🧩 Shopify Apps Spy + Product Scraper
Pricing
from $2.00 / 1,000 product extracteds
Detect which apps any Shopify store has installed (Klaviyo, Recharge, Yotpo, Privy + 30 more). Plus full product catalog & reviews. No login. 5x cheaper.
Pricing
from $2.00 / 1,000 product extracteds
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0.0
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Developer
KazKN
Maintained by CommunityActor stats
1
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6
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3
Monthly active users
17 days ago
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All notable changes to Shopify Apps Spy + Product Scraper are documented here. This project adheres to Semantic Versioning .
The Apify build numbers track each deployment to the Apify Store; the version numbers below reflect functional changes visible to users.
Insufficient permissions for the dataset hung the run when the actor was called via the Apify API or by any user other than the actor owner. Apify runs every public actor under a LIMITED_PERMISSIONS token that can write to the run's default dataset but cannot open globally-named datasets like apps or reviews. The previous code called Actor.openDataset('apps') which hung indefinitely under that token, freezing the run after the first store and leaving the dataset effectively empty (just an error/domain record). The actor now writes every row to the default dataset only, tagged with a record_type field so the output stays filterable.not_a_shopify_store. The actor now distinguishes "blocked by anti-bot" from "not a Shopify store" and auto-retries with a residential proxy on the first block. If the block persists, the diagnostic record now includes an actionable next_steps array (whitelist Apify IPs in Cloudflare, disable Bot Fight Mode for /products.json, etc.).record_type field on every dataset record (product / apps / reviews). Filter the default dataset by this field instead of opening separate named datasets.proxy_tier_used field in diagnostic records (datacenter / residential_fallback) so users can see exactly which network path succeeded.signals array (anti_bot_cloudflare, anti_bot_akamai, etc.) for downstream filtering.Actor.charge() call so a hung billing endpoint can never freeze the run.apps and reviews named datasets need to switch to dataset.filter(r => r.record_type === 'apps') etc. on the default dataset.include_variants: true now emits one row per variant (matching the README contract) instead of a nested variants[] array on a single product row. Each variant row carries the full product fields plus per-variant variant_id, variant_title, sku, option1/2/3, variant-level price, compare_at_price, available, and main_image (variant featured image when available). Verified on aplusnutri.com: the product "Équilibre et bien-être au quotidien" now correctly produces 5 rows (Limonade framboise / Pastèque fraîche / Cerise gourmande / Orange douce / Piña colada).extract_level: 'pro') moved from the named revenue dataset to the default dataset with record_type: 'revenue'. Same LIMITED_PERMISSIONS rationale.aplusnutri.com: detection went from 1 app (klaviyo only) to 6 apps across 6 categories — Klaviyo, Microsoft Clarity, Intelligems, Kaching Bundles, Boostymark Region Block, Shopify Privacy Banner.record_type first for sort/filterexamples/ folder with 5 runnable code samples covering ICP qualification, app market-share research, cold-outreach personalization, cURL quickstart, and Zapier/Make.com integration.actor/actor.json title and .actor/README.md with the new name/products.json endpointextract_level: full for Yotpo, Judge.me, Stamped, Okendo, Loox, Junip, Reviews.io, Rebuybasic, standard, full, propro tier) — observable from review velocity + product velocity for ICP filtering