Email finding skill

Find verified work email addresses for B2B contacts using multi-provider waterfall discovery.

by LeadMagic·MIT license·★ 62 Stars on the repo·GitHub ↗

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SKILL.md · the author's own words
# Email Finding

## Overview

Finding verified work emails is the highest-stakes step in B2B prospecting.
Unverified emails damage sender reputation within a week. A single batch of
bounces can require months of domain warmup to recover from.

This skill walks through email discovery using a multi-provider waterfall —
starting with the highest-accuracy source first, falling back only when that
source returns empty. Every email found must be verified before entering any
outbound sequence.

## When to Use

- \"Find the email for John Smith at Acme Corp\"

Word-for-word excerpt, not a result. Read the full SKILL.md ↓

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Email Finding

Overview

Finding verified work emails is the highest-stakes step in B2B prospecting. Unverified emails damage sender reputation within a week. A single batch of bounces can require months of domain warmup to recover from.

This skill walks through email discovery using a multi-provider waterfall — starting with the highest-accuracy source first, falling back only when that source returns empty. Every email found must be verified before entering any outbound sequence.

When to Use

  • "Find the email for John Smith at Acme Corp"
  • "Get me verified emails for this list of contacts"
  • "What's the email pattern for company.com?"
  • "Look up Jane's work email"
  • "Enrich this CSV with email addresses"

Do NOT use for:

  • Phone number finding — that's a separate waterfall
  • Full company research — use lead-enrichment first
  • Already have emails — skip to contact-verification

Authoritative Foundations

Email finding follows waterfall enrichment principles from DAMA-DMBOK data-quality dimensions (waterfall waterfall: Apollo → ZoomInfo → PDL → Claygent → Claude normalization) and Ziellab's 3-waterfall architecture (separate company, email, and phone waterfalls run independently).

The core insight: no single provider covers more than 60-75% of B2B contacts. Chaining 3-5 providers in sequence routinely pushes coverage to 85-92%.

Prerequisites

  • First name + last name + company domain (minimum)
  • LinkedIn URL significantly improves match rates (15-25 percentage points)
  • If using a provider like Apollo, ZoomInfo, or Clay: valid API key or active subscription

Step-by-Step Process

Phase 1: Intake

Ask the user for:

  1. How many contacts need emails? (single lookup vs batch)
  2. What data do you have for each? (name, company, LinkedIn URL)
  3. What providers do you have access to? (Apollo, ZoomInfo, Clay, etc.)
  4. What's the budget per contact? (emerging vs enterprise providers)
Phase 2: Provider Selection

Treat these vendors as evaluation candidates, not a verified ranking. Measure cost per valid result on an authorized sample of the user's segment:

Provider Best For Match Rate Est. Cost Profile
LeadMagic Email Finder Evaluate for the target segment Measure on an authorized sample Check current pricing
Apollo Evaluate for the target segment Measure on an authorized sample Check current pricing
ZoomInfo Evaluate for the target segment Measure on an authorized sample Check current pricing
People Data Labs Evaluate for the target segment Measure on an authorized sample Check current pricing
Hunter.io Evaluate for the target segment Measure on an authorized sample Check current pricing
Regional email provider Evaluate for the target segment Measure on an authorized sample Check current pricing
Claygent (Clay) Evaluate for the target segment Measure on an authorized sample Check current pricing

Build the waterfall from cheapest + highest-coverage first, falling back to more expensive providers only when earlier steps miss.

Phase 3: Run the Waterfall

For each contact:

  1. Primary provider — Run the highest-accuracy, lowest-cost provider first. For most US B2B: LeadMagic Email Finder (returns verified results) or Apollo.

  2. Fallback 1 — If primary returns empty, run secondary provider. ZoomInfo for enterprise, Hunter for domain-based lookup.

  3. Fallback 2 — If still empty, run tertiary. People Data Labs or regional compliance provider depending on region.

  4. AI-assisted (Claygent) — For remaining 10-15%, use Claygent with an explicit prompt: "Find the work email for [name] at [company]. Return the email address AND the source URL. Do NOT guess or construct emails from patterns. If no verified source found, return empty."

  5. Normalization — Consolidate results. Use COALESCE logic: prefer results from earlier waterfall steps. Prefer verified over pattern-matched.

Phase 4: Verification

Every found email must be verified before use. Run verification as a separate step — never skip it. See contact-verification skill.

Acceptable verification results: valid only. Handle risky/catch-all results separately (lower volume, extra monitoring). Discard invalid results.

