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, and personalized outreach. 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

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

1. Define target parameters (verticals, roles, locations)
2. Run Exa deep search for people and companies in each vertical
3. Run X API search for active voices on relevant topics
4. Score each result against the signal weights
5. Rank and deduplicate

Stage 2: Mutual Ranking

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

Algorithm

1. Pull user's X following list and LinkedIn connections
2. For each high-signal target, check for shared connections
3. Rank mutuals by:

Factor	Weight
Number of connections to targets	40%
Mutual's current role/company	20%
Mutual's location	15%
Industry alignment	15%
Mutual's identifiability (handle/profile)	10%

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, ...
    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. 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

Related Skills

• x-api -- X/Twitter API integration for graph analysis
• investor-outreach -- Investor-specific outreach patterns
• market-research -- Company and fund due diligence