Collect recent posts from specified LinkedIn profiles through Apify, with options to limit the time range and filter by keywords. Use the results to review individuals’ activity or gather content for competitive research, then view them as JSON or a summary table.
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.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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/example-person" --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/example-person" --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)No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
The supplied material contains only placeholder text for the skill’s purpose, inputs, steps, and completion criteria. It does not provide enough information to describe a task or result reliably.
Research supported advertising angles using buyer context, current product facts, customer language, and observed ads. Turn the evidence into static ad, video, or copy ideas, matching video concepts to available recipes. Scores rank ideas for testing; they do not predict results.
Analyze campaign data from Google, Meta, and LinkedIn to find strong and wasteful spend. The skill checks funnel performance and test results, then recommends which campaigns to pause, scale, or test and how to shift budget across channels.
Compare ad spend and signups with downstream outcomes to calculate the cost of each qualified lead. The skill checks tracking coverage, accounts for how long leads need to mature, and groups creatives into actionable categories with confidence notes.
Prepare the intake for a Meta ad campaign by reviewing available brand information first, stating reasonable assumptions, and asking only questions whose answers could affect budget, claims, or legal exposure. It records stable brand facts, a campaign brief, and what was asked versus assumed.
Review ad copy, image text, visuals, and people against platform and brand category rules. It suggests honest fixes and needed disclosures, then flags issues requiring human or legal review, with a separate result for each ad and platform.