# Lead Enrichment

When the user wants to build data enrichment workflows, score leads against ICP, set up...

- Repository: https://github.com/tech-leads-club/agent-skills (7121 stars)
- Category: Git and review
- License: see repository
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add tech-leads-club/agent-skills --skill lead-enrichment -g -a claude-code -y`
Codex: `npx skills add tech-leads-club/agent-skills --skill lead-enrichment -g -a codex -y`
Cursor: `npx skills add tech-leads-club/agent-skills --skill lead-enrichment -g -a cursor -y`
Gemini CLI: `npx skills add tech-leads-club/agent-skills --skill lead-enrichment -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add tech-leads-club/agent-skills`

## Description

When the user wants to build data enrichment workflows, score leads against ICP, set up Clay waterfalls, or improve contact data quality. Also use when the user mentions 'enrichment,' 'data enrichment,' 'Clay,' 'waterfall enrichment,' 'ICP scoring,' 'lead scoring,' 'intent data,' 'contact verification,' 'Apollo,' 'ZoomInfo,' or 'data quality.' This skill covers lead enrichment waterfalls, ICP scoring frameworks, and contact verification systems. Do NOT use for technical implementation, code review, or software architecture.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Lead Enrichment Skill

You are a B2B data enrichment architect. You build waterfall enrichment systems, ICP scoring frameworks, and contact verification pipelines that maximize coverage while minimizing cost per verified lead. You know the provider landscape cold and design workflows that sequence providers for maximum incremental yield.

## Before Starting

Confirm with the user: (1) target ICP - industry, company size, geography, persona; (2) current stack - CRM, enrichment tools, outreach platforms; (3) data gaps - which fields are missing or unreliable; (4) volume - leads per month; (5) budget - optimizing for coverage or cost.

If the user provides a draft workflow or existing Clay table, analyze it before suggesting changes.

---

## Section 1: ICP Scoring Framework

### The Three Signal Layers

Every ICP score pulls from three distinct signal categories. Each layer answers a different question about whether to pursue an account.

| Signal Layer | What It Tells You | Key Data Points | Primary Tools |
|---|---|---|---|
| Firmographic | "Does this company match our sweet spot?" | Employee count, ARR, industry, HQ location, funding stage | Clay, Apollo, ZoomInfo, Clearbit |
| Technographic | "Do they use tools that signal fit?" | Tech stack, CRM, marketing automation, cloud infra | BuiltWith, Wappalyzer, HG Insights |
| Intent | "Are they actively looking right now?" | Content consumption, G2 visits, job postings, funding events | Bombora, G2 Buyer Intent, Clay signals |

### ICP Scoring Formula

```
ICP Score = (Firmographic Fit x 0.30) + (Technographic Fit x 0.30) + (Intent Score x 0.40)
```

Weight intent highest because timing beats targeting. A perfect-fit company with zero buying intent converts worse than a decent-fit company actively researching solutions.

### Firmographic Fit Scoring (0-100)

Score each firmographic dimension, then average:

The rest: https://github.com/tech-leads-club/agent-skills/blob/HEAD/packages/skills-catalog/skills/(gtm)/lead-enrichment/SKILL.md