everything-claude-code/skills/social-graph-ranker/SKILL.md

name, description, metadata

name	description	metadata
social-graph-ranker	Weighted social graph traversal that ranks your network connections by proximity to target leads. Uses exponential decay across hops, parallel execution with lead-intelligence skill, and API-driven outreach prioritization. Replaces Apollo, Clay, and manual networking.	tags networking, outreach, leads, graph-theory, x-api, linkedin, exa

When to Use

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

How It Works

Architecture

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     │                        │
│         └──────────────────────┘                        │
└─────────────────────────────────────────────────────────┘

The Math: Weighted Graph Proximity Score

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)

Execution Steps

1. Build Target List

   • Run lead-intelligence skill in parallel to generate scored ICP leads
   • Or provide manual target list with names/handles

2. 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]

3. 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)

4. 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

5. 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

Configuration

# Target definition
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

Integration with lead-intelligence

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

Example Output

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)