apify-ecommerce

name	apify-ecommerce
description	Scrape e-commerce data for pricing, reviews, bestsellers, and seller discovery across 30+ platforms including Amazon, Walmart, eBay, Shopify, WooCommerce, and more. Use when user asks about product prices, competitor analysis, store scraping, tech stack detection, food delivery, real estate, or marketplace intelligence.
author	Luis Pinto
author_url	https://github.com/luispintoapify
metadata	{"keywords":"ecommerce, pricing, reviews, sentiment, products, sellers, amazon, walmart, ebay, MAP, competitor, research, supply-chain"}

E-Commerce Cluster

Answer natural language e-commerce questions by routing to the right Apify Actor and delivering a synthesized answer via the apify CLI.

CLI rules: Always pass --user-agent apify-awesome-skills/apify-ecommerce, --json (or the relevant --format flag on datasets get-items), and 2>/dev/null. The --user-agent flag is critical for telemetry — never omit it.

Prerequisites

(No need to check it upfront)

• Apify CLI v1.5.0+ (npm install -g apify-cli)
• jq (recommended for quick extraction and filtering; brew install jq on macOS, apt install jq on Linux)
• Authentication via one of:
  • apify login (OAuth, opens browser)
  • APIFY_TOKEN env variable (e.g. export APIFY_TOKEN=... or .env file)
  • Token from Apify Console → Settings → Integrations

Verify auth: apify info --user-agent apify-awesome-skills/apify-ecommerce — should show username and userId.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Detect intent and select Actor
- [ ] Step 2: Fetch Actor schema
- [ ] Step 3: Ask user preferences (format, result count)
- [ ] Step 4: Run the Actor and fetch results
- [ ] Step 5: Analyze results and deliver synthesized answer

Step 1: Detect Intent and Select Actor

Classify the user's message into an intent, then pick the right Actor.

Intent signals:

Signals in user message	Intent
price, cost, cheapest, compare prices, pricing	pricing
review, rating, sentiment, stars, feedback	reviews
bestseller, top selling, most popular, trending	bestsellers
seller, vendor, reseller, who sells	sellers
all products from, scrape store, full catalog	store-scrape
what platform, built on, tech stack, Shopify or WooCommerce	tech-stack
SEO, listing quality, product page audit	seo-audit
competitor funnel, competitor pricing, conversion elements	competitor
search intent, keyword intent, SERP intent	search-intent
match products, same product on different platforms	product-matching
restaurant, food delivery, DoorDash, UberEats, TheFork	food-delivery
enrich store, store metadata, store list	store-enrichment
event, concert, ticket, Eventbrite	events
property, real estate, house listing, Realtor	real-estate
Facebook ads, Meta ads, ad library, competitor ads	ads-intelligence
classified, Craigslist, used item for sale	classifieds
car, used car, vehicle, automotive, Webmotors	automotive
pins, inspiration, Pinterest boards, visual search, Pinterest trends	content-discovery
TikTok Shop, TikTok store, TikTok creator	tiktok-shop
website for sale, domain for sale, Flippa	website-marketplace

If multiple intents are detected, ask: "Do you want [intent A] or [intent B]?"

Actor routing table — always try Primary first, switch to Fallback only if it fails or returns 0 results. The Primary actor (apify/e-commerce-scraping-tool) handles most intents once you feed the right input mode:

• Have target URLs (a listing, profile, or category page) → detailsUrls / listingUrls.
• Have a keyword/marketplace → keyword + marketplaces (product-details mode).
• Broad discovery (competitor, search-intent, classifieds, automotive, real-estate, website-marketplace, events) → use searchEngineKeyword (search-engine mode) or keyword/detailsUrls depending on whether you have a query or URLs.

Exception — skip the Primary and go straight to the Fallback for intents the Primary genuinely can't do (different data source or specialized analysis): tech-stack, seo-audit, store-enrichment, product-matching, ads-intelligence, content-discovery (Pinterest), and tiktok-shop. Routing these to the Primary wastes a run and credits.

Intent	Platform	Primary Actor	Fallback Actor
pricing	Amazon / Walmart / generic	apify/e-commerce-scraping-tool	—
pricing	eBay	apify/e-commerce-scraping-tool	ivanvs/ebay-scraper-pay-per-result
pricing	Etsy	apify/e-commerce-scraping-tool	epctex/etsy-scraper
pricing	Google Shopping	apify/e-commerce-scraping-tool	epctex/google-shopping-scraper
pricing	Facebook Marketplace	apify/e-commerce-scraping-tool	apify/facebook-marketplace-scraper
pricing	SHEIN	apify/e-commerce-scraping-tool	seamless_coffer/shein-product-scraper
pricing	Lazada	apify/e-commerce-scraping-tool	fatihtahta/lazada-scraper
pricing	Canadian Tire	apify/e-commerce-scraping-tool	azzouzana/canadiantire-ca-scraper
pricing	Tesco	apify/e-commerce-scraping-tool	radeance/tesco-scraper
pricing	Shopify	apify/e-commerce-scraping-tool	trovevault/shopify-products-scraper
pricing	WooCommerce	apify/e-commerce-scraping-tool	trovevault/woocommerce-products-scraper
reviews	Amazon / Walmart / generic

