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) - APIFYTOKEN env variable (e.g. export APIFYTOKEN=... 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:

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.

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 PAYPEREVENT 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):

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

1. Result count — suggest defaults by intent:

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 "amazon" or display names. Delivery mode is even narrower: marketplacesDelivery only accepts ["www.doordash.com", "www.instacart.com"] (no UberEats — use the e-commerce/ubereats-reviews-scraper fallback for that). Always confirm accepted values from the --input schema's enum before guessing.