lead-generation

LinkedIn Job Scraper

Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct URLs.

by Athina AI

Run in Gooseworks View skill in Github

Install

Terminal

npx gooseworks install --all

# then, in Claude Code, Cursor, or Codex:
/gooseworks use the linkedin-job-scraper skill

About This Skill

LinkedIn Scraper

Overview

This skill finds LinkedIn job postings by running tools/jobspy_scraper.py, a thin wrapper around the JobSpy library. It handles installation, parameter construction, execution, and result interpretation.

Quick Start

Install the dependency once (requires Python 3.10+):

python3.12 -m pip install -U python-jobspy --break-system-packages

Run the scraper:

python3.12 tools/jobspy_scraper.py \
  --search "software engineer" \
  --location "San Francisco, CA" \
  --results 25 \
  --output .tmp/jobs.csv

Results are saved as CSV and printed as a summary table.

Workflow

Step 1 — Understand the request

Identify from the user's message:

Search term — job title, role, or keyword (required)
Location — city, state, or "Remote" (optional but recommended)
Results wanted — default to 25 if not specified
Recency — hours_old filter if user wants recent posts (e.g. "last 48 hours")
Company filter — linkedin_company_ids if targeting a specific company
Full descriptions — set --fetch-descriptions if user needs job description text

If anything is ambiguous (e.g. "find AI jobs"), pick reasonable defaults and tell the user what you used.

Step 2 — Construct the command

Build the tools/jobspy_scraper.py command using the parameters below. Always save output to .tmp/ so it's disposable and easy to find.

python tools/jobspy_scraper.py \
  --search "<term>" \
  --location "<location>" \
  --results <N> \
  [--hours-old <N>] \
  [--fetch-descriptions] \
  [--company-ids <id1,id2>] \
  [--job-type fulltime|parttime|contract|internship] \
  [--remote] \
  --output .tmp/<descriptive_filename>.csv

Note: --hours-old and --easy-apply cannot be used together (LinkedIn API constraint).

Step 3 — Run the script

Execute the command. The script will print a progress message and a summary of results found.

If the script is not found at tools/jobspy_scraper.py, check whether the file needs to be created by reading skills/linkedin-job-scraper/scripts/jobspy_scraper.py and copying it to tools/.

Step 4 — Interpret and present results

After the run:

Report how many jobs were found
Show a brief table: Title | Company | Location | Salary | Posted
Note the output file path so the user can open it
If 0 results: suggest broadening the search term or removing the location filter

Parameters Reference

Flag	Description	Default
`--search`	Job title / keywords	required
`--location`	City, state, or country	none
`--results`	Number of results to fetch	25
`--hours-old`	Only jobs posted within N hours	none
`--fetch-descriptions`	Fetch full job descriptions (slower)	false
`--company-ids`	Comma-separated LinkedIn company IDs	none
`--job-type`	fulltime, parttime, contract, internship	any
`--remote`	Filter for remote jobs only	false
`--output`	Path for CSV output	.tmp/jobs.csv

Output Columns

The CSV output includes:

Column	Description
`TITLE`	Job title
`COMPANY`	Employer name
`LOCATION`	City / State / Country
`IS_REMOTE`	True/False
`JOB_TYPE`	fulltime, contract, etc.
`DATE_POSTED`	When the listing was posted
`MIN_AMOUNT`	Minimum salary
`MAX_AMOUNT`	Maximum salary
`CURRENCY`	Currency code
`JOB_URL`	Direct link to the LinkedIn posting
`DESCRIPTION`	Full job description (if --fetch-descriptions used)
`JOB_LEVEL`	Seniority level (LinkedIn-specific)
`COMPANY_INDUSTRY`	Industry classification

Common Use Cases

Find recent engineering roles at a startup:

python tools/jobspy_scraper.py --search "growth engineer" --location "New York" \
  --results 50 --hours-old 72 --output .tmp/growth_eng_nyc.csv

Monitor what a specific company is hiring for:

# First find the LinkedIn company ID from the company's LinkedIn URL
python tools/jobspy_scraper.py --search "engineer" --company-ids 1234567 \
  --results 100 --fetch-descriptions --output .tmp/company_hiring.csv

Find remote contract roles:

python tools/jobspy_scraper.py --search "data analyst" --remote \
  --job-type contract --results 30 --output .tmp/remote_contracts.csv

Error Handling

Error	Fix
`ModuleNotFoundError: jobspy`	Run `pip install -U python-jobspy`
0 results returned	Broaden search term, remove location, increase `--results`
Rate limited / blocked	Wait a few minutes; avoid running back-to-back large scrapes
`hours_old and easy_apply cannot both be set`	Remove one of those flags

Script Location

The scraper script lives at tools/jobspy_scraper.py.

If it doesn't exist, copy it from skills/linkedin-scraper/scripts/jobspy_scraper.py to tools/:

cp skills/linkedin-job-scraper/scripts/jobspy_scraper.py tools/

What's included

Search term* — job title, role, or keyword (required)

Location* — city, state, or "Remote" (optional but recommended)

Results wanted* — default to 25 if not specified

Recency* — hours_old filter if user wants recent posts (e.g. "last 48 hours")

Company filter* — linkedin_company_ids if targeting a specific company

Trend Discovery

Discover rising category conversations, formats, sounds, questions, and creator patterns across social platforms, then separate durable demand signals from short-lived noise.

Transcript Intelligence

Turn TikTok, Instagram, YouTube, Facebook, X, LinkedIn, Reddit, or Rumble transcripts into timestamped hooks, claims, objections, proof, calls to action, sponsorship signals, and reusable content atoms. Use directly for transcript analysis or as support for creator, competitor, trend, demand, and repurposing work.

Social Listening Brief

Produce a decision-ready brief of current brand, product, category, and competitor conversations across social platforms, including sentiment drivers, questions, risks, and growth opportunities.