Agent skill · marketing growth · affaan-m
social-graph-ranker
Weighted social-graph ranking for warm intro discovery, bridge scoring, and network gap analysis across X and LinkedIn. Use when the user wants the reusable graph-ranking engine itself, not the broader outreach or network-maintenance workflow layered on top of it.
Why this skill is useful
Provides a specialized algorithm for ranking social connections based on weighted graph theory, which the AI wouldn't generate on its own.
What it needs
About 3k tokens when loaded. Last updated 2026-08-06. 238,342 stars on the source repository.
What this skill does
Social Graph Ranker Canonical weighted graph-ranking layer for network-aware outreach. Use this when the user needs to: rank existing mutuals or connections by intro value map warm paths to a target list measure bridge value across first- and second-order connections decide which targets deserve warm intros versus direct cold outreach understand the graph math independently from lead-intelligence or connections-optimizer When To Use This Standalone Choose this skill when the user primarily wants the ranking engine: "who in my network is best positioned to introduce me?" "rank my mutuals by who can get me to these people" "map my graph against this ICP" "show me the bridge math" Do not use this by itself when the user really wants: full lead generation and outbound sequencing -> use lead-intelligence pruning, rebalancing, and growing the network -> use connections-optimizer Inputs Collect or infer: target people, companies, or ICP definition the user's current graph on X, LinkedIn, or both weighting priorities such as role, industry, geography, and responsiveness traversal depth and decay tolerance Core Model Given: T = weighted target set M = your current mutuals / direct connections d(m, t) = shortest hop distance from mutual m to target t w(t) = target weight from signal scoring Base bridge score: Where: λ is the decay factor, usually 0.5 a direct path contributes full value each extra hop halves the contribution Second-order expansion: Where: N(m) \\ M is the set of people the mutual knows that you do not α discounts second-order reach, usually 0.3 Response-adjusted final ranking: Where: engagement(m) is normalized responsiveness or relationship strength β is the engagement bonus, usually 0.2 Interpretation: Tier 1: high R(m) and direct bridge paths -> warm intro asks Tier 2: medium R(m) and one-hop bridge paths -> conditional intro asks Tier 3: low R(m) or no viable bridge -> direct outreach or follow-gap fill Scoring Signals Weight targets before graph traversa …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills affaan-m/social-graph-ranker--70ab44