producthunt-launches

$npx mdskill add browser-act/skills/producthunt-launches

Scrapes Product Hunt leaderboard launches with maker profiles and contact info.

  • Extracts product launch data from Product Hunt daily, weekly, monthly, or yearly leaderboards.
  • Requires an open browser session to access producthunt.com and may need captcha solving.
  • Determines the leaderboard period based on user-specified date or time range.
  • Returns structured product details, maker profiles, and website contact information.

SKILL.md

.github/skills/producthunt-launchesView on GitHub ↗
---
name: producthunt-launches
description: "Scrape Product Hunt daily/weekly/monthly/yearly leaderboard launches with full product details, maker profiles, and website contact info. Use when user mentions Product Hunt, producthunt, PH scraper, product hunt launches, product hunt leaderboard, scrape product hunt, product hunt data, PH daily launches, product hunt upvotes, product hunt maker info, extract product hunt, product hunt today, top products product hunt, product hunt archive, PH products, product hunt email extraction, product hunt contact info, producthunt.com scraping, get product hunt launches, product hunt API alternative. Also applies to: startup launch monitoring, new product discovery, maker/founder contact enrichment, product hunt lead generation, daily product hunt digest, competitive product tracking."
---

# Product Hunt — Launch Data Extraction

> Input: date/period parameters → Output: structured product launch data with maker profiles and website contact info

## Language

All process output to user (progress updates, process notifications) follows the user's language.

## Objective

Extract complete product launch data from Product Hunt leaderboard pages, enriched with maker profile information and product website contact details.

## Prerequisites

- Browser session is open and can access producthunt.com
- Cloudflare challenge may appear on first visit; use `solve-captcha` or wait for auto-pass

## Pre-execution Checks

### 1. Tool Readiness

If browser-act has been confirmed available in the current session → skip this step.

Invoke `browser-act` via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

### 2. Cloudflare Verification

Product Hunt uses Cloudflare protection. On first navigation:
1. Navigate to target URL
2. If page title shows "Just a moment..." → `wait stable --timeout 15000` then check title again
3. If still blocked → `solve-captcha`
4. Verify page loaded: title should contain "Product Hunt" or "Best of Product Hunt"

## Capability Components

> This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page, never bypassing authentication or access controls. JS code is encapsulated in Python files under the `scripts/` directory, invoked via `eval "$(python scripts/xxx.py)"`. `$(...)` is bash syntax; use the bash tool for execution.

### DOM: Extract product list from leaderboard page

Navigate to the target leaderboard URL first, then extract:

`eval "$(python scripts/extract-leaderboard.py)"`

URL patterns (navigate to the appropriate one before extraction):
- Daily: `https://www.producthunt.com/leaderboard/daily/{YYYY}/{M}/{DD}/all`
- Weekly: `https://www.producthunt.com/leaderboard/weekly/{YYYY}/{week-number}/all`
- Monthly: `https://www.producthunt.com/leaderboard/monthly/{YYYY}/{M}/all`
- Yearly: `https://www.producthunt.com/leaderboard/yearly/{YYYY}/all`

Replace `all` with `featured` for featured-only products.

Output example:
```json
[
  {
    "rank": 1,
    "name": "Product Name",
    "tagline": "Short product description",
    "categories": ["Productivity", "AI"],
    "thumbnail": "https://ph-files.imgix.net/...",
    "upvotes": 135,
    "comments": 42,
    "url": "https://www.producthunt.com/products/product-slug",
    "slug": "product-slug"
  }
]
```

### DOM: Extract launch detail page

Navigate to the launch page URL first (`https://www.producthunt.com/products/{slug}/launches/{launch-slug}`), then extract:

`eval "$(python scripts/extract-launch-detail.py)"`

To find the launch URL from a product page: navigate to `https://www.producthunt.com/products/{slug}` and look for links matching `/products/{slug}/launches/{launch-slug}`.

