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.
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安装前可以查看交给 AI 代理的指令内容。
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.
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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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.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
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