Scrape Product Hunt trending products using Apify. Use when you need to discover new product launches, track competitors on Product Hunt, or monitor the startup ecosystem for relevant launches.
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Scrape trending products from Product Hunt using the Apify maximedupre/product-hunt-scraper actor.
Output fields: Each product has name, tagline, description, url (Product Hunt URL), and additional fields depending on the actor's output.
Requires APIFY_API_TOKEN env var (or --token flag).
# Today's top products
python3 skills/product-hunt-scraper/scripts/scrape_producthunt.py \
--time-period daily --max-products 10 --output summary
# This week's products filtered by keyword
python3 skills/product-hunt-scraper/scripts/scrape_producthunt.py \
--time-period weekly --keywords "AI,marketing" --output summary
# Monthly top products as JSON
python3 skills/product-hunt-scraper/scripts/scrape_producthunt.py \
--time-period monthly --max-products 50
| Flag | Default | Description |
|---|---|---|
--time-period | weekly | daily, weekly, or monthly |
--max-products | 50 | Max products to scrape |
--keywords | none | Keywords to filter (comma-separated, OR logic) |
--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 |
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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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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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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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