Назад към всички

apollo-like-leads-apify

// Use this skill when the user needs B2B lead collection via Apify actor LurATYM4hkEo78GVj (Apollo-like), including filter-based payload building, validated run execution, and JSON/CSV-ready lead output for outreach workflows.

$ git log --oneline --stat
stars:1,933
forks:367
updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nameapollo-like-leads-apify
descriptionUse this skill when the user needs B2B lead collection via Apify actor LurATYM4hkEo78GVj (Apollo-like), including filter-based payload building, validated run execution, and JSON/CSV-ready lead output for outreach workflows.
required_env_varsAPIFY_TOKEN
required-env-varsAPIFY_TOKEN
primary_credentialAPIFY_TOKEN
primary-credentialAPIFY_TOKEN
metadata[object Object]

Apollo-like B2B Leads (Apify Actor)

Overview

This skill runs the Apify actor LurATYM4hkEo78GVj to collect Apollo-style B2B leads with filters such as job title, seniority, location, employee size, industry, and email quality.

Actor link:

  • https://console.apify.com/actors/LurATYM4hkEo78GVj/source

Use this skill when a user asks to:

  • collect B2B contacts similar to Apollo workflows
  • fetch leads with verified emails and optional phones
  • build payloads for founders/execs by geo and industry
  • run repeatable lead collection from script/API

Scope

  • Build validated actor input payloads.
  • Run actor with secure token handling (APIFY_TOKEN env or --apify-token).
  • Return normalized summary and raw lead rows.
  • Support quick preset runs and custom JSON input.

Step-by-step workflow

  1. Confirm target ICP (titles, seniority, location, company size, industries).
  2. Build payload with required lead count and enrichment switches.
  3. Run actor using scripts/apollo_like_leads_actor.py.
  4. Validate lead count and inspect sample rows.
  5. Export rows to n8n/Sheets/CSV as needed.

Authentication

Preferred:

export APIFY_TOKEN='apify_api_xxx'

Alternative:

python3 scripts/apollo_like_leads_actor.py run \
  --apify-token 'apify_api_xxx' \
  --input-json '{"max_results":50,"person_location_country":["United States"]}'

Quick start commands

1) Preset: 50 US founders (verified emails)

APIFY_TOKEN='apify_api_xxx' \
python3 scripts/apollo_like_leads_actor.py quick-founders-us-50

2) Custom run from inline JSON

APIFY_TOKEN='apify_api_xxx' \
python3 scripts/apollo_like_leads_actor.py run \
  --input-json '{
    "max_results": 1000,
    "job_titles": ["CEO", "Founder", "Co-Founder"],
    "job_title_seniority": ["owner", "cxo"],
    "person_location_country": ["United States"],
    "employee_size": ["11-50", "51-200", "201-500"],
    "email_status": "verified",
    "include_emails": true,
    "include_phones": false
  }'

3) Custom run from JSON file

APIFY_TOKEN='apify_api_xxx' \
python3 scripts/apollo_like_leads_actor.py run \
  --input-file references/sample_input.json

Output contract

Script returns JSON with:

  • ok
  • actorId
  • leadsCount
  • inputUsed
  • rows[]

You can pass rows directly to n8n HTTP/Code nodes or map into Google Sheets columns.

Important rules

  • Do not hardcode API keys in workflow templates.
  • Keep max_results realistic for testing first (e.g., 50-200).
  • Use email_status: "verified" for higher outreach quality.
  • If the user wants phone-heavy output, set include_phones: true explicitly.
  • Seniority values should match actor enum (owner, cxo, vp, director, etc.); this script auto-normalizes common Apollo values like founder -> owner.

References

  • references/actor-input-guide.md
  • references/troubleshooting.md