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, X_ACCESS_TOKEN)

Optional (enhance results)

LinkedIn — Via browser-use MCP or direct API for connection graph
Apollo/Clay API — For enrichment cross-reference if user has access
GitHub MCP — For developer-centric lead qualification

Pipeline Overview

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

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.

Algorithm

Pull user's X following list and LinkedIn connections
For each high-signal target, check for shared connections
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

Output Format

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 Person

Path Types (ordered by warmth)

Direct mutual — You both follow/know the same person
Portfolio connection — Mutual invested in or advises target's company
Co-worker/alumni — Mutual worked at same company or attended same school
Event overlap — Both attended same conference/program
Content engagement — Target engaged with mutual's content or vice versa

Stage 4: Enrichment

For each qualified lead, pull:

Full name, current title, company
Company size, funding stage, recent news
Recent X posts (last 30 days) — topics, tone, interests
Mutual interests with user (shared follows, similar content)
Recent company events (product launch, funding round, hiring)

Enrichment Sources

Exa: company data, news, blog posts
X API: recent tweets, bio, followers
GitHub: open source contributions (for developer-centric leads)
LinkedIn (via browser-use): full profile, experience, education

Stage 5: Outreach Draft

Generate personalized outreach for each lead. Two modes:

Warm Intro Request (to mutual)

hey [mutual name],

quick ask. i see you know [target name] at [company].
i'm building [your product] which [1-line relevance to target].
would you be open to a quick intro? happy to send you a
forwardable blurb.

[your name]

Direct Cold Outreach (to target)

hey [target name],

[specific reference to their recent work/post/announcement].
i'm [your name], building [product]. [1 line on why this is
relevant to them specifically].

[specific low-friction ask].

[your name]

Anti-Patterns (never do)

Generic templates with no personalization
Long paragraphs explaining your whole company
Multiple asks in one message
Fake familiarity ("loved your recent talk!" without specifics)
Bulk-sent messages with visible merge fields

Configuration

Users should set these environment variables:

# Required
export X_BEARER_TOKEN="..."
export X_ACCESS_TOKEN="..."
export X_ACCESS_TOKEN_SECRET="..."
export X_API_KEY="..."
export X_API_SECRET="..."
export EXA_API_KEY="..."

# Optional
export LINKEDIN_COOKIE="..." # For browser-use LinkedIn access
export APOLLO_API_KEY="..."  # For Apollo enrichment

Agents

This skill includes specialized agents in the agents/ subdirectory:

signal-scorer — Searches and ranks prospects by relevance signals
mutual-mapper — Maps social graph connections and finds warm paths
enrichment-agent — Pulls detailed profile and company data
outreach-drafter — Generates personalized messages

Example Usage

User: find me the top 20 people in prediction markets I should reach out to

Agent workflow:
1. signal-scorer searches Exa and X for prediction market leaders
2. mutual-mapper checks user's X graph for shared connections
3. enrichment-agent pulls company data and recent activity
4. outreach-drafter generates personalized messages for top ranked leads

Output: Ranked list with warm paths and draft outreach for each

lead-intelligence