Escalation — if both Primary and Fallback fail or return 0 results, discover a current alternative live instead of guessing an ID:

# Find relevant, well-rated, pay-per-event Actors for the platform/intent.
# Keep the default relevance sort — `--sort-by popularity` surfaces generic
# big-name scrapers over the platform you actually asked for.
apify actors search "PLATFORM or INTENT keywords" \
  --pricing-model PAY_PER_EVENT --limit 10 --json \
  --user-agent apify-awesome-skills/apify-ecommerce 2>/dev/null \
  | jq '[.items[]
      | select(.stats.totalUsers > 100 and .actorReviewRating > 4.5)
      | {id: (.username + "/" + .name), users: .stats.totalUsers,
         rating: (.actorReviewRating | (. * 100 | round / 100)),
         pricing: .currentPricingInfo.pricingModel}]'

Pick the top match. Before running it, confirm it requests only limited permissions (check the Actor's Store page / README — prefer Actors that don't require full account access). If the PAY_PER_EVENT filter returns nothing, drop the --pricing-model flag and re-run, keeping the ≥100-users and ≥4.5-rating bar.

Step 2: Fetch Actor Schema

Fetch the Actor summary, input schema, and README:

# Summary (title, description, pricing, stats)
apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-ecommerce --json 2>/dev/null

# Input schema — use --input WITHOUT --json to get the clean schema directly.
# (Adding --json returns the full ~250 KB actor object instead, with the schema
#  buried as an escaped string under .taggedBuilds.latest.build.inputSchema.)
apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-ecommerce --input 2>/dev/null

# README (capabilities, examples, gotchas)
apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-ecommerce --readme 2>/dev/null

Replace ACTOR_ID with the selected Actor (e.g., apify/e-commerce-scraping-tool).

Primary actor input cheat-sheet. apify/e-commerce-scraping-tool is mode-driven — pick fields by intent (always set the matching max…Results cap):

Intent	Minimal input
pricing (keyword)	{"keyword": "wireless earbuds", "marketplaces": ["www.amazon.com"], "maxProductResults": 50}
pricing (specific URLs)	{"detailsUrls": [{"url": "https://…"}], "maxProductResults": 50}
store-scrape (category)	{"listingUrls": [{"url": "https://…/category"}], "maxProductResults": 500}
reviews	{"keywordReviews": "echo dot", "marketplacesReviews": ["www.amazon.com"], "sortReview": "Most recent", "maxReviewResults": 200}
sellers	{"sellerUrls": [{"url": "https://…"}], "maxSellerResults": 50}
pricing (Google Shopping)	{"searchEngineKeyword": "ps5", "countryCode": "us", "maxSearchEngineResults": 50}
food-delivery	{"keywordDelivery": "pizza", "marketplacesDelivery": ["www.doordash.com"], "addressDelivery": "New York, NY", "maxDeliveryResults": 50}

For any other actor (or fields not listed), fetch the schema with the --input command above.

Step 3: Ask User Preferences

Before running, ask:

1. Output format:
   • Quick answer (default) — synthesized answer in chat, no file saved
   • CSV — full export saved to disk
   • JSON — full export saved to disk
2. Result count — suggest defaults by intent:

Intent	Default
pricing	50 products
reviews	200 reviews
bestsellers	100 items
sellers	50 sellers
store-scrape	all (unlimited)
food-delivery	50 restaurants
all others	20–50

Cost safety: Always set a sensible result limit in the Actor input. For the Primary actor the cap field is mode-specific — maxProductResults, maxReviewResults, maxSellerResults, maxSearchEngineResults, or maxDeliveryResults (there is no single maxResults). For Fallback actors, use whatever the schema exposes (maxResults, resultsLimit, maxItems, maxCrawledPages, etc.). Default to the per-intent values above unless the user explicitly asks for more. Warn the user before running large scrapes (1000+ results) as they consume more Apify credits.

Step 4: Run the Actor and Fetch Results

Two steps: run the Actor (blocks until done), then fetch dataset items in the requested format.

Run the Actor — returns run metadata as JSON; extract defaultDatasetId for the next step:

apify actors call "ACTOR_ID" -i 'JSON_INPUT' \
  --user-agent apify-awesome-skills/apify-ecommerce --json 2>/dev/null

From the output use .id (run ID), .status (should be SUCCEEDED), and .defaultDatasetId.

