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:

[
  {
    "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:

{
  "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:

{
  "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:

{
  "title": "Product Website Title",
  "url": "https://product-website.com",
  "email": "contact@product-website.com",
  "allEmails": ["contact@product-website.com", "support@product-website.com"],
  "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:

{
  "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": "hello@product-website.com",
    "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.