Job Market And Recruitment

When: User wants to find and analyze job postings by role, company, or location.

Pipeline

1. Search jobs -> harvestapi/linkedin-job-search
   • Key input: keyword, location, datePosted, limit
2. Get job details -> apimaestro/linkedin-job-detail
   • Pipe: results[].jobUrl -> urls
   • Key input: urls

Output fields

Step 1: title, company, location, jobUrl, postedDate, applicantsCount Step 2: description, requirements, seniority, employmentType, salary

Gotcha

Both Actors are PPE. Step 1: ~$0.001/job. Step 2: ~$0.005/job. For 200 jobs, total ~$1.20. Estimate and confirm with user.

Candidate sourcing

When: User wants to find potential candidates matching specific criteria.

Pipeline

1. Search profiles -> harvestapi/linkedin-profile-search
   • Key input: keyword, title, location, industry, limit
2. Enrich with details -> apimaestro/linkedin-profile-full-sections-scraper
   • Pipe: results[].profileUrl -> urls
   • Key input: urls

Output fields

Step 1: fullName, headline, location, profileUrl, currentCompany Step 2: experience[], education[], skills[], certifications[], languages[]

Gotcha

Step 2 (apimaestro/linkedin-profile-full-sections-scraper) costs ~$0.01/profile - the most expensive LinkedIn scraper. Use sparingly for shortlisted candidates only.

Sales signal outreach - job posting as buying signal

When: User wants to monitor company job postings as a signal to identify sales opportunities - e.g., a “Head of Data Engineering” hire suggests budget for data tooling.

Pipeline

1. Monitor target postings -> harvestapi/linkedin-job-search
   • Key input: searchUrl (LinkedIn Jobs URL with company or role filters), keywords
2. Get company context -> harvestapi/linkedin-company
   • Pipe: results[].companyUrl -> companyUrls

Output fields

Step 1: title, companyName, description, employmentType, seniorityLevel, jobUrl Step 2: name, industry, employeeCount, description, specialties[]

Gotcha

Job descriptions contain implicit buying signals - tech stack mentions, pain points, and headcount growth. Pass description to an LLM to extract inferred tech stack and budget tier before prioritizing outreach. Contact finding (Hunter.io) uses the native n8n node, not an Apify Actor.

Upwork job monitoring for freelancers

When: User wants to continuously monitor Upwork for new jobs matching their skills.

Pipeline

1. Scrape Upwork search -> apify/playwright-scraper
   • Key input: startUrls (Upwork search URL with skill filters), pseudoUrls, maxCrawledPages

Output fields

Step 1: title, description, budget, clientJobsPosted, clientHireRate, postedAt, url

Gotcha

No dedicated Upwork Actor exists in Apify Store - verify with apify actors search "upwork" --user-agent apify-agent-skills/apify-ultimate-scraper for community options before defaulting to apify/playwright-scraper. Upwork pages are JS-heavy so Playwright is required over basic HTTP scraping. For high-frequency monitoring (every 15 min), store seen job URLs to avoid re-processing duplicates.

GitHub contributor discovery

When: User wants to find developers who contribute to specific open-source projects.

Pipeline

1. Get contributors -> janbuchar/github-contributors-scraper
   • Key input: repoUrls

Output fields

Step 1: username, contributions, profileUrl, avatarUrl