Fetch results — pick the variant based on the user's preference:

# Quick answer: total count + fields + top 5 in chat (no file)
apify datasets info DATASET_ID --json \
  --user-agent apify-awesome-skills/apify-ecommerce 2>/dev/null \
  | jq '{itemCount, fields, consoleUrl}'
apify datasets get-items DATASET_ID --limit 5 \
  --user-agent apify-awesome-skills/apify-ecommerce --format json 2>/dev/null

# CSV file
apify datasets get-items DATASET_ID \
  --user-agent apify-awesome-skills/apify-ecommerce --format csv 2>/dev/null > YYYY-MM-DD_OUTPUT_FILE.csv

# JSON file
apify datasets get-items DATASET_ID \
  --user-agent apify-awesome-skills/apify-ecommerce --format json 2>/dev/null > YYYY-MM-DD_OUTPUT_FILE.json

Other --format options: jsonl, xlsx, xml, rss, html. Use --offset N to paginate large datasets.

Tip: for anything more than a quick peek, save the dataset to a local file first (with > file.json / > file.csv) and run further analysis from disk. apify datasets get-items always streams over the network, so piping it straight into jq re-downloads the whole thing every iteration.

Combining with jq for quick extraction:

Treat jq as a complement to apify datasets get-items, not a replacement: server-side --limit / --offset / --format keeps cost and bandwidth down. Use jq on a sample item or on a file you already saved.

# Discover real field names from one sample item (Actor outputs vary —
# use this before composing further jq queries)
apify datasets get-items DATASET_ID --limit 1 --format json \
  --user-agent apify-awesome-skills/apify-ecommerce 2>/dev/null \
  | jq '.[0]'

# Quick aggregation from a JSON file you already saved with the commands above
jq '[.[] | select(.rating != null and .rating >= 4.5)] | length' YYYY-MM-DD_OUTPUT_FILE.json

Step 5: Analyze Results and Deliver Answer

After the run completes, deliver a direct synthesized answer — not a data dump:

• Pricing: price range, average, top 5 cheapest with URLs
• Reviews: average rating, top 3 positive and negative themes, recent snippets
• Bestsellers: top 10 by rank with name, price, rating, URL
• Sellers: total sellers, price range per seller, unauthorized seller flags
• Store-scrape: total products, category breakdown, price range, stock summary
• Tech-stack: platform detected, confidence level, notable plugins
• Food delivery: restaurant count, average rating, price tier breakdown
• Ads intelligence: total ads, active/inactive split, top creative formats

Error Handling

• Auth error → run apify login, or set APIFY_TOKEN env var
• Actor not found → check Actor ID spelling in the routing table
• Run status FAILED → open the console URL (.consoleUrl from run metadata) for logs
• Timeout / very long run → pass --timeout <seconds> to apify actors call
• No results → broaden the keyword, switch to the Fallback Actor, then use the Escalation discovery command (under Step 1) if both fail
• proxy is required → add "proxy": {"useApifyProxy": true} to the Actor input
• Platform not detected → default to apify/e-commerce-scraping-tool with generic intent

Gotchas

• --input --json is a trap. It returns the full ~250 KB actor object, not the schema. Use apify actors info ID --input --user-agent apify-awesome-skills/apify-ecommerce 2>/dev/null (no --json) for the clean schema; only dig into .taggedBuilds.latest.build.inputSchema if you specifically need it as JSON.
• The Primary actor has no maxResults field. Its caps are mode-specific (maxProductResults, maxReviewResults, maxSellerResults, maxSearchEngineResults, maxDeliveryResults). Setting maxResults does nothing and the run scrapes unbounded.
• The Primary handles most intents via the right input mode (URLs → detailsUrls/listingUrls; query → keyword or searchEngineKeyword), including competitor, search-intent, classifieds, automotive, real-estate, website-marketplace, and events. It genuinely can't do tech-stack, seo-audit, store-enrichment, product-matching, ads-intelligence, content-discovery (Pinterest), or tiktok-shop — route those straight to the Fallback.
• apify actors call -i expects valid JSON on one line. For inputs with URL arrays or quotes, write a file and pass -i @input.json instead of inlining — shell quoting silently corrupts complex inputs.
• datasets get-items always streams over the network. Save to a file once (> file.json), then run jq against the file — don't re-pipe the command into jq repeatedly or you re-download every time.
• apify actors search --sort-by popularity ignores relevance. It returns the biggest-name scrapers regardless of your query (an "etsy" search surfaces Instagram/Google Maps Actors). For escalation discovery keep the default relevance sort and filter on stats.totalUsers/actorReviewRating instead.
• marketplaces values are full domain slugs, e.g. ["www.amazon.com", "www.ebay.com"] — not or display names. Delivery mode is even narrower: only accepts (no UberEats — use the fallback for that). Always confirm accepted values from the schema's before guessing.