scoring, shortest-path bridge analysis, second-order network expansion, and engagement-adjusted ranking. It applies configurable decay models to connection distance so that closer relationships contribute more value than distant ones. The ranking model incorporates factors such as role alignment, company fit, geographic relevance, responsiveness, influence, and prior interaction history. The workflow includes building a weighted target set, traversing the user’s network graph, computing bridge scores, expanding second-order candidates, and generating ranked recommendations for warm introductions, conditional bridge paths, and uncovered network gaps.

This skill is intended for users who want the reusable graph-ranking layer itself rather than a complete lead generation or network management workflow. Typical users include founders, recruiters, operators, sales professionals, investors, and business development teams seeking efficient introductions into target organizations or industries. It is especially useful for evaluating whether a target should be approached through a warm intro, a conditional bridge, or direct outreach, while also helping users understand the underlying graph-ranking methodology independently from broader outreach systems.