social-graph-ranker

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

When to Use

适用场景

Use this skill when you need to:

Find warm intro paths to specific people or companies
Rank your existing connections by networking value
Identify which mutuals to ask for introductions
Prioritize outbound outreach by warmth and proximity
Map your social graph against a target lead list

当你有以下需求时可使用本skill：

寻找通往特定人员或公司的熟人引荐路径
按人脉价值对你的现有连接进行排序
识别可以请求帮忙引荐的共同好友
根据关系热度和接近度优先排序对外触达动作
将你的社交图与目标线索列表做匹配映射

How It Works

工作原理

Architecture

架构

Two parallel pipelines feed a unified ranking engine:

┌─────────────────────────────────────────────────────────┐
│                    SOCIAL GRAPH RANKER                   │
├──────────────────────┬──────────────────────────────────┤
│   INBOUND PIPELINE   │        OUTBOUND PIPELINE         │
│                      │                                   │
│  Your Connections    │     Target Lead List (ICP)        │
│  ┌──────────────┐    │     ┌──────────────────┐         │
│  │ X Mutuals    │    │     │ lead-intelligence │         │
│  │ X Followers  │    │     │ skill (parallel)  │         │
│  │ LI Connections│   │     │ Exa + X API +     │         │
│  └──────┬───────┘    │     │ enrichment agents │         │
│         │            │     └────────┬─────────┘         │
│         ▼            │              ▼                    │
│  ┌──────────────┐    │     ┌──────────────────┐         │
│  │ Connection   │    │     │ Ranked Lead List  │         │
│  │ Graph Build  │    │     │ (scored by ICP    │         │
│  │ (adjacency)  │    │     │  fit + response   │         │
│  └──────┬───────┘    │     │  probability)     │         │
│         │            │     └────────┬─────────┘         │
│         └────────────┼──────────────┘                    │
│                      ▼                                   │
│         ┌──────────────────────┐                        │
│         │  GRAPH INTERSECTION  │                        │
│         │  Match connections   │                        │
│         │  against targets     │                        │
│         └──────────┬───────────┘                        │
│                    ▼                                     │
│         ┌──────────────────────┐                        │
│         │  WEIGHTED RANKING    │                        │
│         │  Exponential decay   │                        │
│         │  across hops         │                        │
│         └──────────┬───────────┘                        │
│                    ▼                                     │
│         ┌──────────────────────┐                        │
│         │  PRIORITIZED OUTPUT  │                        │
│         │  1. Warm intro asks  │                        │
│         │  2. Direct outreach  │                        │
│         │  3. Network gaps     │                        │
│         └──────────────────────┘                        │
└─────────────────────────────────────────────────────────┘

两个并行流水线为统一排序引擎提供数据：

┌─────────────────────────────────────────────────────────┐
│                    SOCIAL GRAPH RANKER                   │
├──────────────────────┬──────────────────────────────────┤
│   INBOUND PIPELINE   │        OUTBOUND PIPELINE         │
│                      │                                   │
│  Your Connections    │     Target Lead List (ICP)        │
│  ┌──────────────┐    │     ┌──────────────────┐         │
│  │ X Mutuals    │    │     │ lead-intelligence │         │
│  │ X Followers  │    │     │ skill (parallel)  │         │
│  │ LI Connections│   │     │ Exa + X API +     │         │
│  └──────┬───────┘    │     │ enrichment agents │         │
│         │            │     └────────┬─────────┘         │
│         ▼            │              ▼                    │
│  ┌──────────────┐    │     ┌──────────────────┐         │
│  │ Connection   │    │     │ Ranked Lead List  │         │
│  │ Graph Build  │    │     │ (scored by ICP    │         │
│  │ (adjacency)  │    │     │  fit + response   │         │
│  └──────┬───────┘    │     │  probability)     │         │
│         │            │     └────────┬─────────┘         │
│         └────────────┼──────────────┘                    │
│                      ▼                                   │
│         ┌──────────────────────┐                        │
│         │  GRAPH INTERSECTION  │                        │
│         │  Match connections   │                        │
│         │  against targets     │                        │
│         └──────────┬───────────┘                        │
│                    ▼                                     │
│         ┌──────────────────────┐                        │
│         │  WEIGHTED RANKING    │                        │
│         │  Exponential decay   │                        │
│         │  across hops         │                        │
│         └──────────┬───────────┘                        │
│                    ▼                                     │
│         ┌──────────────────────┐                        │
│         │  PRIORITIZED OUTPUT  │                        │
│         │  1. Warm intro asks  │                        │
│         │  2. Direct outreach  │                        │
│         │  3. Network gaps     │                        │
│         └──────────────────────┘                        │
└─────────────────────────────────────────────────────────┘

The Math: Weighted Graph Proximity Score

算法逻辑：加权图接近度评分

Given:

