Ecommerce.Amazon Market Product Search logo

Ecommerce.Amazon Market Product Search

Organization
nexscope-ai
ecommerce.amazon-market-product-search

Multi-dimensional Amazon product search and filtering based on Nexscope data, covering 14 marketplaces, with support for historical monthly snapshot lookback. Trigger when the user mentions Nexscope product search, Amazon product filtering, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot review, product search, monthly sales/revenue, ABA keyword product discovery, price range filtering, new product discovery, multi-condition combined filtering, product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot. Even if the user does not explicitly mention "Nexscope", if their need involves Amazon product search, filtering, comparison, or product exploration by category/brand/seller dimensions, this skill should also be triggered.

Overview

Publishernexscope-ai
Repositorynexscope-ecommerce-skills
Skill nameecommerce.amazon-market-product-search
Stars
104
Forks
14
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nexscope-ai on GitHub. Read the source before you install it.

Installation

Install the Ecommerce.Amazon Market Product Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nexscope-ai/nexscope-ecommerce-skills/tree/main/ecommerce-amazon-market-product-search
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nexscope-ai/nexscope-ecommerce-skills.git /tmp/nexscope-ecommerce-skills
mkdir -p .claude/skills
cp -r /tmp/nexscope-ecommerce-skills/ecommerce-amazon-market-product-search .claude/skills/ecommerce.amazon-market-product-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ecommerce.Amazon Market Product Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ecommerce.Amazon Market Product Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ecommerce.Amazon Market Product Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Nexscope Product Search

This skill guides you on how to search and filter Amazon products via Nexscope across multiple dimensions, helping Amazon sellers discover products, analyze competitors, and explore market opportunities.

Core Concepts

Nexscope Product Search supports multi-dimensional product retrieval with 16 query types, single or multi-condition AND combinations, and historical monthly snapshot lookback from January 2024. Data covers pricing, BSR rankings, monthly sales, FBA fees, and profit analysis.

Key differentiator: This tool is for searching and filtering across products. If you need detailed trend data (sales/price/BSR history) for a specific ASIN, use the Nexscope Product Detail skill instead.

Data Fields

Response data covers the following categories (see references/api.md for complete field reference):

  • Basic info: ASIN, title, brand, listing URL, images (main + list), parent ASIN, variation count, weight, size
  • Pricing & profit: current price, sale price (after coupon), strikethrough price, coupon, FBA fees (with detail breakdown), platform fee, profit amount & rate
  • Sales: monthly sales units, monthly revenue, daily sales, daily revenue (values of -1 = cannot estimate)
  • Rankings: BSR rank, category, sub-category rankings
  • Ratings: rating score, rating count
  • Listing info: listing date, days online
  • Seller: Buybox seller name/ID/country, FBA status, seller count
  • Listing features: A+ content, video, brand store

Supported Marketplaces

US (United States), GB (United Kingdom), DE (Germany), FR (France), IN (India), CA (Canada), JP (Japan), ES (Spain), IT (Italy), MX (Mexico), AE (United Arab Emirates), AU (Australia), BR (Brazil), SA (Saudi Arabia)

Default marketplace is US. Use us when the user doesn't specify a marketplace.

Note: Nexscope uses lowercase codes (e.g., us, gb, de), and UK is coded as gb (not uk).

How to Invoke

  • API Endpoint: POST /sorftime/amazon/productQuery (complete params/response/error codes in references/api.md)
  • Python Script: python scripts/amazon_market_product_search.py '<JSON params>' [--inline]
  • Cost constraint: This tool consumes credits; the same session and parameter combination is called only once by default, with a 24h local cache in the script. On failure or empty results, do not automatically retry with different keywords, pagination, or postal codes; inform the user about additional consumption before continuing to search.

