Job Scraper

Search for job postings across LinkedIn and Indeed using Apify. Find open roles by keyword, location, company, or job type. Use for hiring signal detection, GTM research, or competitive intelligence.

No LinkedIn cookies. No Indeed login. Just search queries in, structured job data out.

When to Auto-Load

Load this skill when:

• User says "find jobs", "who is hiring", "what roles is [company] hiring for"
• User wants hiring signals ("find companies growing their AI team")
• User wants competitive intelligence ("what is [competitor] hiring for")
• User says "job search", "open roles", "job listings", "job postings"

Prerequisites

Apify API Token

Required for both LinkedIn and Indeed scraping. Set in .env:

APIFY_API_TOKEN=your_token_here

No LinkedIn cookies, Indeed login, or any platform credentials needed. That's the only setup.

Sources

This skill searches two job platforms via Apify actors:

Source Selection Logic

Do NOT ask the user which source to use unless genuinely ambiguous. Decide based on context:

1. User specifies a source → use that source only.
2. Context strongly suggests one source:
   • B2B/tech/SaaS roles, enterprise companies, seniority-level filtering → LinkedIn
   • Hourly/blue-collar roles, local/retail jobs, salary-focused search → Indeed
   • Company hiring research ("what is Stripe hiring for") → LinkedIn (better company filtering)
3. No clear signal → search both sources, deduplicate results by job title + company name, present combined results.

After deciding, tell the user which source(s) you're searching and why. Don't ask — inform.

Workflow

Phase 0: Understand the Request

Extract from the user's message:

• Search term — job title, role, or keyword (required)
• Location — city, state, country, or "Remote" (optional)
• Company — specific company name (optional)
• Recency — "recent", "last week", "last 30 days" (optional)
• Job type — fulltime, parttime, contract, internship (optional)
• Remote — whether to filter for remote jobs (optional)
• Result count — how many results they want (default: 25)

If anything is ambiguous, pick reasonable defaults and tell the user what you chose. Do not ask clarifying questions for things you can reasonably infer.

Phase 1: Search

LinkedIn — automation-lab/linkedin-jobs-scraper

API call:

curl -X POST "https://api.apify.com/v2/acts/automation-lab~linkedin-jobs-scraper/runs?token=$APIFY_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQuery": "AI engineer",
    "location": "San Francisco",
    "maxItems": 25
  }'

Input fields:

Polling for results:

# Check run status (poll every 10s)
curl "https://api.apify.com/v2/acts/automation-lab~linkedin-jobs-scraper/runs/{RUN_ID}?token=$APIFY_API_TOKEN"
 
# When status is SUCCEEDED, fetch results
curl "https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN"

Output fields per job:

• title — Job title
• companyName — Company name
• companyLinkedinUrl — Company LinkedIn page
• companyLogo — Logo URL
• location — City, state
• salary — Salary text (when available)
• employmentType — Full-time, Part-time, Contract, etc.
• seniorityLevel — Entry, Mid-Senior, Director, Executive, etc.
• jobFunction — Engineering, Sales, Marketing, etc.
• industries — Industry classification
• descriptionText — Full job description (plain text)
• descriptionHtml — Full job description (HTML)
• applicantsCount — Number of applicants
• postedAt — When posted (e.g., "6 days ago")
• url — Direct link to the LinkedIn job posting
• applyUrl — Direct apply URL

Indeed — borderline/indeed-scraper

API call:

curl -X POST "https://api.apify.com/v2/acts/borderline~indeed-scraper/runs?token=$APIFY_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "AI engineer",
    "location": "San Francisco, CA",
    "country": "us",
    "maxResults": 25
  }'

Input fields:

Important: The country field is required for Indeed. If the user doesn't specify a country, default to us. Use lowercase 2-letter codes only.

Output fields per job:

• title — Job title
• companyName — Company name
• companyDescription — Company description
• companyNumEmployees — Company size
• companyRevenue — Company revenue range
• companyUrl — Company Indeed page
• location — Object with city, postalCode, country, formattedAddressShort, latitude, longitude, streetAddress
• salary — Object with salaryCurrency, salaryMin, salaryMax, salaryText, salaryType (hourly/yearly)
• descriptionText — Full job description (plain text)
• descriptionHtml — Full job description (HTML)
• datePublished — Posted date (YYYY-MM-DD)
• age — Human-readable age ("24 days ago")
• expired — Whether job is still active
• isRemote — Remote flag
• jobType — Employment type
• jobUrl — Direct Indeed job URL
• applyUrl — Direct apply URL
• rating — Company rating and review count
• emails — Contact emails (when available)
• attributes — Job attributes list (benefits, requirements, etc.)
• hiringDemand — Urgent hire / high volume hiring flags

Phase 2: Filter & Deduplicate

Recency Filtering

If the user asked for recent jobs, filter results by date:

• LinkedIn: Use postedAt field (e.g., "6 days ago") — parse the text to determine recency.
• Indeed: Use datePublished field (YYYY-MM-DD) — compare against today's date.

Remove jobs older than what the user requested. If no recency filter specified, still remove jobs older than 30 days by default to avoid stale data.

Deduplication (when using both sources)

When searching both LinkedIn and Indeed, the same job may appear on both platforms. Deduplicate by matching:

1. Normalize company name (lowercase, strip "Inc", "LLC", "Corp", etc.)
2. Normalize job title (lowercase)
3. If company name AND job title match, keep the result with richer data (prefer Indeed for salary data, LinkedIn for seniority level)

Phase 3: Present Results

Show results as a summary table:

Source: LinkedIn + Indeed (deduplicated)
Jobs found: {count}
Location: {location}
Search: "{query}"
 
| # | Title | Company | Location | Salary | Posted | Source |
|---|-------|---------|----------|--------|--------|--------|
| 1 | AI Engineer | Stripe | SF, CA | $200K-$300K | 3 days ago | LinkedIn |
| 2 | ML Engineer | Meta | Menlo Park, CA | $58.65/hr | Mar 14 | Indeed |
| ... |

After the table:

• Note how many were filtered for recency
• Note how many duplicates were removed
• Provide the total cost of the search

If the user wants more detail on a specific job, show the full description.

Phase 4: Export (Optional)

If the user wants to save results:

{search-term}-jobs-{YYYY-MM-DD}.csv

CSV columns:

title, company, location, salary, employment_type, seniority_level, posted_date, job_url, apply_url, description, source

Normalize fields across sources so the CSV has a consistent schema regardless of whether the job came from LinkedIn or Indeed.

Cost Estimates

LinkedIn is cheaper per job. Indeed returns richer data per job. Both together give the most complete picture.

Common Use Cases

Hiring signal detection: "Find companies hiring AI engineers in SF" → Search both sources, group by company, rank by number of open roles. Companies with 5+ AI roles are actively building.

Competitive intelligence: "What is Anthropic hiring for?" → Search LinkedIn with searchQuery: "Anthropic". Shows their open roles, team growth, and strategic priorities.

Salary research: "What do ML engineers make in NYC?" → Search Indeed (richer salary data). Filter to NYC, aggregate salary ranges.

GTM prospecting: "Find companies hiring for VP of Sales" → These companies are scaling their sales org and may need sales tools. Export the company list for outreach.

Error Handling