Platform Scraper

Phase 1: Platform-Specific Scraping

For each selected platform, use WebSearch with site-specific queries. Generate 2-3 queries per platform by rotating service keywords. Execute searches with rate limiting: max 3 concurrent, 2-second pause between batches of 3.

Freelancer.com

• Query: site:freelancer.com/projects "{keyword}" for each service keyword
• Extract: project title, description, budget range, skills required, posted date, URL, proposals count
• Note: Freelancer projects always have /projects/ in the URL path. Filter results missing this pattern.

PeoplePerHour

• Query: site:peopleperhour.com "{keyword}" "project"
• Extract: project title, description, budget, posted date, URL

Guru

• Query: site:guru.com/jobs "{keyword}"
• Extract: title, description, budget, skills, URL

Fiverr

• Query: site:fiverr.com/categories "{keyword}" OR search for buyer request forums
• Note: Fiverr is seller-first. Focus on buyer request threads and "looking for" language, not seller profiles.

Upwork

• Query: site:upwork.com/freelance-jobs "{keyword}"
• Extract: title, description, budget, client history, URL

Reddit

• Query: site:reddit.com/r/shopify OR site:reddit.com/r/ecommerce "{keyword}" "{intent_keyword}"
• Also search: r/smallbusiness, r/Entrepreneur, r/DTC, r/forhire
• Extract: post title, body text, subreddit, author, post date, URL, comment count
• Prioritize posts with question marks and help-seeking language ("need", "looking for", "recommend", "anyone know").

LinkedIn

• Query: site:linkedin.com/posts "{keyword}" "{intent_keyword}"
• Also: site:linkedin.com/jobs "{keyword}"
• Extract: post text, author, company, post date, URL
• Note: LinkedIn post URLs are not always indexable. Accept partial results.

Behance

• Query: site:behance.net/joblist "{keyword}"
• Extract: job title, company, description, URL

Dribbble

• Query: site:dribbble.com/jobs "{keyword}"
• Extract: job title, company, description, URL

Shopify Community

• Query: site:community.shopify.com "{keyword}" "{intent_keyword}"
• Extract: thread title, body, author, date, URL
• Focus on threads in "Shopify Design" and "Shopify Apps" categories.

IndieHackers

• Query: site:indiehackers.com "{keyword}" "{intent_keyword}"
• Extract: post title, body, author, date, URL
• Look for founders asking about ecommerce, Shopify, or D2C store builds.

Funding News

• Query: "{industry} startup" "raises" OR "funding" OR "seed round" this week
• Sources to check: TechCrunch, Crunchbase, YourStory (for India)
• Extract: company name, amount raised, round, industry, investors, URL
• Parse headline for company name (usually the first entity before "raises" or "secures").
• Parse for funding amount: look for patterns like $X million, Rs X crore, etc.

Twitter/X

• Query: site:twitter.com OR site:x.com "{keyword}" "shopify" OR "ecommerce" OR "d2c"
• Also: site:x.com "{industry}" "just launched" OR "coming soon" OR "new store"
• Extract: tweet text, handle, bio, website from profile, follower count
• Focus on brand accounts, not agencies or developers

Instagram Brands

• Query: site:instagram.com "{keyword}" "shop" OR "store" OR "link in bio"
• Target hashtags: #shopifystore, #d2cbrand, #ecommerce, #shopifyseller
• Extract: profile name, handle, bio, website link from bio, follower count
• Focus on D2C brands with active storefronts

Product Hunt

• Query: site:producthunt.com "{keyword}" OR "ecommerce" OR "shopify"
• Extract: product name, tagline, maker name, website, launch date, upvotes
• Focus on recently launched D2C products and ecommerce tools

Google Maps / Business Directories

• Query: site:google.com/maps "{industry}" "{market}" + site:justdial.com "{keyword}" (India) + site:yelp.com "{keyword}" (US/UK)
• Extract: business name, phone number, website, address, rating
• Phone numbers are a key differentiator for multi-channel outreach

Shopify Store Discovery

• Query: site:myshopify.com "{industry_keyword}" + site:builtwith.com "shopify" "{industry}"
• Extract: store name, URL, theme (if visible), industry
• Look for stores with basic themes (Dawn, Debut) indicating redesign need
• These are warm leads: they already use Shopify but need upgrades

Phase 2: Normalize Results

Normalize all results from every platform into a single standard format:

{
  "title": "Project or post title",
  "description": "First 500 chars of description or body text",
  "platform": "Freelancer",
  "url": "https://...",
  "budget": "$500-$1000",
  "posted_date": "2024-01-15",
  "market": "US",
  "contact": "username or name if available",
  "company": "Company name if identifiable from context",
  "skills_required": ["shopify", "liquid", "theme-development"],
  "contact_surfaces": {
    "has_email": false,
    "has_linkedin": false,
    "has_instagram": false,
    "has_phone": false,
    "has_website": true,
    "channel_count": 1,
    "discovery_notes": "Website from store URL."
  },
  "raw_data": {}
}

Rules:

• market should be inferred from currency symbols, country mentions, or TLD patterns in the content.
• budget should be normalized to a consistent format: "$X" or "$X-$Y" or "Rs X" for Indian currency. Set to empty string if not found.
• contact should capture the username, author name, or poster identity if visible.
• skills_required should be extracted from explicit skill tags or inferred from description keywords.

Phase 3: Filter

Apply filters in this order:

1. Negative keyword filter: Check title + description against icp.negative_keywords. Reject any result matching a negative keyword (these are typically self-promoters, spam, or irrelevant categories).

2. Relevance check: The result must match at least one primary_keyword OR one secondary_keyword from the config. Results matching zero keywords are discarded.

3. Recency filter: Skip results with a posted_date older than the time_window parameter. If posted_date is empty/unknown, keep the result but mark it with "date_unknown": true.

4. Dedup by URL: Remove exact URL duplicates. If the same company or person appears from different URLs (same underlying opportunity), keep only the most recent or most detailed one.

Phase 4: Output

Return an array of normalized leads, sorted by relevance:

1. Primary keyword matches first (stronger signal)
2. Secondary keyword matches second
3. Within each group, sort by recency (newest first)

Present results grouped by platform with counts:

Found {N} leads across {M} platforms:

Freelancer ({count}):
1. "Project title" - $budget - URL
2. ...

Reddit ({count}):
1. "Post title" - r/subreddit - URL
2. ...

LinkedIn ({count}):
...

[Other platforms with results]

Total: {N} leads
Filtered out: {X} (negative keywords: {a}, irrelevant: {b}, stale: {c}, duplicates: {d})

Trim to max_results after sorting. If more results exist, note the overflow count.

Example Usage

Trigger phrases:

• "Scrape platforms for leads"
• "Find gigs on Freelancer and Upwork"
• "Search all platforms for Shopify projects"
• "Run platform scraper"
• "Find freelance gigs matching our services"

User: Scrape Freelancer and Reddit for Shopify leads
Assistant: [reads config, generates site-specific queries for Freelancer and Reddit, executes with rate limiting, normalizes to standard format, filters and deduplicates, presents grouped results]

User: Run platform scraper on all platforms, last 3 days
Assistant: [same flow across all 12 platforms, time_window set to 3 days]