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Playbook

Use Apollo as the default high-recall people/company prospector.
  • Keep include_similar_titles=true unless the user explicitly asks for strict title matching.
  • For broad discovery, start with person_seniorities + q_keywords and only tighten after you inspect totals.
  • Prefer keyword-style constraints in q_keywords and q_organization_name over overly narrow exact strings.
  • Use low per_page for pilot checks, then scale once payload shape and match quality are confirmed.
  • For changed-company email recovery specifically, do not force Apollo first; prefer the scenario default order from the GTM meta skill.
Obfuscated last-name handling (for email pattern workflows):
  • Detect redacted/obfuscated last names early (for example: S****, K., -, N/A, redacted, masked punctuation-heavy strings).
  • Treat last_name_obfuscated from apollo_people_search as non-authoritative for name-based email finding.
  • Do not pass obfuscated last names into leadmagic_email_finder or pattern generators.
  • Required bridge step: apollo_people_search -> apollo_people_match (by Apollo id) -> use person.last_name for name-dependent flows.
  • If last name is obfuscated, do not rely on first.last / first.lastInitial / firstInitial.lastInitial patterns as primary candidates.
  • Prefer fallback order: direct provider email fields and enrichment lookups first (Apollo/person enrichment/LinkedIn-based enrichment), then emit pattern candidates only when confidence is acceptable.
  • Persist deterministic flags for downstream branching, for example last_name_obfuscated=true and name_quality=low|medium|high.
  • Keep recall-first behavior: obfuscation checks should gate pattern generation quality, not force strict matching globally.
deepline tools get apollo_search_people --json

deepline tools execute apollo_search_people --payload '{"filters":{"person_seniorities":["vp","head","director"],"q_keywords":"b2b saas growth marketing","include_similar_titles":true},"limit":5,"offset":0}' --json

deepline enrich --input leads.csv --output leads.csv.out.csv --with 'people=apollo_people_search:{"q_organization_domains_list":["{{domain}}"],"person_titles":["{{title}}"],"include_similar_titles":true,"per_page":1,"page":1}' --json