Apify

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.

1. 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 &
   

2. 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
• scrapeFacebookComments(input) - Post comments

Business & Lead Generation

Google Maps

• searchGoogleMaps(input) - Search places (with contact extraction!)
• scrapeGoogleMapsPlace(input) - Single place details
• scrapeGoogleMapsReviews(input) - Place reviews

E-commerce

Amazon

• scrapeAmazonProduct(input) - Product details + reviews
• scrapeAmazonReviews(input) - Product reviews only

Web Scraping

General Web

• scrapeWebsite(input) - Custom multi-page crawling
• scrapePage(url, pageFunction) - Single page extraction

⚙️ Configuration

Environment Variables:

# Required - Get from https://console.apify.com/account/integrations
APIFY_TOKEN=apify_api_xxxxx...

Actor Run Options:

{
  memory: 2048,    // MB: 128, 256, 512, 1024, 2048, 4096, 8192
  timeout: 300,    // seconds
  build: 'latest'  // or specific build number
}

🎯 When to Use This vs MCP

Use File-Based (this skill):

• ✅ Need to filter large datasets (>100 results)
• ✅ Want to transform/aggregate data in code
• ✅ Multiple sequential operations
• ✅ Control flow (loops, conditionals)
• ✅ Maximum token efficiency

Use MCP:

• ❌ Simple single operations with small results (<10 items)
• ❌ One-off exploratory queries
• ❌ Don't want to write code

🔗 Links

• Apify Platform: https://apify.com
• Actor Store: https://apify.com/store
• API Docs: https://docs.apify.com/api/v2

---

Remember: Filter data in code BEFORE returning to model context. This is where the 99% token savings happen!

Gotchas

• Actor selection matters. Each social platform has specific actors — don't use a generic scraper for Instagram when a dedicated Instagram actor exists.
• Rate limits vary by platform and plan. Check actor documentation for limits before running large scrapes.
• Scraped data format varies by actor. Read the actor's output schema before processing results.

Examples

Example 1: Scrape Instagram profile

User: "get the recent posts from this Instagram account"
→ Selects Instagram Profile actor
→ Runs with target profile URL
→ Returns structured post data (text, engagement, dates)

Example 2: LinkedIn company scrape

User: "scrape this company's LinkedIn page"
→ Selects LinkedIn Company actor
→ Returns company info, employee count, recent posts

Execution Log

After completing any workflow, append a single JSONL entry:

echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"Apify","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl

Replace WORKFLOW_USED with the workflow executed, 8_WORD_SUMMARY with a brief input description, and SECONDS with approximate wall-clock time. Log status: "error" if the workflow failed.