/lead-intelligence

name: lead-intelligence
description: AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.
metadata:
  origin: ECC

Lead Intelligence

Agent-powered lead intelligence pipeline that finds, scores, and reaches high-value contacts through social graph analysis and warm path discovery.

When to Activate

• User wants to find leads or prospects in a specific industry
• Building an outreach list for partnerships, sales, or fundraising
• Researching who to reach out to and the best path to reach them
• User says "find leads", "outreach list", "who should I reach out to", "warm intros"
• Needs to score or rank a list of contacts by relevance
• Wants to map mutual connections to find warm introduction paths

Tool Requirements

Required

• **Exa MCP** — Deep web search for people, companies, and signals (`web_search_exa`)
• **X API** — Follower/following graph, mutual analysis, recent activity (`X_BEARER_TOKEN`, plus write-context credentials such as `X_CONSUMER_KEY`, `X_CONSUMER_SECRET`, `X_ACCESS_TOKEN`, `X_ACCESS_TOKEN_SECRET`)

Optional (enhance results)

• **LinkedIn** — Direct API if available, otherwise browser control for search, profile inspection, and drafting
• **Apollo/Clay API** — For enrichment cross-reference if user has access
• **GitHub MCP** — For developer-centric lead qualification
• **Apple Mail / Mail.app** — Draft cold or warm email without sending automatically
• **Browser control** — For LinkedIn and X when API coverage is missing or constrained

Pipeline Overview

┌─────────────┐     ┌──────────────┐     ┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐
│ 1. Signal   │────>│ 2. Mutual    │────>│ 3. Warm Path    │────>│ 4. Enrich    │────>│ 5. Outreach     │
│    Scoring  │     │    Ranking   │     │    Discovery    │     │              │     │    Draft        │
└─────────────┘     └──────────────┘     └─────────────────┘     └──────────────┘     └─────────────────┘

Voice Before Outreach

Do not draft outbound from generic sales copy.

Run `brand-voice` first whenever the user's voice matters. Reuse its `VOICE PROFILE` instead of re-deriving style ad hoc inside this skill.

If live X access is available, pull recent original posts before drafting. If not, use supplied examples or the best repo/site material available.

Stage 1: Signal Scoring

Search for high-signal people in target verticals. Assign a weight to each based on:

| Signal | Weight | Source | |--------|--------|--------| | Role/title alignment | 30% | Exa, LinkedIn | | Industry match | 25% | Exa company search | | Recent activity on topic | 20% | X API search, Exa | | Follower count / influence | 10% | X API | | Location proximity | 10% | Exa, LinkedIn | | Engagement with your content | 5% | X API interactions |

Signal Search Approach

# Step 1: Define target parameters
target_verticals = ["prediction markets", "AI tooling", "developer tools"]
target_roles = ["founder", "CEO", "CTO", "VP Engineering", "investor", "partner"]
target_locations = ["San Francisco", "New York", "London", "remote"]

# Step 2: Exa deep search for people
for vertical in target_verticals:
    results = web_search_exa(
        query=f"{vertical} {role} founder CEO",
        category="company",
        numResults=20
    )
    # Score each result

# Step 3: X API search for active voices
x_search = search_recent_tweets(
    query="prediction markets OR AI tooling OR developer tools",
    max_results=100
)
# Extract and score unique authors

Stage 2: Mutual Ranking

For each scored target, analyze the user's social graph to find the warmest path.

Ranking Model

1. Pull user's X following list and LinkedIn connections 2. For each high-signal target, check for shared connections 3. Apply the `social-graph-ranker` model to score bridge value 4. Rank mutuals by:

| Factor | Weight | |--------|--------| | Number of connections to targets | 40% — highest weight, most connections = highest rank | | Mutual's current role/company | 20% — decision maker vs individual contributor | | Mutual's location | 15% — same city = easier intro | | Industry alignment | 15% — same vertical = natural intro | | Mutual's X handle / LinkedIn | 10% — identifiability for outreach |

Canonical rule:

Use social-graph-ranker when the user wants the graph math itself,
the bridge ranking as a standalone report, or explicit decay-model tuning.

Inside this skill, use the same weighted bridge model:

B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
R(m) = B_ext(m) · (1 + β · engagement(m))

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: no viable bridge -> direct cold outreach using the same lead record

Output Format

If the user explicitly wants the ranking engine broken out, the math visualized, or the network scored outside the full lead workflow, run `social-graph-ranker` as a standalone pass first and feed the result back into this pipeline.
MUTUAL RANKING REPORT
=====================

#1  @mutual_handle (Score: 92)
    Name: Jane Smith
    Role: Partner @ Acme Ventures
    Location: San Francisco
    Connections to targets: 7
    Connected to: @target1, @target2, @target3, @target4, @target5, @target6, @target7
    Best intro path: Jane invested in Target1's company

#2  @mutual_handle2 (Score: 85)
    ...

Stage 3: Warm Path Discovery

For each target, find the shortest introduction chain:

You ──[follows]──> Mutual A ──[invested in]──> Target Company
You ──[follows]──> Mutual B ──[co-founded with]──> Target Person
You ──[met at]──> Event ──[also attended]──> Target Pers