Lead Intelligence

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

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┌─────────────┐     ┌──────────────┐     ┌─────────────────┐     ┌──────────────┐     ┌─────────────────┐
│ 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 Search Approach

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# 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:

Canonical rule:

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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:

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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

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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:

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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)

1. Direct mutual: You both follow/know the same person
2. Portfolio connection: Mutual invested in or advises target's company
3. Co-worker/alumni: Mutual worked at same company or attended same school
4. Event overlap: Both attended same conference/program
5. 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. The draft should match the source-derived voice profile and the target channel.

Channel Rules

Email

• Use for the highest-value cold outreach, warm intros, investor outreach, and partnership asks
• Default to drafting in Apple Mail / Mail.app when local desktop control is available
• Create drafts first, do not send automatically unless the user explicitly asks
• Subject line should be plain and specific, not clever

LinkedIn

• Use when the target is active there, when mutual graph context is stronger on LinkedIn, or when email confidence is low
• Prefer API access if available
• Otherwise use browser control to inspect profiles, recent activity, and draft the message
• Keep it shorter than email and avoid fake professional warmth

X

• Use for high-context operator, builder, or investor outreach where public posting behavior matters
• Prefer API access for search, timeline, and engagement analysis
• Fall back to browser control when needed
• DMs and public replies should be much tighter than email and should reference something real from the target's timeline

Channel Selection Heuristic

Pick one primary channel in this order:

1. warm intro by email
2. direct email
3. LinkedIn DM
4. X DM or reply

Use multi-channel only when there is a strong reason and the cadence will not feel spammy.

Warm Intro Request (to mutual)

Goal:

• one clear ask
• one concrete reason this intro makes sense
• easy-to-forward blurb if needed

Avoid:

• overexplaining your company
• social-proof stacking
• sounding like a fundraiser template

Direct Cold Outreach (to target)

Goal:

• open from something specific and recent
• explain why the fit is real
• make one low-friction ask

Avoid:

• generic admiration
• feature dumping
• broad asks like "would love to connect"
• forced rhetorical questions

Execution Pattern

For each target, produce:

1. the recommended channel
2. the reason that channel is best
3. the message draft
4. optional follow-up draft
5. if email is the chosen channel and Apple Mail is available, create a draft instead of only returning text

If browser control is available:

• LinkedIn: inspect target profile, recent activity, and mutual context, then draft or prepare the message
• X: inspect recent posts or replies, then draft DM or public reply language

If desktop automation is available:

• Apple Mail: create draft email with subject, body, and recipient

Do not send messages automatically without explicit user approval.

Anti-Patterns

• generic templates with no personalization
• long paragraphs explaining your whole company
• multiple asks in one message
• fake familiarity without specifics
• bulk-sent messages with visible merge fields
• identical copy reused for email, LinkedIn, and X
• platform-shaped slop instead of the author's actual voice

Configuration

Users should set these environment variables:

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# Required
export X_BEARER_TOKEN="..."
export X_ACCESS_TOKEN="..."
export X_ACCESS_TOKEN_SECRET="..."
export X_CONSUMER_KEY="..."
export X_CONSUMER_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

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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, voice profile summary, and channel-specific outreach drafts or drafts-in-app

Related Skills

• brand-voice for canonical voice capture
• connections-optimizer for review-first network pruning and expansion before outreach