Find Email by Name & Company

What this skill does

Resolves a person from their name and company. Prefers search_linkedin_profile (name + company in one call); falls back to search_people (searching by name and filtering by company) when needed. Returns the matching profile for you to confirm - then hands off to Enrich Contact to retrieve email and phone.

Use this when you want to find someone's LinkedIn profile. If you already have the URL, skip straight to Enrich Contact.

Inputs

• Name - full name (or first + last separately)
• Company domain - e.g. acme.com (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 me [Name]"
• "look up [Name] at [Company]"
• "can you find [Name]"
• "search for someone named [Name]"

Note: you can search by name directly - the preferred call takes name + company. The search_people fallback also searches names (via its free-text query) and can filter by seniority, department, or country.

How it works

1. Resolves company name to domain
2. Looks up the person by name + company (paging deeper on the search_people fallback if needed)
3. Returns the match (or a numbered list if multiple) for confirmation
4. On confirmation, hands off to Enrich Contact

Output - single match

Jane Smith | Head of Marketing | Acme Corp
https://www.linkedin.com/in/janesmith
Email on file: jane@acmecorp.com  risk score: A

Is this the right person? I can pull their contact info if so.

When the search record already carries a business_email, it's shown with its A-F risk score - flagged if the grade falls outside the set you accepted.

Output - multiple matches

Numbered list of Name | Title | Company | LinkedIn URL - you pick one.

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 include the business email - if it's present for the matched person, it's in hand without a separate enrich call
• If the first page doesn't contain the person, the skill pages deeper or retries by company name before giving up
• Only a first name with no domain to resolve the company first; if the match is ambiguous, asks for more detail
• 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