social-graph-ranker

SKILL.md

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:

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:

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:

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

Build the weighted target set.

Pull the user's graph from X, LinkedIn, or both.

Compute direct bridge scores.

Expand second-order candidates for the highest-value mutuals.

Rank by R(m).

Return:

best warm intro asks

conditional bridge paths

graph gaps where no warm path exists

Output Shape

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