Output strategy (script default behavior):

  • Always write the full response to <cwd>/nexscope/<YYYY-MM-DD>/<session>/data/nexscope-sorftime-amazon-product-query-<timestamp>.json (<cwd> is the working directory at script execution time, i.e. the current project directory in Claude Code; <session> is taken from the SESSION_ID env var, auto-grouped by user task; do not write to /tmp, error if current directory is not writable)
  • Response body <= 8 KB: print full JSON to stdout after saving
  • Response body > 8 KB: print only summary to stdout after saving (top-level fields, common counts like total/costToken, length of largest list field + first 3 samples)
  • Add --inline to force full output to stdout (still saves to disk)

Data reading tip: Check the summary first to decide if it's enough; when specific fields are needed, prefer using jq or ConvertFrom-Json to extract from the saved json file on demand, avoiding loading the entire JSON into context.

How to Build Queries

The key parameters are marketplace (required), queryMode, queryType, and queryValue. The query system has two modes and 16 filter types that can be combined flexibly.

Principles for Building Queries

  1. Always specify the marketplace: Use lowercase site codes, e.g., us, de, jp
  2. Choose the right query mode: Use queryMode=1 for a single filter; use queryMode=2 to combine multiple filters with AND logic
  3. Match queryType with queryValue format: Each queryType expects a specific format - see the table below. Mismatched formats will cause errors
  4. Mind price units: Price filters (queryType=8) use smallest currency unit (cents for USD), so $19.99 = 1999
  5. Use open ranges when appropriate: Omit one end for open range - ,1000 means "up to 1000"; 100, means "100 or more"
  6. Use queryMonth for historical comparison: Format yyyy-MM; compare with a second call without queryMonth to see changes over time

Query Types (queryType, for queryMode=1)

queryTypeNamequeryValue FormatExample
1ASIN SimilarASINB0CVM8TXHP
2CategoryNodeId3743561
3BrandBrand nameAnker
4Seller NameStore nameAnkerDirect
5Seller IDSellerIdA294P4X9EWVXLJ
6ABA KeywordKeywordPower Bank
7Title/Attribute MatchKeywords10,000mAh 30W
8Price Rangemin,max (in cents)1,1000 (=$0.01~$10)
9Monthly Sales Rangemin,max100,1000
10Seasonal ProductsMonth list1,2,3 (peak in Jan-Mar)
11Listing Date Rangestart,end (yyyy-MM-dd)2024-06-01,2024-12-01
12Rating Rangemin,max3,5
13Review Count Rangemin,max10,500
14Rank Rangebsr_min,bsr_max;sub_min,sub_max500,5000;1,100
15FulfillmentFBA / FBMFBA,FBM
16Variation Countmin,max1,50

Important: queryType=1 (ASIN Similar) finds products similar to the given ASIN, not the ASIN itself. To query a single product's detail, use the Nexscope Product Detail skill.

Historical Snapshots (queryMonth)

Set queryMonth (format yyyy-MM) to query a past month's product data snapshot. This lets users compare historical prices, rankings, and sales with current data.

  • Supported range: January 2024 to present (~2 years)
  • US, GB, DE support full "unlimited" lookback mode
  • Other sites support Top 100 products only in lookback
  • AU, BR, IN do not support lookback

Query Examples for Common Scenarios

1. Competitors of a given ASIN

queryMode: 1, queryType: 1, queryValue: B0CVM8TXHP, marketplace: us

2. Browse a category's top products

queryMode: 1, queryType: 2, queryValue: 3743561, marketplace: us

3. Analyze a brand's product portfolio

queryMode: 1, queryType: 3, queryValue: Anker, marketplace: us

4. Search by ABA keyword

queryMode: 1, queryType: 6, queryValue: Power Bank, marketplace: us

5. Discover seasonal products (Q4 peak)

queryMode: 1, queryType: 10, queryValue: 10,11,12, marketplace: us

6. Compare historical vs current data

queryMonth: 2024-11, queryMode: 1, queryType: 2, queryValue: 3743561, marketplace: us
-> Compare with current data (no queryMonth) to see price/sales changes