T = set of target leads you want to reach
M = set of your mutuals/connections
G = social graph (adjacency)
d(m, t) = shortest path distance from mutual m to target t

For each mutual m, compute:

Bridge Score:

B(m) = Σ_{t ∈ T} w(t) · λ^{d(m,t) - 1}

Where:

```
w(t)
```
= target weight (from lead-intelligence signal score: role 30%, industry 25%, activity 20%, influence 10%, location 10%, engagement 5%)
```
λ
```
= decay factor, typically 0.5 (halves value each hop)
```
d(m,t)
```
= hop distance (1 = direct connection, 2 = mutual-of-mutual, etc.)
Convention:
```
d(m,t) = 1
```
for direct connection, so
```
λ^0 = 1
```
(full value)

Properties:

Direct connection to target: contributes
```
w(t) · 1.0
```
One hop away: contributes
```
w(t) · 0.5
```
Two hops: contributes
```
w(t) · 0.25
```
Three hops: contributes
```
w(t) · 0.125
```
Effectively zero beyond ~6 hops (Gaussian/exponential decay → 0)

Extended Score (second-order network value):

For deeper traversal, also consider the mutual's own network reach:

B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \ M} Σ_{t ∈ T} w(t) · λ^{d(m',t)}

Where:

```
N(m) \ M
```
= connections of m that you DON'T already have
```
α
```
= second-order discount (typically 0.3)
This captures: "even if m doesn't know my targets directly, m knows people I don't, who might"

Final Ranking:

R(m) = B_ext(m) · (1 + β · engagement(m))

Where:

```
engagement(m)
```
= normalized score of how responsive m is (reply rate, interaction frequency)
```
β
```
= engagement bonus weight (typically 0.2)

给定参数：

T = 你想要触达的目标线索集合
M = 你的共同好友/人脉连接集合
G = 社交图（邻接关系）
d(m, t) = 共同好友m到目标t的最短路径距离

对每个共同好友m，计算：

桥梁评分：

B(m) = Σ_{t ∈ T} w(t) · λ^{d(m,t) - 1}

其中：

```
w(t)
```
= 目标权重（来自lead-intelligence信号评分：角色30%、行业25%、活跃度20%、影响力10%、所在地10%、互动度5%）
```
λ
```
= 衰减因子，通常为0.5（每多一跳价值减半）
```
d(m,t)
```
= 跳数距离（1 = 直接连接，2 = 好友的好友，以此类推）
约定：直接连接的
```
d(m,t) = 1
```
，因此
```
λ^0 = 1
```
（全额价值）

特性：

与目标直接连接：贡献
```
w(t) · 1.0
```
间隔1跳：贡献
```
w(t) · 0.5
```
间隔2跳：贡献
```
w(t) · 0.25
```
间隔3跳：贡献
```
w(t) · 0.125
```
超过约6跳后价值基本归零（高斯/指数衰减趋近于0）

扩展评分（二阶人脉价值）：

如需更深层遍历，还可考虑共同好友自身的人脉覆盖范围：

B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \ M} Σ_{t ∈ T} w(t) · λ^{d(m',t)}

其中：

```
N(m) \ M
```
= 你尚未添加的m的人脉连接
```
α
```
= 二阶折扣系数（通常为0.3）
该参数用于衡量：「即使m不直接认识我的目标，m认识的我不认识的人也有可能认识目标」

最终排序：

R(m) = B_ext(m) · (1 + β · engagement(m))

其中：

```
engagement(m)
```
= m响应度的标准化评分（回复率、互动频率）
```
β
```
= 互动度加成权重（通常为0.2）

Execution Steps

执行步骤

Build Target List
- Run lead-intelligence skill in parallel to generate scored ICP leads
- Or provide manual target list with names/handles
Harvest Your Graph
- X API:
```
GET /2/users/:id/following
```
  and
```
GET /2/users/:id/followers
```
- LinkedIn: connection export CSV or browser-use scraping
- Build adjacency map:
```
mutual → [their connections]
```
Intersect and Score
- For each mutual, check which targets they follow/connect with
- Compute B(m) with decay
- For top-k mutuals, expand one more hop and compute B_ext(m)
Generate Output
- Tier 1: Mutuals with B(m) > threshold → warm intro requests
- Tier 2: Targets with no warm path → direct cold outreach via lead-intelligence
- Tier 3: Network gaps → suggest who to follow/connect with to build bridges
Draft Messages
- Warm intro: "Hey [mutual], I saw you're connected to [target]. I'm working on [context]. Would you be open to making an intro?"
- Uses outreach-drafter agent from lead-intelligence for personalization

