Customization
Before executing, check for user customizations at: ~/.claude/skills/PAI/USER/SKILLCUSTOMIZATIONS/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:8888/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
Direct TypeScript access to 9 popular Apify actors with 99% token savings.
🔌 File-Based MCP
This skill is a file-based MCP - a code-first API wrapper that replaces token-heavy MCP protocol calls.
Why file-based? Filter data in code BEFORE returning to model context = 97.5% token savings.
Architecture: See ~/.claude/skills/PAI/SYSTEM/DOCUMENTATION/FileBasedMCPs.md
🎯 Overview
Direct TypeScript access to the 9 most popular Apify actors without MCP overhead. Filter and transform data in code BEFORE it reaches the model context.
📊 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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 '~/.claude/skills/Apify/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 ${allViral.length} viral posts across 3 platforms`)Pattern 2: Lead Enrichment Pipeline
import { searchGoogleMaps, scrapeLinkedInProfile } from '~/.claude/skills/Apify/actors'
// 1. Find businesses on Google Maps
const restaurants = await searchGoogleMaps({
query: 'restaurants in SF',
maxResults: 100,
scrapeContactInfo: true
})
// 2. Filter for qualified leads
const qualified = restaurants.filter(r =>
r.rating >= 4.5 &&
r.email &&
r.reviewsCount >= 50
)
// 3. Enrich with LinkedIn data (if available)
const enriched = await Promise.all(
qualified.map(async (restaurant) => {
// Try to find LinkedIn company page
// ... additional enrichment logic
return restaurant
})
)Pattern 3: Competitive Analysis Dashboard
import {
scrapeInstagramProfile,
scrapeYouTubeChannel,
scrapeTikTokProfile
} from '~/.claude/skills/Apify/actors'
async function analyzeCompetitor(username: string) {
// Gather data from all platforms
const [instagram, youtube, tiktok] = await Promise.all([
scrapeInstagramProfile({ username, maxPosts: 30 }),
scrapeYouTubeChannel({ channelUrl: `https://youtube.com/@${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 tokensFile-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
scrapeInstagramProfile(input)- Profile + postsscrapeInstagramPosts(input)- Posts from userscrapeInstagramHashtag(input)- Posts by hashtagscrapeInstagramComments(input)- Comments on post
scrapeLinkedInProfile(input)- Profile + experience + emailsearchLinkedInJobs(input)- Job listingsscrapeLinkedInPosts(input)- Posts from profile/company
TikTok
scrapeTikTokProfile(input)- Profile + videosscrapeTikTokHashtag(input)- Videos by hashtagscrapeTikTokComments(input)- Comments on video
YouTube
scrapeYouTubeChannel(input)- Channel + videossearchYouTube(input)- Search videosscrapeYouTubeComments(input)- Comments on video
scrapeFacebookPosts(input)- Posts from pagesscrapeFacebookGroups(input)- Group postsscrapeFacebookComments(input)- Post comments
Business & Lead Generation
Google Maps
searchGoogleMaps(input)- Search places (with contact extraction!)scrapeGoogleMapsPlace(input)- Single place detailsscrapeGoogleMapsReviews(input)- Place reviews
E-commerce
Amazon
scrapeAmazonProduct(input)- Product details + reviewsscrapeAmazonReviews(input)- Product reviews only
Web Scraping
General Web
scrapeWebsite(input)- Custom multi-page crawlingscrapePage(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!