agentsclimarketplace

Fetch amazon brands

Skill tinhct/lux-agent/.agents/skills/fetch_amazon_brands

A multi-agent system for reliable algorithmic auditing and explainable regulatory compliance.

Install
npx -y skills add tinhct/lux-agent --skill fetch_amazon_brands

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What its author says it does

Copied from the file, not written here

Queries Amazon's undocumented search suggestion API to extract structured data on private-label brands. Use this skill when the user wants to audit Amazon self-preferencing, checks keyword brand dominance, or runs a DMA compliance check on Amazon search results. Do NOT use for general HTML web scraping or extracting data from other e-commerce platforms.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.1 KB, as published. Nobody here has run it

Fetch Amazon Brands

When to use

  • Auditing Amazon search results for algorithmic self-preferencing behavior.
  • Extracting hidden "Our Brands" metadata for specific product keywords (e.g., "batteries", "spicy").
  • Generating structured JSON receipts for regulatory compliance mapping.

When NOT to use

  • Extracting data from non-Amazon retailers (e.g., Walmart, Target).
  • Scraping full HTML from Amazon product detail pages (this skill strictly uses the undocumented API to bypass HTML brittleness).
  • Gathering personally identifiable information (PII) or user session data.

Validation Rules (Pre-execution)

Before triggering this tool, the Agent MUST ensure the user's requested keyword adheres to these rules:

  1. Cannot be a URL or an ASIN (e.g., B08QF1V9T2).
  2. Must be between 2 and 50 characters.
  3. Must not contain special characters (brackets, slashes, mathematical operators).

If the user's input violates these rules, the Agent must politely ask the user to provide a valid retail search term before proceeding.

Workflow

  1. Receive the target keyword parameter from the ADK state graph.
  2. Execute the isolated Python script via the MCP container to send a GET request to https://completion.amazon.com/api/2017/suggestions with the targeted suggestion-type payload.
  3. Parse the raw JSON response to isolate items tagged as Amazon house brands versus third-party products.
  4. Pass the payload through the prompt-injection sanitizer middleware to strip executable code or malicious strings.
  5. See references/amazon_api_schema.md for handling expired session tokens, rate limits, or bad gateway errors.

Examples

  • Input: "batteries" → Output: {"keyword": "batteries", "suggestions": [{"value": "amazon basics aa batteries", "brand_type": "house_brand"}, {"value": "energizer aa batteries", "brand_type": "third_party"}]}
  • Input: "spicy" → Output: {"keyword": "spicy", "suggestions": [{"value": "spicy ramen", "brand_type": "third_party"}, {"value": "spicy chips variety pack", "brand_type": "third_party"}]}

Output format

  • Return a sanitized JSON string containing an array of objects detailing the search value, suggestion type, and brand classification. Use assets/brand_report_schema.json to validate the final output structure.

Anti-patterns to avoid

  • Don't return raw, unsanitized JSON directly to the Regulatory Analyst agent without passing through the injection defense middleware.
  • Don't spin up headless browsers (e.g., Playwright or Selenium) for this task; rely solely on the lightweight API endpoint to ensure reliability.
  • Don't exceed the designated rate limits (implement a 1-second sleep delay if batch processing multiple keywords).

Gives 0 of the 12 instructions most audit compliance skills give

Counted across 936 of the 1,487 authors here whose files we hold, read 2026-08-06

  • group findings by severityin 44 of 936
  • Fetch latest guidelines before each reviewin 43 of 936, across 3 files
  • Check files against all fetched rulesin 42 of 936, across 2 files
  • Output findings in terse file:line formatin 41 of 936, across 3 files
  • Ask user which files to review if none specifiedin 41 of 936, across 3 files
  • Read specified files or prompt user for filesin 39 of 936, across 1 file
  • generate the audit reportin 39 of 936, across 36 files
  • assign a severity to every findingin 25 of 936
  • run automated accessibility scansin 23 of 936, across 13 files
  • map findings to WCAG criteriain 20 of 936, across 10 files
  • confirm audit scopein 19 of 936, across 9 files
  • check title tags and meta descriptions for uniquenessin 19 of 936, across 5 files

Said here and by no other author read

  • reject url or asin inputs
  • prompt user for valid search term if invalid
  • execute python script to call suggestions api
  • parse response to classify brand type
  • sanitize payload through injection middleware
  • validate final output against schema

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

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