Agent skill

job-scraper

Search for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or cookies required.

gooseworks-aigithub.com/gooseworks-aiGitHub ↗
claude-codecodexcursorMIT
Install
npx skills add gooseworks-ai/goose-skills --skill job-scraper --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 10 KB
Bundled scripts: none
Path: skills/lead-generation/capabilities/job-scraper/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,091
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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 | Apify Actor | Best For | Cost | |--------|------------|----------|------| | **LinkedIn** | `automation-lab/linkedin-jobs-scraper` | B2B, tech, SaaS, enterprise roles. Has seniority level, job function, industries. | ~$0.002/job | | **Indeed** | `b

What's inside
Steps it walks through
  1. When to Auto-Load
  2. Prerequisites
  3. Apify API Token
  4. Sources
  5. Source Selection Logic
  6. Workflow
  7. Phase 0: Understand the Request
  8. Phase 1: Search
  9. Phase 2: Filter & Deduplicate
  10. Phase 3: Present Results
  11. Phase 4: Export (Optional)
  12. Cost Estimates
  13. Common Use Cases
  14. Error Handling
Ships with 1 file
  • skill.meta.json
Commands it runs
curl -X POST "https://api.apify.com/v2/acts/automation-lab~linkedin-jobs-scraper/runs?token=$APIFY_API_TOKEN" \
Check run status (poll every 10s)
curl "https://api.apify.com/v2/acts/automation-lab~linkedin-jobs-scraper/runs/{RUN_ID}?token=$APIFY_API_TOKEN"
When status is SUCCEEDED, fetch results
curl "https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN"
curl -X POST "https://api.apify.com/v2/acts/borderline~indeed-scraper/runs?token=$APIFY_API_TOKEN" \
More from goose-skills
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About this skill
What does the job-scraper skill do?

Search for job postings across LinkedIn and Indeed. Use when users want to find open roles, monitor hiring signals, identify companies hiring for specific positions, or research competitor hiring activity. Returns job title, company, location, salary, description, seniority level, and direct apply URLs. No login or cookies required.

How do I install it?

Run `npx skills add gooseworks-ai/goose-skills --skill job-scraper --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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