构建目标列表
- 并行运行lead-intelligence skill生成带评分的理想客户画像（ICP）线索
- 或手动提供包含姓名/账号的目标列表
采集你的社交图数据
- X API：
```
GET /2/users/:id/following
```
  和
```
GET /2/users/:id/followers
```
- LinkedIn：连接导出CSV或browser-use爬虫
- 构建邻接映射：
```
共同好友 → [Ta的人脉列表]
```
匹配与评分
- 对每个共同好友，检查Ta关注/连接了哪些目标
- 结合衰减系数计算B(m)
- 对排名前k的共同好友，再扩展一跳计算B_ext(m)
生成输出
- 第一梯队：B(m)高于阈值的共同好友 → 请求熟人引荐
- 第二梯队：无熟人引荐路径的目标 → 通过lead-intelligence直接冷触达
- 第三梯队：人脉缺口 → 建议你关注/连接哪些人来搭建桥梁
草稿消息生成
- 熟人引荐话术：「嗨[共同好友姓名]，我看到你和[目标]是连接关系，我目前正在做[项目背景]，可以麻烦你帮忙引荐一下吗？」
- 调用lead-intelligence的outreach-drafter agent实现个性化话术生成

Configuration

配置

yaml

undefined

yaml

undefined

Target definition

targets:

handle: "@targetperson" platform: x weight: 0.9 # override signal score

targets:

handle: "@targetperson" platform: x weight: 0.9 # override signal score

Decay parameters

decay_factor: 0.5 # λ — halve value per hop max_depth: 3 # don't traverse beyond 3 hops second_order_discount: 0.3 # α — discount for network-of-network engagement_bonus: 0.2 # β — bonus for responsive mutuals

API configuration

x_api: bearer_token: $X_BEARER_TOKEN rate_limit_delay: 1.1 # seconds between API calls linkedin: method: csv_export # or browser_use csv_path: ~/Downloads/Connections.csv

undefined

x_api: bearer_token: $X_BEARER_TOKEN rate_limit_delay: 1.1 # seconds between API calls linkedin: method: csv_export # or browser_use csv_path: ~/Downloads/Connections.csv

undefined

Integration with lead-intelligence

与lead-intelligence集成

This skill runs IN PARALLEL with lead-intelligence:

lead-intelligence generates the target list (T) with signal scores
social-graph-ranker maps your network against those targets
Combined output: prioritized outreach plan with warm paths where available

本skill与lead-intelligence并行运行：

lead-intelligence生成带信号评分的目标列表（T）
social-graph-ranker将你的人脉与这些目标做匹配
合并输出：带可用熟人引荐路径的优先级外展计划

Example Output

输出示例

BRIDGE RANKING — Top 10 Mutuals by Network Value
═══════════════════════════════════════════════════

#1  @alex_quant (B=4.72)
    Direct → @kalshi_ceo (w=0.9), @polymarket_shayne (w=0.85)
    1-hop  → @a16z_crypto (w=0.7, via @defi_mike)
    Action: Ask for intros to Kalshi + Polymarket

#2  @sarah_vc (B=3.15)
    Direct → @sequoia_partner (w=0.95)
    1-hop  → @yc_gustaf (w=0.8, via @batch_founder)
    Action: Ask for Sequoia intro

#3  @dev_community (B=2.88)
    Direct → @cursor_ceo (w=0.6), @vercel_guillermo (w=0.6)
    2-hop  → @anthropic_dario (w=0.95, via @cursor_ceo → @anthropic_team)
    Action: Ask for Cursor intro, mention Anthropic angle

NETWORK GAPS — No warm path exists
═══════════════════════════════════
@target_x — Suggest following @bridge_person_1, @bridge_person_2
@target_y — Direct cold outreach recommended (lead-intelligence draft ready)

BRIDGE RANKING — Top 10 Mutuals by Network Value
═══════════════════════════════════════════════════

#1  @alex_quant (B=4.72)
    Direct → @kalshi_ceo (w=0.9), @polymarket_shayne (w=0.85)
    1-hop  → @a16z_crypto (w=0.7, via @defi_mike)
    Action: Ask for intros to Kalshi + Polymarket

#2  @sarah_vc (B=3.15)
    Direct → @sequoia_partner (w=0.95)
    1-hop  → @yc_gustaf (w=0.8, via @batch_founder)
    Action: Ask for Sequoia intro

#3  @dev_community (B=2.88)
    Direct → @cursor_ceo (w=0.6), @vercel_guillermo (w=0.6)
    2-hop  → @anthropic_dario (w=0.95, via @cursor_ceo → @anthropic_team)
    Action: Ask for Cursor intro, mention Anthropic angle

NETWORK GAPS — No warm path exists
═══════════════════════════════════
@target_x — Suggest following @bridge_person_1, @bridge_person_2
@target_y — Direct cold outreach recommended (lead-intelligence draft ready)