ecommerce.temu-product-query

EHunt Temu Product Query (ehunt/temu/productQuery)

Product Understanding Step

Before running this skill, if the user provides a product, image, ASIN, listing, product ID, or product idea, first identify:

• product type/category
• main use case
• key physical, design, or functional features
• target marketplace, country, or platform
• user's analysis goal

Use this product understanding to choose parameters, filters, competitors, regions, keywords, or analysis dimensions before executing the script.

Core Question

Which Temu products match the user's sourcing, category, sales, price, rating, fulfillment, or listing-activity filters?

When to Use

• User asks to search, filter, or rank Temu products.
• User wants Temu product sourcing ideas by price, sales, reviews, rating, listing date, tags, or fulfillment mode.
• User asks for semi-managed Temu products or region-specific Temu opportunities.
• User provides a product idea and wants comparable Temu products or category examples.

Clarify or Infer Before Querying

If the user request is broad or ambiguous, identify:

• product category or keyword
• target site, country, or semi-managed region
• price, sales, rating, review, or publish-time constraints
• fulfillment mode and listing status
• desired sort metric, such as weekly sales, total sales, price, rating, or revenue

Use sensible defaults when the user's intent is clear: first page, 20 products, weekly sales descending, active listings.

Differs From / Not Applicable

• Use ecommerce.temu-store-query when the user wants seller/store rankings or seller performance.
• Use this skill when the unit of analysis is the individual product.
• Do not use for TikTok Shop products, Amazon products, patent checks, or listing keyword optimization.

Workflow

1. Understand the product/category intent and normalize filters.
2. Resolve category name to ID when the user gives a category name.
3. Query EHunt Temu product data with explicit filters.
4. Return a compact product table with product, sales, revenue, price, rating, reviews, tags, and fulfillment.
5. Add short sourcing observations and recommended follow-up filters.

Output Structure

Default output should include:

• Query assumptions and filters used
• Product table: title/product ID, site/region, category, price, sales, revenue, rating, reviews, tags, fulfillment mode, listing status
• Key observations: strongest products, low-price/high-sales candidates, review gaps, new listings, possible saturation
• Suggested next step: inspect stores, compare TikTok/Amazon demand, or narrow by category/price band

Calls the EHunt data source via the Nexscope proxy gateway, route /api/v1/tools/research/ehunt/temu/productQuery (display name: Temu Product 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.

Built-in Category Search

When the user specifies a category by name rather than ID, first call the category search script to resolve the name to an ID, then pass that ID as categoryHome or categoryBackend.

Key Points

• Pagination: page starts at 1; pageSize defaults to 20, max 100.
• Range parameters: *Begin / *End pairs (price, rating, reviews, total/weekly/daily sales, publish time) form upstream range filters.
• Category: categoryHome (front-end category ID), categoryBackend (back-end category ID).
• Fulfillment mode: isLocal (0 = fully managed, 1 = semi-managed); region for semi-managed regions.
• Listing status: soldOut (0 = listed, 1 = delisted).
• Tags: tags and customTags, comma-separated.
• Sorting: sortBy is a "field-direction" string, e.g. order_week-0 (weekly sales descending, default), price-0, order_total-0, rating-0.

Scripts

Script	Purpose
scripts/ehunt_temu_product_query.py	CLI debug tool; POSTs JSON to the proxy gateway

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_product_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