Find a Contact's Email

What this skill does

Optimised single-person lookup: takes a name and company and returns the verified business email (plus the matched LinkedIn profile) in one call, falling back to a company search and name match when needed. About 10 seconds total.

Don't use this if you already have a LinkedIn URL - use Enrich Contact instead.

Inputs

• Name - full name of the person
• Company - name or domain (domain inferred for well-known companies)
• Acceptable risk scores - which A-F deliverability grades to keep (recommended: A, B, C and F; D excluded)

When it activates

• "find contact for [Name] at [Company]"
• "get the email for [Name]"
• "who is [Name] at [Company]"

How it works

1. Resolves company name to domain (asks if unknown)
2. Looks up the business email by name + company in one call
3. If that misses, searches contacts at the company and matches the name
4. Returns the business email (and matched LinkedIn URL)

Output

Jane Smith - Head of Marketing, Acme Corp
LinkedIn: https://www.linkedin.com/in/janesmith
Business email: jane@acmecorp.com  risk score: A (validated 2024-11-03)

The risk score is shown on every email returned, including grades you excluded - flagged, never silently dropped.

After results, offers to also check personal email or mobile.

Search filters

search_people filters on query, company, company_domain, country, state, city, seniority, department or functional_area, industry, linkedin_industry, naics_code, employee_range, and revenue_range.

• Filters are free precision - execution cost comes from the free-text query, so a filters-only search runs ~40x cheaper and lands more precisely. A bare query is never the right shape; at minimum it gets country attached (~99% fill rate).
• Size and revenue apply to the person's current employer - "VPs at 51-200 employee software companies" is one call, not a company search followed by a people search.
• Three industry vocabularies - broad industry buckets, LinkedIn's ~150 niche labels, or a NAICS code at any depth. Shortening the code widens the net.
• Sparse filters are the usual cause of a thin result set - seniority/industry/department are ~60% filled and naics_code/linkedin_industry ~50%, and every filter is exact-match, silently dropping records where the field is empty.
• Sizing is free - count_only: true returns a match count without fetching data or charging tokens, and every response carries results.total to read before paginating.

Tips

• Search results already carry the business email - if present for the matched person, no separate enrich call is needed
• For multiple people, Find and Enrich handles bulk lookups more efficiently
• If the person isn't on the first page, pages deeper or retries by company name before giving up
• Every returned email carries an A-F deliverability risk score; you're asked up front which grades to keep, and out-of-range grades are shown and flagged rather than silently dropped