everything-claude-code/skills/lead-intelligence/SKILL.md

name, description, origin

name	description	origin
lead-intelligence	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.	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 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:

# 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

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