Skill: hundevmode/apollo-like-leads-apify (version 1.0.0)

--- name: apollo-like-leads-apify description: 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. required_env_vars: - APIFY_TOKEN required-env-vars: - APIFY_TOKEN primary_credential: APIFY_TOKEN primary-credential: APIFY_TOKEN metadata: short-description: Run Apollo-like B2B leads actor via Apify openclaw: requires: env: - APIFY_TOKEN primaryCredential: APIFY_TOKEN --- # 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: ```bash export APIFY_TOKEN='apify_api_xxx' ``` Alternative: ```bash 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) ```bash APIFY_TOKEN='apify_api_xxx' \ python3 scripts/apollo_like_leads_actor.py quick-founders-us-50 ``` ### 2) Custom run from inline JSON ```bash 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 ```bash 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`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)