Scrape recent posts from LinkedIn profiles using Apify. Use when you need to monitor what specific people are posting on LinkedIn, track founder/exec activity, or gather LinkedIn content for competitive intelligence.
npx gooseworks install --claude # Then in your agent: /gooseworks <prompt> --skill linkedin-profile-post-scraper
Scrape recent posts from specific LinkedIn profiles using the Apify harvestapi/linkedin-profile-posts actor.
Requires APIFY_API_TOKEN env var (or --token flag). Install dependency: pip install requests.
# Scrape recent posts from a profile
python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
--profiles "https://www.linkedin.com/in/marcelsantilli" --max-posts 10
# Multiple profiles with keyword filtering
python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
--profiles "https://www.linkedin.com/in/person1,https://www.linkedin.com/in/person2" \
--keywords "AI,growth" --days 30
# Summary table
python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
--profiles "https://www.linkedin.com/in/marcelsantilli" --output summary| Flag | Default | Description |
|---|---|---|
--profiles | required | LinkedIn profile URL(s), comma-separated |
--max-posts | 20 | Max posts to scrape per profile |
--keywords | none | Keywords to filter (comma-separated, OR logic) |
--days | 30 | Only include posts from last N days |
--output | json | Output format: json or summary |
--token | env var | Apify token (prefer APIFY_API_TOKEN env var) |
--timeout | 300 | Max seconds to wait for the Apify run |
~$2 per 1,000 posts scraped. The script prints a cost estimate before running.
postedAt/postedDatehttps://www.linkedin.com/in/username)postedAt/postedDatehttps://www.linkedin.com/in/username)Diagnose Meta Ads campaign performance using Meta's actual system mechanics — Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue — and produce structured, testable recommendations that avoid judging segments by average CPA instead of marginal efficiency.
Pre-flight policy check for Meta ads. Takes ad copy plus advertiser context, resolves and fetches the relevant Meta transparency-center policy pages at runtime, and returns a Pass / Fix Required / Block verdict with cited findings and rewrites.
For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.