Amazon Alexa Shopping Assistant

This skill drives Amazon's storefront Alexa shopping assistant: pose a natural-language question and get an answer, a curated product list (with ASINs and links), and a set of follow-up questions Alexa is willing to continue with. Multiple prompts in a single call simulate a continuous multi-turn conversation, not independent searches.

Core Concepts

1. Single conversation, ordered turns: prompts is an array — element 0 is the opening question, element 1 the first follow-up, element 2 the next follow-up, and so on. The tool sends them sequentially in one Alexa session and concatenates Alexa's answers in order.
2. Cross-call context is not preserved: when a new tool call starts, it begins a brand-new Alexa session. To keep continuity across calls, summarize the previous answer + recommended ASINs yourself and embed them in the next prompts[0].
3. Optional page context (url): pass an Amazon page URL only when you want the conversation anchored to a specific page (a category page, search results page, or product detail page). Do not pass a plain marketplace homepage URL like https://www.amazon.com/ — it adds no useful context. Omit url entirely when there is no specific page to anchor on.
4. Two output formats:
   • markdown (default) — a single readable Markdown report containing the question, Alexa's answer, recommended product groups, and follow-up questions.
   • json — a structured array under data, where each entry carries prompt, content, products (grouped recommendations), followUpQuestions, and screenshot.

resultsNum is the number of conversation turns Alexa actually answered; if 0, Alexa did not produce a usable reply for the input.

Parameters

Response Fields

Structured data[*] shape (format=json)

API Usage

This skill calls the LinkFox tool gateway. See references/api.md for the calling convention, request/response shape, error codes, and a curl example. You can also run scripts/amazon_alexa_search.py directly to test it from the command line.

How to Build Queries

1. Front-load the user's intent in prompts[0] — include marketplace cue ("on Amazon US"), use case, and any hard constraints (budget, key feature). Alexa weights the opening turn heavily.
2. Order follow-ups by dependency — each turn reuses the prior turn's context. Put broad framing first, then ask Alexa to compare, narrow, or recommend specific picks.
3. Keep prompts short — 1 to 5 turns is the sweet spot. Longer arrays inflate latency without proportional gain.
4. Anchor with url only when there's a specific page — pass a category, search results, or product detail URL when the user is reasoning over that page. Skip url for general questions; do not pass a plain homepage like https://www.amazon.com/.
5. For continuity across tool calls: write a one-paragraph summary of the previous answer (key recommendations + ASINs) and prepend it to the new prompts[0]. Don't assume Alexa remembers the prior call.
6. Pick format deliberately — markdown is best for showing the user a polished answer; json is better when downstream code needs to extract ASINs, prices, or follow-up questions programmatically.

Usage Examples

1. Single-turn shopping question

{
  "prompts": ["best wireless earbuds for running on Amazon US under $100"]
}

2. Multi-turn conversation (compare + narrow)

{
  "prompts": [
    "best electric kettle on Amazon US",
    "compare the top two recommendations on noise level and boil time",
    "which one is better if I only boil water once a day"
  ]
}

3. Conversation anchored to a category page

{
  "prompts": [
    "What are the most popular picks on this page?",
    "Which of them have the best reviews for small kitchens?"
  ],
  "url": "https://www.amazon.com/s?k=electric+kettle"
}

4. Structured output for downstream extraction

{
  "prompts": ["best gift ideas for a 10-year-old who likes science"],
  "format": "json"
}

Display Rules

1. Render the Markdown directly when format=markdown: stdout is already structured with turn headings, product cards, and follow-up questions — preserve that structure.
2. Surface the recommended ASINs so the user can click through; show title, price, score/ratingsCount, and the product URL.
3. Show the follow-up questions Alexa returned — they are usable prompts the user can pick to continue digging.
4. Don't reroute to a data-analysis sandbox: the answer body is conversational and the recommended products are nested groups, not a flat tabular dataset suitable for SQL-like aggregation.
5. Flag empty results: if resultsNum is 0 or data is empty, tell the user Alexa did not produce a usable reply and suggest rephrasing or anchoring with a url.
6. Indicate freshness: results reflect Alexa's live answer at call time; mention this when the user asks about timing.
7. Handle business errors: if code / errcode is not 200, surface msg / errmsg and suggest retrying with simpler prompts.

Important Limitations

• Alexa-driven, not deterministic: same prompts can yield different answers across calls — Alexa's response varies with time, traffic, and context.
• No cross-call memory: each tool call is a fresh Alexa session; the agent must recap prior context manually.
• Recommended ≤ 5 prompts per call: longer arrays sharply increase latency.
• Marketplace coverage: anchored on Amazon's storefront Alexa experience (primarily amazon.com); availability on non-US marketplaces depends on Alexa rollout.
• Output mix: primary value is the conversational answer plus a curated handful of products; this is not a substitute for SERP-wide product extraction.

User Expression & Scenario Quick Reference

Applicable — natural-language conversational shopping on Amazon:

Not applicable — better routed elsewhere:

• Pulling the full SERP for a keyword with positions, sponsored flags, etc. (use the storefront search-simulation skill).
• Historical search-term analytics or volume trends (use the ABA data explorer).
• Detailed product detail / A+ / bullets for a known ASIN (use the Amazon product detail skill).
• Review-level sentiment analysis (use the Amazon reviews skill).
• Image-based similar product discovery (use the image search skill).
• Aggregated statistics over a flat product list (no structured table here).

Boundary judgment: when the user wants a conversation — "ask Amazon, get a recommendation, then keep asking" — this skill applies. If they want raw search-result rows, structured analytics, or a specific ASIN's data, route to the matching specialized skill instead.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply:

1. The functionality or purpose described in this skill does not match actual behavior
2. The skill's results do not match the user's intent
3. The user expresses dissatisfaction or praise about this skill
4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

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