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

---
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, and personalized outreach. Use when the user wants to find, qualify, and reach high-value contacts.
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`, `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

```python
# 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

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% — 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 |

### Weighted Bridge Ranking

Treat this as the canonical network-ranking stage for lead intelligence. Do not run a separate graph skill when this stage is enough.

Given:
- `T` = target leads
- `M` = your mutuals / existing connections
- `d(m, t)` = shortest hop distance from mutual `m` to target `t`
- `w(t)` = target weight from signal scoring

Compute the base bridge score for each mutual:

```text
B(m) = Σ_{t ∈ T} w(t) · λ^(d(m,t) - 1)
```

Where:
- `λ` is the decay factor, usually `0.5`
- a direct connection contributes full value
- each extra hop halves the contribution

For second-order reach, expand one level into the mutual's own network:

```text
B_ext(m) = B(m) + α · Σ_{m' ∈ N(m) \\ M} Σ_{t ∈ T} w(t) · λ^(d(m',t))
```

Where:
- `N(m) \\ M` is the set of people the mutual knows that you do not
- `α` is the second-order discount, usually `0.3`

Then rank by response-adjusted bridge value:

```text
R(m) = B_ext(m) · (1 + β · engagement(m))
```

Where:
- `engagement(m)` is a normalized responsiveness score
- `β` is the engagement bonus, usually `0.2`

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

```
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. 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:

```bash
# 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
```