7. Multi-condition: new FBA products with good sales

queryMode: 2
queryValue: [{"QueryType":11,"Content":"2024-06-01,"},{"QueryType":9,"Content":"300,"},{"QueryType":15,"Content":"FBA"}]
marketplace: us

8. Find low-price high-sales products

queryMode: 2
queryValue: [{"QueryType":8,"Content":",2000"},{"QueryType":9,"Content":"500,"}]
marketplace: us

9. Check a seller's product portfolio

queryMode: 1, queryType: 4, queryValue: AnkerDirect, marketplace: us

Display Rules

  1. Present data only: Show query results in clear tables without subjective business advice
  2. Ranking clarification: When showing ranking data, remind users that lower values mean better rankings
  3. Pagination notice: Search results return max 100 products per page, up to 200 pages. If results are large, show highlights and remind users to paginate
  4. Sales estimation caveat: Values of -1 in sales/revenue fields mean "cannot estimate" - explain this to the user rather than showing -1 directly
  5. Error handling: When a query fails, explain the reason based on the msg field and suggest adjusting query criteria

Important Limitations

  • Pagination: Max 100 products per page, max 200 pages
  • Historical snapshots: AU, BR, IN do not support historical lookback
  • Non-structured data: Results do not support secondary analysis via _dataQuery_executeDynamicQuery
  • Sales estimation: Products in non-standard categories may return -1 for sales fields
  • ABA keyword search (queryType=6): Currently only supports ABA keywords, not arbitrary search terms

User Expression & Scenario Quick Reference

Applicable -- Product search and filtering on Amazon:

User SaysScenario
"Find the top-selling products in this category"Category exploration
"What are Anker's hot-selling products"Brand analysis
"What are the competitors for this ASIN"Competitor discovery
"Help me find some seasonal products"Seasonal product discovery
"Which new products have monthly sales above 500"Filtered product discovery
"Price snapshot for this category during last year's peak season"Historical snapshot comparison
"What else does this seller sell"Seller portfolio
"Help me filter FBA products with profit margin above 30%"Profit-focused filtering
"Products with 1000+ monthly sales and 4+ star rating"Multi-condition filtering
"Products with wireless charger in the title"Title keyword search

Not applicable -- Needs beyond product search:

  • Detailed trend/history data for a specific ASIN (use Nexscope Product Detail)
  • ABA search term ranking data (use ABA Data Explorer)
  • Advertising / PPC strategy
  • Product reviews content analysis
  • Patent or trademark checks

Boundary judgment: When users say "competitor analysis" or "market research", if they need to discover and compare products across dimensions (category, brand, price range, etc.), this skill applies. If they need historical trend curves for a specific ASIN, use the Product Detail skill. If they need keyword search volume data, use ABA Data Explorer.

Authentication

Set the NEXSCOPE_API_KEY environment variable. If credentials are missing or expire, visit https://www.nexscope.ai/help/skills-external-access?co-from=skillNS to top up credits.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ecommerce.Amazon Market Product Search AI skill do?

Multi-dimensional Amazon product search and filtering based on Nexscope data, covering 14 marketplaces, with support for historical monthly snapshot lookback. Trigger when the user mentions Nexscope product search, Amazon product filtering, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot review, product search, monthly sales/revenue, ABA keyword product discovery, price range filtering, new product discovery, multi-condition combined filtering, product search, competitor research, category analysis, brand bestsellers, seller...

Why use Ecommerce.Amazon Market Product Search on TypingMind?

Because you install it once and use it with any model. Ecommerce.Amazon Market Product Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ecommerce.Amazon Market Product Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nexscope-ai/nexscope-ecommerce-skills/tree/main/ecommerce-amazon-market-product-search. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ecommerce.Amazon Market Product Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ecommerce.Amazon Market Product Search?

As many as you like. As long as a model supports skills, you can use Ecommerce.Amazon Market Product Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ecommerce.Amazon Market Product Search AI skill free?

It is published on GitHub by nexscope-ai. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