ecommerce-temu-store-query

name	ecommerce.temu-store-query
version	1.0.0
category	product-sourcing
description	Query Temu stores/sellers via EHunt filters. Triggers: Temu store analysis, Temu seller, store ranking, revenue, followers, semi-managed stores. Use for seller/store-level analysis, not individual product sourcing; use temu-product-query for products.

EHunt Temu Store Query (ehunt/temu/storeQuery)

Core Question

Which Temu stores match the user's seller, revenue, category, region, or performance filters?

When to Use

• User asks to find or rank Temu stores/sellers.
• User wants to compare Temu seller performance, revenue, followers, ratings, product count, or category focus.
• User asks for semi-managed or fully managed Temu stores in specific regions.
• User wants store-level sourcing leads rather than individual product listings.

Clarify or Infer Before Querying

If the user request is broad or ambiguous, identify:

• target site or region
• category or seller niche
• performance metric to sort by, such as weekly sales, total revenue, rating, followers, or product count
• fulfillment mode if relevant
• desired result count and time window

Use sensible defaults when the user's intent is clear: US site, weekly sales descending, first page, 20 stores.

Differs From / Not Applicable

• Use ecommerce.temu-product-query when the user wants individual Temu products, product prices, product sales, ratings, tags, or product sourcing.
• Use this skill when the unit of analysis is the seller/store.
• Do not use for TikTok Shop data, Amazon seller data, product detail pages, or listing copy optimization.

Workflow

1. Understand the store-level goal and normalize filters.
2. Resolve region/site, category, fulfillment mode, ranges, and sort order.
3. Query EHunt Temu store data.
4. Return a compact seller table with the most relevant metrics.
5. Add a short interpretation: strongest stores, unusual outliers, and next filters to try.

Output Structure

Default output should include:

• Query assumptions and filters used
• Store ranking table: store name/ID, site, category focus, sales, revenue, rating, followers, product count, fulfillment mode
• Key observations: top performers, fast-growing or high-efficiency stores, weak data points
• Suggested next step: refine category, inspect products from selected stores, or compare regions

Calls the EHunt data source via the Nexscope proxy gateway, route /api/v1/tools/research/ehunt/temu/storeQuery (display name: Temu Store Query). Authentication and routing are handled by the Nexscope proxy; if the response contains a root-level code field, success is determined by the live response.

Key Points

• Pagination: page starts at 1; pageSize defaults to 20, max 100.
• Range parameters: *Min / *Max pairs (total/weekly/monthly sales, total/weekly/monthly revenue, rating, reviews, followers, product count) form upstream range filters.
• Site: siteId country site ID, comma-separated for multiple values (100 = US, 101 = UK, 102 = EU, 103 = JP, 105 = KR).
• Category: category back-end category ID, comma-separated for multiple values.
• Fulfillment mode: isLocal (0 = fully managed, 1 = semi-managed, string type).
• Opening date: listedTimeBegin / listedTimeEnd (YYYY-MM-DD).
• Sorting: sortBy is a "field-direction" string, e.g. order_week_count-0 (weekly sales descending, default), order_count-0, total_revenue-0, rating-0.

Scripts (Optional)

CLI debugging: python scripts/ehunt_temu_store_query.py '<JSON>' (requires environment variables NEXSCOPE_API_KEY and NEXSCOPE_PROXY_BASE). See references/api.md for details.

Reference

Full request/response parameter tables: references/api.md.

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/ehunt_temu_store_query.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"

Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data -- do not commit them. Files are not auto-deleted; clean up when the task is done.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern -- apply your judgment based on the response characteristics, e.g.:

• High field count per record, or fields you do not need
• Batch/paginated results (multiple items per call)
• Long-text fields (descriptions, reviews, HTML, time series)
• Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

Warning: The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read.

Environment Variables

Variable	Required	Description
NEXSCOPE_PROXY_BASE	Yes	NexScope proxy base URL
NEXSCOPE_API_KEY	Yes	NexScope proxy API key