RB2B Intent Triage

What this skill does

Takes a full RB2B export and routes each row to the correct enrichment action:

Rows are prioritised by intent signals before processing: AllTimePageViews, RecentPageCount, NewProfile, and Tags (e.g. "Hot page"). The output is a single ranked outreach list.

Chain

Full RB2B CSV
  to define ICP + company fit criteria
  to triage each row into buckets
  to enrich ICP visitors directly (linkedin_to_business_email per-URL parallel + linkedin_to_mobile_phone batch)
  to find ICP contacts at company-only + non-ICP-visitor accounts
      (search_people to linkedin_to_business_email per-URL parallel + linkedin_to_mobile_phone batch)
  to merge + rank by intent signal

Inputs

• RB2B CSV export - upload or paste (full export, any mix of Person/Company rows)
• ICP definition - role/title, seniority, department criteria
• Company fit criteria - optional industry, size, or revenue filter
• Acceptable risk scores - which A-F deliverability grades to keep (recommended: A, B, C and F; D excluded)

Output

Rows processed:       X  (X Person, X Company)
ICP visitors:         X  to enriched directly
Non-ICP visitors:     X  to X unique companies to X ICP contacts found
Company rows:         X  to X ICP contacts found
Total outreach list:  X contacts
Business emails:      X/N (XX%)
Mobile numbers:       X/N (XX%)

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.

Tips

• NewProfile = true = first visit - high urgency signal
• Tags like "Hot page" from RB2B surface in the priority ranking automatically
• AllTimePageViews + RecentPageCount together indicate sustained vs. spiking intent
• For a single workflow (not triage), use focused skills: Enrich RB2B Visitors, RB2B Companies to ICP Contacts, or RB2B Non-ICP Visitors to ICP Contacts
• 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