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Social media scraping, business data, e-commerce via Apify actors. USE WHEN Twitter, Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps, Amazon scraping.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/Sheshiyer/skill-clusters --skill apifyコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SOC 職業分類に基づく
Arcplume runs Grok through the Grok Build CLI's own OAuth-authenticated session (grok login) for image generation, with strict preflight validation, secret-safe handling, and headless CLI-driven execution -- no separate XAI_API_KEY billing. Video falls back to the billed xAI API. USE WHEN a user wants to generate an image via a locally logged-in Grok Build CLI session, e.g. 'generate an image with grok', 'use grok build', or 'use my logged-in grok session'.
Shared reference for the Selemene cluster: the two report surfaces (deterministic Rust reports vs. narrative witness-pipeline readings), the @selemene/bridge CLI contract, the output manifest format, and non-prescriptive witnessing tone. USE WHEN deciding which Selemene surface to invoke or when routing between birth/compatibility/transit reports and solo/dyadic readings.
Route Selemene Engine tasks to the right surface: deterministic reports (birth/compatibility/transit) via selemene-core and the @selemene/bridge CLI, or narrative witness readings via selemene-report. USE WHEN the user wants anything under the Selemene/Noesis umbrella but has not named the exact surface.
SKILL.md を表示中
| name | apify |
| cluster | research-knowledge |
| version | 1.0.0 |
| description | Social media scraping, business data, e-commerce via Apify actors. USE WHEN Twitter, Instagram, LinkedIn, TikTok, YouTube, Facebook, Google Maps, Amazon scraping. |
| context | fork |
Before executing, check for user customizations at:
~/.Codex/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.
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.
Direct TypeScript access to 9 popular Apify actors with 99% token savings.
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 ~/.Codex/skills/PAI/DOCUMENTATION/FileBasedMCPs.md
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.
import { scrapeInstagramProfile, searchGoogleMaps } from '~/.Codex/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
Instagram - Track engagement:
import { scrapeInstagramProfile, scrapeInstagramPosts } from '~/.Codex/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 '~/.Codex/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 '~/.Codex/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)
Google Maps - Local business leads:
import { searchGoogleMaps } from '~/.Codex/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 '~/.Codex/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)
Amazon - Price monitoring:
import { scrapeAmazonProduct } from '~/.Codex/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`)
Any Website - Custom extraction:
import { scrapeWebsite } from '~/.Codex/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
)
import {
scrapeInstagramHashtag,
scrapeTikTokHashtag,
searchYouTube
} from '~/.Codex/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 ${allViral.length} viral posts across 3 platforms`)
import { searchGoogleMaps, scrapeLinkedInProfile } from '~/.Codex/skills/Apify/actors'
// 1. Find businesses on Google Maps
const restaurants = await searchGoogleMaps({
query: 'restaurants in SF',
maxResults: 100,
scrapeContactInfo: true
})
// 2. Filter for qualified leads
const qualified = restaurants.filter(r =>
r.rating >= 4.5 &&
r.email &&
r.reviewsCount >= 50
)
// 3. Enrich with LinkedIn data (if available)
const enriched = await Promise.all(
qualified.map(async (restaurant) => {
// Try to find LinkedIn company page
// ... additional enrichment logic
return restaurant
})
)
import {
scrapeInstagramProfile,
scrapeYouTubeChannel,
scrapeTikTokProfile
} from '~/.Codex/skills/Apify/actors'
async function analyzeCompetitor(username: string) {
// Gather data from all platforms
const [instagram, youtube, tiktok] = await Promise.all([
scrapeInstagramProfile({ username, maxPosts: 30 }),
scrapeYouTubeChannel({ channelUrl: `https://youtube.com/@${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) || [])
}
}
}
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)
scrapeInstagramProfile(input) - Profile + postsscrapeInstagramPosts(input) - Posts from userscrapeInstagramHashtag(input) - Posts by hashtagscrapeInstagramComments(input) - Comments on postscrapeLinkedInProfile(input) - Profile + experience + emailsearchLinkedInJobs(input) - Job listingsscrapeLinkedInPosts(input) - Posts from profile/companyscrapeTikTokProfile(input) - Profile + videosscrapeTikTokHashtag(input) - Videos by hashtagscrapeTikTokComments(input) - Comments on videoscrapeYouTubeChannel(input) - Channel + videossearchYouTube(input) - Search videosscrapeYouTubeComments(input) - Comments on videoscrapeFacebookPosts(input) - Posts from pagesscrapeFacebookGroups(input) - Group postsscrapeFacebookComments(input) - Post commentssearchGoogleMaps(input) - Search places (with contact extraction!)scrapeGoogleMapsPlace(input) - Single place detailsscrapeGoogleMapsReviews(input) - Place reviewsscrapeAmazonProduct(input) - Product details + reviewsscrapeAmazonReviews(input) - Product reviews onlyscrapeWebsite(input) - Custom multi-page crawlingscrapePage(url, pageFunction) - Single page extractionEnvironment 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
}
Use File-Based (this skill):
Use MCP:
Remember: Filter data in code BEFORE returning to model context. This is where the 99% token savings happen!