apify

name Apify version 1.1.22 description Scrapes social platforms, business data, and e-commerce via Apify actors — Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps, Amazon, and web crawls — filtering in code. USE WHEN scrape Instagram, scrape LinkedIn, scrape TikTok, scrape YouTube, scrape Facebook, Google Maps leads, Amazon reviews, business intelligence, multi-platform social listening, competitive analysis, lead generation, social monitoring, Apify actors, web crawl, extract contacts. NOT FOR X/Twitter account operations like posting, threads, or bookmarks (those need a dedicated X API client), 4-tier progressive scraping with proxy escalation (use BrightData), or real-Chrome bot bypass and computer use (use Interceptor). Customization Before executing, check for user customizations at: ~/.claude/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Apify/ If this directory exists, load and apply any PREFERENCES.md, configurations, or resources found there. These override default behavior. If the directory does not exist, proceed with skill defaults. 🚨 MANDATORY: Voice Notification (REQUIRED BEFORE ANY ACTION) You MUST send this notification BEFORE doing anything else when this skill is invoked. Send voice notification : curl -s -X POST http://localhost:31337/notify \ -H "Content-Type: application/json" \ -d '{"message": "Running the WORKFLOWNAME workflow in the Apify skill to ACTION"}' \ > /dev/null 2>&1 & Output text notification : Running the **WorkflowName** workflow in the **Apify** skill to ACTION... This is not optional. Execute this curl command immediately upon skill invocation. Apify - Social Media & Web Scraping What It Does Scrapes social platforms, business data, and e-commerce through Apify actors: Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps business search, Amazon, and general-purpose web crawling. TypeScript wrappers filter and transform the data in code before any of it reaches the model, so a 100-post scrape costs roughly what 10 posts would. Runs platforms in parallel for social-listening dashboards and chains Google Maps into LinkedIn for lead enrichment. The Problem Scraping through a raw MCP dumps every unfiltered result straight into model context — a single Instagram profile with 100 posts burns ~52,000 tokens, most of it noise you'll throw away. You usually want the top 10 posts, the negative reviews from the last week, the qualified leads with an email. Doing that filtering after the data hits the model is too late; the tokens are already spent. Filtering in code first cuts that 52,000 down to ~500. How It Works This skill is a file-based MCP — a code-first API wrapper that replaces token-heavy MCP protocol calls. You call an actor wrapper, filter and sort the result in TypeScript, and only the filtered slice reaches model context. That code-before-context step is where the 95-99% token savings come from. Workflow Routing Workflow Trigger File Update update Apify skill, refresh actors, actor calls failing unexpectedly, monthly capability check Workflows/Update.md (inline) all scrape/lead/crawl requests — scrape Instagram/LinkedIn/TikTok/YouTube/Facebook, Google Maps leads, Amazon reviews, web crawl Actor wrappers under actors/ (see Actor Reference below) 📊 Available Actors Social Media (5 platforms) Instagram (145k users, 4.60★) - Profiles, posts, hashtags, comments LinkedIn (26k users, 4.10★) - Profiles, jobs, posts TikTok (90k users, 4.61★) - Profiles, videos, hashtags, comments YouTube (40k users, 4.40★) - Channels, videos, comments, search Facebook (35k users, 4.56★) - Posts, groups, comments Business & Lead Generation Google Maps (198k users, 4.76★) - HIGHEST VALUE! Search businesses, extract contacts, reviews, images Perfect for lead generation E-commerce Amazon (8k users, 4.97★) - Products, reviews, pricing Web Scraping Web Scraper (94k users, 4.39★) - General-purpose, works with ANY website 🚀 Quick Start Basic Usage Pattern import { scrapeInstagramProfile, searchGoogleMaps } from 'actors' // 1. Call the actor wrapper const profile = await scrapeInstagramProfile ({ username : 'target_username' , maxPosts : 50 }) // 2. Filter in code - BEFORE data reaches model! const viral = profile. latestPosts ?. filter ( p => p. likesCount > 10000 ) // 3. Only filtered results reach model context console . log (viral) // ~10 posts instead of 50 📚 Examples by Use Case Social Media Monitoring Instagram - Track engagement: import { scrapeInstagramProfile, scrapeInstagramPosts } from 'actors' // Get profile with recent posts const profile = await scrapeInstagramProfile ({ username : 'competitor' , maxPosts : 100 }) // Filter in code - only high-performing posts from last 30 days const thirtyDaysAgo = Date . now () - ( 30 * 24 * 60 * 60 * 1000 ) const topRecent = profile. latestPosts ?. filter ( p => new Date (p. timestamp ). getTime () > thirtyDaysAgo && p. likesCount > 5000 ) . sort ( ( a, b ) => b. likesCount - a. likesCount ) . slice ( 0 , 10 ) // Only 10 posts reach model instead of 100! LinkedIn - Job search: import { searchLinkedInJobs } from 'actors' const jobs = await searchLinkedInJobs ({ keywords : 'AI engineer' , location : 'San Francisco' , remote : true , maxResults : 200 }) // Filter in code - only senior roles at well-funded startups const topJobs = jobs. filter ( j => j. seniority ?. includes ( 'Senior' ) && parseInt (j. applicants || '0' ) > 50 ) TikTok - Trend analysis: import { scrapeTikTokHashtag } from 'actors' const videos = await scrapeTikTokHashtag ({ hashtag : 'ai' , maxResults : 500 }) // Filter in code - only viral content const viral = videos . filter ( v => v. playCount > 1000000 ) . sort ( ( a, b ) => b. playCount - a. playCount ) . slice ( 0 , 20 ) Lead Generation (Business Intelligence) Google Maps - Local business leads: import { searchGoogleMaps } from 'actors' // Search with contact info extraction const places = await searchGoogleMaps ({ query : 'restaurants in Austin' , maxResults : 500 , includeReviews : true , maxReviewsPerPlace : 20 , scrapeContactInfo : true // Extracts emails from websites! }) // Filter in code - only highly-rated with email/phone const qualifiedLeads = places . filter ( p => p. rating >= 4.5 && p. reviewsCount >= 100 && (p. email || p. phone ) ) . map ( p => ({ name : p. name , rating : p. rating , reviews : p. reviewsCount , email : p. email , phone : p. phone , website : p. website , address : p. address })) // Export leads - only qualified results! console . log ( `Found ${qualifiedLeads.length} qualified leads` ) Google Maps - Review sentiment analysis: import { scrapeGoogleMapsReviews } from 'actors' const reviews = await scrapeGoogleMapsReviews ({ placeUrl : 'https://maps.google.com/maps?cid=12345' , maxResults : 1000 }) // Filter in code - analyze sentiment by rating const recentNegative = reviews . filter ( r => { const thirtyDaysAgo = Date . now () - ( 30 * 24 * 60 * 60 * 1000 ) return ( r. rating <= 2 && new Date (r. publishedAtDate ). getTime () > thirtyDaysAgo && r. text . length > 50 ) }) // Identify common complaints const complaints = recentNegative. map ( r => r. text ) E-commerce & Competitive Intelligence Amazon - Price monitoring: import { scrapeAmazonProduct } from 'actors' const product = await scrapeAmazonProduct ({ productUrl : 'https://www.amazon.com/dp/B08L5VT894' , includeReviews : true , maxReviews : 200 }) // Filter in code - only recent negative reviews const recentNegative = product. reviews ?. filter ( r => { const weekAgo = Date . now () - ( 7 * 24 * 60 * 60 * 1000 ) return ( r. rating <= 2 && new Date (r. date ). getTime () > weekAgo ) }) console . log ( `Price: $ ${product.price} ` ) console . log ( `Rating: ${product.rating} /5` ) console . log ( `Recent issues: ${recentNegative?.length} complaints` ) Custom Web Scraping Any Website - Custom extraction: import { scrapeWebsite } from 'actors' const products = await scrapeWebsite ({ startUrls : [ 'https://example.com/products' ], linkSelector : 'a.product-link' , maxPagesPerCrawl : 100 , pageFunction : ` async function pageFunction(context) { const { request, $, log } = context return { url: