# Lead intelligence Human Guide

## What This Is For
AI-native lead intelligence and outreach pipeline. It gives the agent a clearer input/output frame for paid media: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Lead intelligence agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for lead intelligence.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Lead intelligence guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- 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
- **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`)
- **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

## Decision Points And Nuance
The original skill emphasizes: When to Activate, Tool Requirements, Required, Optional (enhance results), Pipeline Overview, Voice Before Outreach, Stage 1: Signal Scoring, Signal Search Approach, Stage 2: Mutual Ranking, Ranking Model.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- 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.
- Create drafts first, do not send automatically unless the user explicitly asks
- Keep it shorter than email and avoid fake professional warmth
- Do not send messages automatically without explicit user approval.
- Only report verified data. Do not hallucinate company details.
- Do not assume connections exist based on similar bios or locations alone.
- **Personalize or don't send.** Every message must reference something specific to the recipient.

## Copy-And-Paste Prompt
```text
Use the Lead intelligence human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the affaan-m/ecc skill entry for `lead-intelligence`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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

```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
```