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

Render Model Comparison Grid

Render a 'model comparison grid' video from a config — a fal-style "same prompt, N contenders" showcase — a dark real-DOM stage where per beat a monospace prompt fades in centered, docks to a small top strip, then a labeled 2-4 panel grid (static images OR muted video clips, mixable per cell) staggers in and holds for comparison, plus a minimal end card — frame-stepped via Playwright (video cells are frame-seeked deterministically) and encoded with FFmpeg. Deterministic assembly, FREE (cell media comes from create-image-fal / create-video-fal, music from create-music-elevenlabs), text stays pixel-crisp. Use for the model-comparison-grid format.

Render Offer Ad

Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.

Render Myth Vs Fact

Assemble a myth-vs-fact kinetic-typography explainer video ad (≈29.5s, 9:16) from N myth/fact pairs + hook / turn / punch copy + palette + a brand end-card PNG + a VO track — a hook, 3 red-strike MYTH cards that flip to teal-check FACT cards (per-line strikethrough that crosses EVERY wrapped line), a "what actually works" turn, an optional proof reveal, a punch line, and a static end card. DETERMINISTIC assembly with ZERO AI-gen visuals — HTML hyperframes rendered frame-exact via Playwright (`window.renderAt(t)`, animation a pure function of beat-local time), Whisper beat-snap to VO word onsets, concat at a uniform fps, karaoke `.ass` captions burned last (suppressed on the proof + end-card beats), and a VO + optional music mix (music −20 dB, `amix normalize=0`, tail fade). FREE (Python + Playwright + ffmpeg); the recipe supplies the copy / palette / end-card / VO and gates the paid VO / music / Whisper calls to their own capabilities. Use for the myth-vs-fact format.