Build an Outbound Prospect List

What this skill does

Full end-to-end ICP-to-contact-list workflow. Define your ICP, search for people by company and people attributes (industry, domain, seniority, department, country), enrich the gaps with business email and mobile, and get a clean exportable list ready for outreach.

Before searching, the skill gates on input quality - if nothing scopes the search, it asks for more detail rather than burning credits on a vague search.

Chain

company + people ICP criteria
  to search_people  (find matching people)
  to linkedin_to_business_email     (per-URL parallel calls - business emails)
  to linkedin_to_mobile_phone              (batch array - mobile, optional)

Inputs - at least one filter

How it works

1. Confirms at least one filter is present - prompts if under-specified
2. Runs a free credit check and estimates total cost
3. Probe search on 1 result to verify quality before scaling
4. Paginates to target count (25 results per call)
5. Deduplicates, then enriches - email as per-URL parallel calls, phone as one batch array call
6. Outputs exportable table

Output

Prospects found:   X
Business emails:   X/N (XX%)
Mobile numbers:    X/N (XX%)
Credits used:      ~XXX

Email risk scores:
  A: X   B: X   C: X   D: X   F: X
  kept (accepted grades):  X
  filtered (excluded):     X

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.

Company-side filters

search_companies filters on query (company name only), domain, industry, linkedin_industry, naics_code, employee_range, revenue_range, country, and state.

• domain beats free text every time - exact match, and full URLs are normalised automatically, so https://www.acme.com/about needs no cleanup first.
• Filters are free precision - a filters-only search runs ~40x cheaper than one carrying free text, and most firmographic work needs no query at all.
• Industry vocabularies overlap - the same company can sit under "Information Technology" or "Computer Software", so a thin result is often a neighbouring-value problem rather than a coverage one.
• Company location is derived from where the team is based, and large multinationals resolve to a single one of their offices - pair location with another filter on enterprise or international targeting.
• Range values are exact and skewed - "1-10" dominates employee counts, "$500k - $1M" dominates revenue, and the legacy "$1 - $1M" band holds far fewer records than its numeric equivalent.

Tips

• Industry + seniority + department is a strong role-based targeting combination; use company_domain (not domain) to scope search_people to one company
• If filters produce fewer results than your target, the skill suggests broadening one constraint at a time
• 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