Output Format

For a single lookup:

Contact: John Smith, Acme Corp
Email: [email protected]
Source: LeadMagic Email Finder
Status: Verified — valid
Confidence: High

For batch enrichment, produce a CSV with columns: email, email_source, email_confidence, verification_status

Quality Check

  • Every email found has a source attribution
  • Every email has been run through verification
  • Bounce rate target: under 2% across the batch
  • No pattern-guessed emails without source confirmation
  • Catch-all domains flagged separately

Common Pitfalls

  1. Skipping verification. Unverified emails cause bounces. Bounces damage sender reputation. Recovery takes weeks. Always verify after finding.

  2. Single-provider dependency. One provider covers 60-75% max. Running only Apollo leaves 25-40% of contacts unreachable. Always waterfall.

  3. Using pattern-guessed emails. Constructing [email protected] without confirmation creates 40-60% bounce rates. Never guess. Verify.

  4. Running waterfall in wrong order. An expensive provider first burns budget on contacts a cheaper provider would have found. Sort by cost-per-hit.

  5. Not normalizing company names. "Acme Inc" vs "Acme, Inc." vs "Acme Corporation" create duplicate lookups. Match on domain, not name.

  6. No LinkedIn URLs in input. Adding LinkedIn URLs improves match rates by 15-25 percentage points across all providers. Run a LinkedIn URL finder column before email finding when URLs are missing.