Output example:
```json
{
  "name": "Product Name",
  "tagline": "Short product tagline",
  "description": "Full product description from OG meta",
  "categories": ["Productivity", "Social Media"],
  "images": ["https://ph-files.imgix.net/gallery1.png", "https://ph-files.imgix.net/gallery2.png"],
  "websiteUrl": "https://product-website.com/?ref=producthunt",
  "upvotes": 135,
  "launchDate": "2025-05-27T07:26:33-07:00",
  "makers": [{"href": "/@username", "name": "Maker Name"}],
  "ogImage": "https://ph-files.imgix.net/og-image.png"
}
```

### DOM: Extract maker profile

Navigate to maker profile URL (`https://www.producthunt.com/@{username}`), then extract:

`eval "$(python scripts/extract-maker-profile.py)"`

Output example:
```json
{
  "name": "Maker Name",
  "slug": "@username",
  "headline": "Creating SaaS Products",
  "aboutText": "Bio text about the maker",
  "links": ["https://twitter.com/username", "https://linkedin.com/in/username"],
  "followers": 22,
  "url": "https://www.producthunt.com/@username"
}
```

### DOM: Extract website email and content

Navigate to the product website URL, wait for load, then extract:

`eval "$(python scripts/extract-website-content.py)"`

Alternatively, use `stealth-extract` for faster extraction without a browser session:
`stealth-extract {website-url} --content-type markdown` then parse the markdown for email patterns.

Output example:
```json
{
  "title": "Product Website Title",
  "url": "https://product-website.com",
  "email": "[email protected]",
  "allEmails": ["[email protected]", "[email protected]"],
  "websiteRawText": "Full visible text content of the website..."
}
```

### Composite: Full product extraction (leaderboard + detail + maker + website)

Complete pipeline replicating the full Product Hunt scraper workflow:

1. Navigate to leaderboard page → `wait stable` → `eval "$(python scripts/extract-leaderboard.py)"`
2. For each product from step 1:
   a. Navigate to `https://www.producthunt.com/products/{slug}` → find launch link → navigate to launch page
   b. `wait stable` → `eval "$(python scripts/extract-launch-detail.py)"` → get full details + maker links + website URL
3. (Optional, if `scrapeMakers` is enabled) For each unique maker from step 2:
   a. Navigate to `https://www.producthunt.com/{maker.href}` → `wait stable` → `eval "$(python scripts/extract-maker-profile.py)"`
4. (Optional, if `scrapeWebsite` is enabled) For each product website URL from step 2:
   a. Navigate to website URL → `wait stable` → `eval "$(python scripts/extract-website-content.py)"`
5. Merge all data by product slug

Final output example per product:
```json
{
  "date": "2026-06-10T00:00:00Z",
  "launchDate": "2026-06-10T07:01:04Z",
  "url": "https://www.producthunt.com/products/product-slug",
  "name": "Product Name",
  "shortDescription": "Short tagline",
  "description": "Full description text",
  "categories": ["Productivity", "AI"],
  "maker": {
    "makerHref": "https://www.producthunt.com/@username",
    "name": "Maker Name",
    "slug": "@username",
    "url": "https://www.producthunt.com/@username",
    "links": ["https://twitter.com/maker", "https://linkedin.com/in/maker"],
    "aboutText": "Maker bio text"
  },
  "websiteUrl": "https://product-website.com",
  "images": ["https://ph-files.imgix.net/image1.png"],
  "upvotes": 135,
  "website": {
    "title": "Product Website",
    "url": "https://product-website.com",
    "email": "[email protected]",
    "websiteRawText": "Full page text content..."
  }
}
```

## Pagination

**No pagination required for daily/weekly leaderboard**: All products for a given day load on a single page (typically 15-50 products per day). No infinite scroll or "load more" button exists.

**Yearly leaderboard**: May contain many products. Apply `topNProducts` filter to limit. All visible products are rendered on the single page.