request.url, title: $('h1.product-title').text(), price: $('span.price').text(), inStock: $('.in-stock').length > 0, description: $('.description').text() } } ` }) // Filter in code - only available products under $100 const affordable = products. filter ( p => p. inStock && parseFloat (p. price . replace ( '$' , '' )) < 100 ) 🎨 Advanced Patterns Pattern 1: Multi-Platform Social Listening import { scrapeInstagramHashtag, scrapeTikTokHashtag, searchYouTube } from 'actors' // Run all platforms in parallel const [instagramPosts, tiktokVideos, youtubeVideos] = await Promise . all ([ scrapeInstagramHashtag ({ hashtag : 'ai' , maxResults : 100 }), scrapeTikTokHashtag ({ hashtag : 'ai' , maxResults : 100 }), searchYouTube ({ query : '#ai' , maxResults : 100 }) ]) // Combine and filter - only viral content across all platforms const allViral = [ ...instagramPosts. filter ( p => p. likesCount > 10000 ), ...tiktokVideos. filter ( v => v. playCount > 100000 ), ...youtubeVideos. filter ( v => v. viewsCount > 50000 ) ] console . log ( `Found ${allViral.length} viral posts across 3 platforms` ) Pattern 2: Lead Enrichment Pipeline import { searchGoogleMaps, scrapeLinkedInProfile } from 'actors' // 1. Find businesses on Google Maps const restaurants = await searchGoogleMaps ({ query : 'restaurants in SF' , maxResults : 100 , scrapeContactInfo : true }) // 2. Filter for qualified leads const qualified = restaurants. filter ( r => r. rating >= 4.5 && r. email && r. reviewsCount >= 50 ) // 3. Enrich with LinkedIn data (if available) const enriched = await Promise . all ( qualified. map ( async (restaurant) => { // Try to find LinkedIn company page // ... additional enrichment logic return restaurant }) ) Pattern 3: Competitive Analysis Dashboard import { scrapeInstagramProfile, scrapeYouTubeChannel, scrapeTikTokProfile } from 'actors' async function analyzeCompetitor ( username : string ) { // Gather data from all platforms const [instagram, youtube, tiktok] = await Promise . all ([ scrapeInstagramProfile ({ username, maxPosts : 30 }), scrapeYouTubeChannel ({ channelUrl : `https://youtube.com/@ ${username} ` , maxVideos : 30 }), scrapeTikTokProfile ({ username, maxVideos : 30 }) ]) // Calculate engagement metrics in code return { username, instagram : { followers : instagram. followersCount , avgLikes : average (instagram. latestPosts ?. map ( p => p. likesCount ) || []), engagementRate : calculateEngagement (instagram) }, youtube : { subscribers : youtube. subscribersCount , avgViews : average (youtube. videos ?. map ( v => v. viewsCount ) || []) }, tiktok : { followers : tiktok. followersCount , avgPlays : average (tiktok. videos ?. map ( v => v. playCount ) || []) } } } 💰 Token Savings Calculator Example: Instagram profile with 100 posts MCP Approach: 1. search-actors → 1,000 tokens 2. call-actor → 1,000 tokens 3. get-actor-output → 50,000 tokens (100 unfiltered posts) TOTAL: ~52,000 tokens File-Based Approach: const profile = await scrapeInstagramProfile ({ username : 'user' , maxPosts : 100 }) // Filter in code - only top 10 posts const top = profile. latestPosts ?. sort ( ( a, b ) => b. likesCount - a. likesCount ) . slice ( 0 , 10 ) // TOTAL: ~500 tokens (only 10 filtered posts reach model) Savings: 99% reduction (52,000 → 500 tokens) 🔧 Actor Reference Social Media Instagram scrapeInstagramProfile(input) - Profile + posts scrapeInstagramPosts(input) - Posts from user scrapeInstagramHashtag(input) - Posts by hashtag scrapeInstagramComments(input) - Comments on post LinkedIn scrapeLinkedInProfile(input) - Profile + experience + email searchLinkedInJobs(input) - Job listings scrapeLinkedInPosts(input) - Posts from profile/company TikTok scrapeTikTokProfile(input) - Profile + videos scrapeTikTokHashtag(input) - Videos by hashtag scrapeTikTokComments(input) - Comments on video YouTube scrapeYouTubeChannel(input) - Channel + videos searchYouTube(input) - Search videos scrapeYouTubeComments(input) - Comments on video Facebook scrapeFacebookPosts(input) - Posts from pages scrapeFacebookGroups(input) - Group posts