--- name: social-graph-ranker-24657648f76e description: 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. metadata: origin: ECC --- # 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: ```text B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1) ``` Where: - `λ` is the decay factor, usually `0.5` - a direct path contributes full value - each extra hop halves the contribution Second-order expansion: ```text B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t)) ``` 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: ```text R(m) = B_ext(m) · (1 + β · engagement(m)) ``` 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 traversal with whatever matters for the current priority set: - role or title alignment - company or industry fit - current activity and recency - geographic relevance - influence or reach - likelihood of response Weight mutuals after traversal with: - number of weighted paths into the target set - directness of those paths - responsiveness or prior interaction history - contextual fit for making the intro ## Workflow 1. Build the weighted target set. 2. Pull the user's graph from X, LinkedIn, or both. 3. Compute direct bridge scores. 4. Expand second-order candidates for the highest-value mutuals. 5. Rank by `R(m)`. 6. Return: - best warm intro asks - conditional bridge paths - graph gaps where no warm path exists ## Output Shape ```text SOCIAL GRAPH RANKING ==================== Priority Set: Platforms: Decay Model: Top Bridges - mutual / connection base_score: extended_score: best_targets: path_summary: recommended_action: Conditional Paths - mutual / connection reason: extra hop cost: No Warm Path - target recommendation: direct outreach / fill graph gap ``` ## Related Skills - `lead-intelligence` uses this ranking model inside the broader target-discovery and outreach pipeline - `connections-optimizer` uses the same bridge logic when deciding who to keep, prune, or add - `brand-voice` should run before drafting any intro request or direct outreach - `x-api` provides X graph access and optional execution paths --- Source attribution: affaan-m Original project: https://github.com/affaan-m/everything-claude-code/blob/25278e7d7b6cf6e6a4e7a6c8e10f14ff22f0eb5b/skills/social-graph-ranker/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Affaan Mustafa Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: affaan-m Original project: https://github.com/affaan-m/everything-claude-code/blob/25278e7d7b6cf6e6a4e7a6c8e10f14ff22f0eb5b/skills/social-graph-ranker/SKILL.md Attribution basis: Source repository record Fields of work: Marketing, Sales Roles: Business Development, Growth Marketer, Influencer Marketer, Marketing Analyst, Sales Development Rep (SDR) Specialties: Specialty not specified Tools: X Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: d127aeda4e74aa9a46df51e44950c4ca47985add043384c4ec3310ca05329b0a