## Success Criteria

- `result count >= 1` (at least one product extracted from leaderboard)
- Core fields non-null: `name`, `tagline`, `upvotes`, `url` present for every product
- Data consistency: extracted product names match what is displayed on the page
- When detail enrichment is performed: `websiteUrl` or `maker` present for enriched items

## Known Limitations

- Cloudflare protection requires initial challenge pass; may need `solve-captcha` on first visit
- No public API available; Product Hunt only accepts persisted GraphQL queries. All data must be extracted via DOM
- Rate limiting: rapid sequential page navigations may trigger Cloudflare blocks. Add 2-3 second delays between product detail page visits
- The `/all` URL path (used by older scrapers) now returns 404; use `/leaderboard/daily/` path instead
- Product detail pages may vary in structure for older launches vs newer ones
- Website email extraction depends on email being visible in page text or HTML; mailto links and contact forms with obfuscated emails will not be captured

## Execution Efficiency

- **Batch orchestration**: Write a bash script to loop through the command templates serially within a single session; do not parallelize within one browser. Add 2-3 second delays between navigations to avoid Cloudflare blocks. To increase throughput, open multiple stealth browser sessions and distribute work across them
- **Test before batch execution**: After writing a batch script, first test with 1-2 items to verify the script runs correctly; only then run the full batch
- **Reduce redundant pre-operations**: The leaderboard extraction gives all basic data in one pass; only visit detail pages when full description, images, or maker info are needed
- **Error resumption**: Save results item by item during batch processing; on failure, resume from the breakpoint rather than starting over
- **Skip website extraction when not needed**: Website content extraction is the slowest step (external site navigation). Only enable when email/content data is specifically required

## Experience Notes

Path: `browser-act-skill-forge-memories/producthunt-scraper-producthunt-launches.memory.md` (working directory is determined by the Agent running the Skill)

**Before execution**: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.

**After execution**: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line:
`{YYYY-MM-DD}: {what happened} → {conclusion}`

Normal execution does not write to the file.

More from browser-act/skills

SkillDescription
1688-product-detailExtracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.
airbnb-listing-detailFetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.
airbnb-search-listingExtracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
amazon-alexa-qaAmazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
amazon-asin-lookup-api-skillThis skill helps users extract structured product details from Amazon using a specific ASIN (Amazon Standard Identification Number). Use this skill when the user asks to get Amazon product details by ASIN, lookup Amazon product title and price using ASIN, extract Amazon product ratings and reviews count for a specific ASIN, check Amazon product availability and current price, get Amazon product description and features via ASIN, enrich product catalog with Amazon data using ASIN, monitor Amazon product price changes for specific ASINs, retrieve Amazon product brand and material information, fetch Amazon product images and specifications by ASIN, validate Amazon ASIN and get product metadata.
amazon-best-selling-products-finder-api-skillThis skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.
amazon-bestseller-listingAmazon Best Sellers listing scraper: extract product cards from any Amazon Best Sellers (zgbs) or /gp/bestsellers/ category page — returns rank (position on chart), asin, title, url, image, imageAlt, price, stars, reviewCount, ratingRaw per item, plus category metadata (categoryName, categoryFullName, categoryUrl) and pagination state (currentPage, hasNextPage, nextPageUrl). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, etc.). Use when user mentions Amazon Best Sellers, Amazon bestsellers, Amazon top 100, Amazon zgbs, Amazon /zgbs/, Amazon /gp/bestsellers/, Amazon Best Sellers Rank, Amazon BSR, Amazon top ranked products, Amazon top-selling products, Amazon chart, Amazon category ranking, Amazon best sellers by category, Amazon best sellers electronics, Amazon best sellers kitchen, Amazon best sellers toys, scrape Amazon bestsellers, extract Amazon top 100, Amazon rank scraper, Amazon best seller list, Amazon leaderboard, Amazon trending products, discover trending Amazon products, Amazon niche discovery, Amazon top ranked ASINs. Also applies to competitive intelligence via ranking snapshots, spotting up-and-coming products, sourcing bestseller ASINs for further enrichment, tracking rank changes over time, and building bestseller-per-category datasets.
amazon-buy-box-monitor-api-skillThis skill helps users extract basic product details other sellers prices and seller ratings from Amazon via ASIN automatically using the BrowserAct API. Agent should proactively apply this skill when users express needs like query Amazon buy box information, monitor Amazon product prices, extract Amazon product details by ASIN, check other sellers prices on Amazon, get Amazon seller ratings and feedback count, monitor buy box ownership for a specific ASIN, track Amazon fulfillment methods for competitors, compare Amazon product prices across different sellers, retrieve Amazon buy box availability status, analyze Amazon seller profile details.
amazon-competitor-analyzerScrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
amazon-listing-competitor-analysis-skillThis skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.