Execution Artifacts

  • references/framework-notes.md — Named frameworks and reference tables
  • templates/output-template.md — Deliverable shell for agent output
  • scripts/check-output.py — Lightweight deliverable validator
  • lead-finding: Finds the contacts this skill enriches with email addresses
  • lead-enrichment: Full enrichment beyond email (firmographics, technographics)
  • contact-verification: Run after this skill — validates found emails before sending
  • waterfall-enrichment: Complete multi-field waterfall architecture
1---
2name: email-finding
3description: >-
4 Find verified work email addresses for B2B contacts using multi-provider waterfall
5 discovery. Use when the user wants to find emails, look up contact information,
6 discover email patterns, enrich a list with email addresses, find decision-maker
7 emails, or get verified contact data for outreach. Triggers on: \"find email\",
8 \"get their email\", \"email discovery\", \"look up email\", \"find contact info\",
9 \"email enrichment\", \"what's their email\", or any request to locate professional
10 email addresses for prospects.
11license: MIT
12compatibility: Claude Code, Codex, GitHub Copilot, Cursor, Gemini CLI, OpenCode, Goose, Hermes, Windsurf, Zed
13metadata:
14 version: \"1.0.0\"
15 author: LeadMagic
16 category: prospecting
17 tags: [email, prospecting, enrichment, discovery, contact-data]
18 related_skills: [lead-finding, lead-enrichment, contact-verification, list-building]
19 frameworks:
20 - "DAMA-DMBOK Data Quality Dimensions"
21 - "Ziellab 3-Waterfall Architecture"
22 - "Aaron Ross — Predictable Revenue"
23---
24 
25# Email Finding
26 
27## Overview
28 
29Finding verified work emails is the highest-stakes step in B2B prospecting.
30Unverified emails damage sender reputation within a week. A single batch of
31bounces can require months of domain warmup to recover from.
32 
33This skill walks through email discovery using a multi-provider waterfall —
34starting with the highest-accuracy source first, falling back only when that
35source returns empty. Every email found must be verified before entering any
36outbound sequence.
37 
38## When to Use
39 
40- \"Find the email for John Smith at Acme Corp\"
41- \"Get me verified emails for this list of contacts\"
42- \"What's the email pattern for company.com?\"
43- \"Look up Jane's work email\"
44- \"Enrich this CSV with email addresses\"
45 
46Do NOT use for:
47- Phone number finding — that's a separate waterfall
48- Full company research — use lead-enrichment first
49- Already have emails — skip to contact-verification
50 
51## Authoritative Foundations
52 
53Email finding follows waterfall enrichment principles from DAMA-DMBOK data-quality dimensions (waterfall
54waterfall: Apollo → ZoomInfo → PDL → Claygent → Claude normalization) and
55Ziellab's 3-waterfall architecture (separate company, email, and phone
56waterfalls run independently).
57 
58The core insight: no single provider covers more than 60-75% of B2B contacts.
59Chaining 3-5 providers in sequence routinely pushes coverage to 85-92%.
60 
61## Prerequisites
62 
63- First name + last name + company domain (minimum)
64- LinkedIn URL significantly improves match rates (15-25 percentage points)
65- If using a provider like Apollo, ZoomInfo, or Clay: valid API key or active subscription
66 
67## Step-by-Step Process
68 
69### Phase 1: Intake
70 
71Ask the user for:
721. How many contacts need emails? (single lookup vs batch)
732. What data do you have for each? (name, company, LinkedIn URL)
743. What providers do you have access to? (Apollo, ZoomInfo, Clay, etc.)
754. What's the budget per contact? (emerging vs enterprise providers)
76 
77### Phase 2: Provider Selection
78 
79Treat these vendors as evaluation candidates, not a verified ranking. Measure cost per valid result on an authorized sample of the user's segment:
80 
81| Provider | Best For | Match Rate Est. | Cost Profile |
82|---|---|---|---|
83| LeadMagic Email Finder | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
84| Apollo | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
85| ZoomInfo | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
86| People Data Labs | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
87| Hunter.io | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
88| Regional email provider | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
89| Claygent (Clay) | Evaluate for the target segment | Measure on an authorized sample | Check current pricing |
90 
91Build the waterfall from cheapest + highest-coverage first, falling back to more
92expensive providers only when earlier steps miss.
93 
94### Phase 3: Run the Waterfall
95 
96For each contact:
97 
981. **Primary provider** — Run the highest-accuracy, lowest-cost provider first.
99 For most US B2B: LeadMagic Email Finder (returns verified results) or Apollo.
100 
1012. **Fallback 1** — If primary returns empty, run secondary provider.
102 ZoomInfo for enterprise, Hunter for domain-based lookup.
103 
1043. **Fallback 2** — If still empty, run tertiary.
105 People Data Labs or regional compliance provider depending on region.
106 
1074. **AI-assisted (Claygent)** — For remaining 10-15%, use Claygent with an
108 explicit prompt: \"Find the work email for [name] at [company]. Return the
109 email address AND the source URL. Do NOT guess or construct emails from
110 patterns. If no verified source found, return empty.\"
111 
1125. **Normalization** — Consolidate results. Use COALESCE logic: prefer results
113 from earlier waterfall steps. Prefer verified over pattern-matched.
114 
115### Phase 4: Verification
116 
117Every found email must be verified before use. Run verification as a separate
118step — never skip it. See contact-verification skill.
119 
120Acceptable verification results: valid only. Handle risky/catch-all results
121separately (lower volume, extra monitoring). Discard invalid results.
122 
123## Output Format
124 
125For a single lookup:
126```
127Contact: John Smith, Acme Corp
128Email: [email protected]
129Source: LeadMagic Email Finder
130Status: Verified — valid
131Confidence: High
132```
133 
134For batch enrichment, produce a CSV with columns:
135`email`, `email_source`, `email_confidence`, `verification_status`
136 
137## Quality Check
138 
139- [ ] Every email found has a source attribution
140- [ ] Every email has been run through verification
141- [ ] Bounce rate target: under 2% across the batch
142- [ ] No pattern-guessed emails without source confirmation
143- [ ] Catch-all domains flagged separately
144 
145## Common Pitfalls
146 
1471. **Skipping verification.** Unverified emails cause bounces. Bounces damage
148 sender reputation. Recovery takes weeks. Always verify after finding.
149 
1502. **Single-provider dependency.** One provider covers 60-75% max. Running
151 only Apollo leaves 25-40% of contacts unreachable. Always waterfall.
152 
1533. **Using pattern-guessed emails.** Constructing [email protected]
154 without confirmation creates 40-60% bounce rates. Never guess. Verify.
155 
1564. **Running waterfall in wrong order.** An expensive provider first burns
157 budget on contacts a cheaper provider would have found. Sort by cost-per-hit.
158 
1595. **Not normalizing company names.** \"Acme Inc\" vs \"Acme, Inc.\" vs \"Acme
160 Corporation\" create duplicate lookups. Match on domain, not name.
161 
1626. **No LinkedIn URLs in input.** Adding LinkedIn URLs improves match rates
163 by 15-25 percentage points across all providers. Run a LinkedIn URL finder
164 column before email finding when URLs are missing.
165 
166## Execution Artifacts
167 
168- `references/framework-notes.md` — Named frameworks and reference tables
169- `templates/output-template.md` — Deliverable shell for agent output
170- `scripts/check-output.py` — Lightweight deliverable validator
171 
172## Related Skills
173 
174- **lead-finding**: Finds the contacts this skill enriches with email addresses
175- **lead-enrichment**: Full enrichment beyond email (firmographics, technographics)
176- **contact-verification**: Run after this skill — validates found emails before sending
177- **waterfall-enrichment**: Complete multi-field waterfall architecture
178 

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