Amazon Reviews

SKILL.md

---
name: linkfox-amazon-reviews
description: 按ASIN获取并分析亚马逊商品评论，支持15个站点(含美国站)，按星级筛选评论。当用户提到亚马逊评论、美国站评论、商品评价、买家投诉、差评、好评、星级评分、评论分析、评论情感、产品改良建议、Vine评论、已验证购买评论、竞品评论研究、Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback时触发此技能。即使用户未明确说"评论"，只要其需求涉及读取、筛选或分析亚马逊商品的买家评论，也应触发此技能。
---

# Amazon Product Reviews

Fetch and analyze Amazon product reviews to help sellers extract actionable insights from customer feedback.

## Core Concepts

This tool retrieves real customer reviews for a given Amazon ASIN across **15 marketplaces**. You can control how many reviews to fetch per star rating (1-5 stars, up to 100 each), sort by recency or helpfulness, and apply various filters. Only one ASIN per request; for multiple ASINs, make separate calls.

## API Usage

All 15 marketplaces (including US) use a single unified endpoint:

- Call `scripts/amazon_reviews.py`, pass `domainCode: "<code>"`. Use `domainCode: "com"` for Amazon.com. See `references/api.md`

## Parameter Guide

| Parameter | Type | Required | Scope | Description | Default |
|-----------|------|----------|-------|-------------|---------|
| asin | string | Yes | All | Amazon product ASIN | - |
| star1Num | integer | No | Main endpoint | 1-star reviews to fetch (0-100) | 10 |
| star2Num | integer | No | Main endpoint | 2-star reviews to fetch (0-100) | 10 |
| star3Num | integer | No | Main endpoint | 3-star reviews to fetch (0-100) | 10 |
| star4Num | integer | No | Main endpoint | 4-star reviews to fetch (0-100) | 10 |
| star5Num | integer | No | Main endpoint | 5-star reviews to fetch (0-100) | 10 |
| sortBy | string | No | All | `recent` (newest) or `helpful` (most helpful) | `recent` |
| formatType | string | No | All | `current_format` or `all_formats` | `current_format` |
| domainCode | string | No | Main endpoint | Marketplace code (see Supported Marketplaces); use `com` for US | `com` |
| filterByKeyword | string | No | Main endpoint | Filter reviews by keyword (max 1000 chars) | - |
| reviewerType | string | No | Main endpoint | `all_reviews` or `avp_only_reviews` (verified only) | `all_reviews` |
| mediaType | string | No | Main endpoint | `all_contents` or `media_reviews_only` | `all_contents` |

### Star Count Defaults

- If no star count fields are provided, `star1Num` to `star5Num` all default to `10`.
- If any star count field is provided, unspecified star counts default to `0`.

## Supported Marketplaces

| Marketplace | Code |
|-------------|------|
| United States | `com` |
| Canada | `ca` |
| United Kingdom | `co.uk` |
| Germany | `de` |
| France | `fr` |
| Italy | `it` |
| Spain | `es` |
| Japan | `co.jp` |
| India | `in` |
| Australia | `com.au` |
| Brazil | `com.br` |
| Mexico | `com.mx` |
| Netherlands | `nl` |
| Sweden | `se` |
| United Arab Emirates | `ae` |

Use `domainCode` for every supported marketplace. Always confirm the user's intended marketplace.

## Usage Examples

**1. Fetch US reviews (Amazon.com)**
```json
{"asin": "B08N5WRWNW", "domainCode": "com", "star1Num": 10, "star2Num": 10, "star3Num": 10, "star4Num": 10, "star5Num": 10, "sortBy": "recent"}
```

**2. Fetch negative reviews with keyword filter (Germany)**
```json
{"asin": "B08N5WRWNW", "domainCode": "de", "star1Num": 30, "star2Num": 30, "filterByKeyword": "quality", "reviewerType": "avp_only_reviews"}
```

**3. Fetch 5-star reviews with media (Japan)**
```json
{"asin": "B08N5WRWNW", "domainCode": "co.jp", "star5Num": 50, "star1Num": 0, "star2Num": 0, "star3Num": 0, "star4Num": 0, "sortBy": "helpful", "mediaType": "media_reviews_only"}
```

**4. Fetch only 3-star reviews (explicit star mode)**
```json
{"asin": "B0FP5C63HZ", "domainCode": "com", "star3Num": 100}
```

## Display Rules

1. **Present data clearly**: Show reviews grouped by star rating with key fields: rating, title, text, date, verified status, helpful count.
2. **Summarize when appropriate**: For many reviews, provide a theme/pain-point summary before listing individuals.
3. **Highlight actionable insights**: Call out recurring complaints in negative reviews; note praised features in positive reviews.
4. **Vine and verified labels**: Clearly indicate Vine Voice and verified purchase status.
5. **Media indicators**: Note when reviews include images or videos.
6. **Response normalization**: Normalize rating and helpful-count fields for consistent display when the raw response uses marketplace-specific text formats.
7. **Error handling**: When a query fails, explain the reason based on the response message and suggest adjusting parameters.
8. **Single ASIN limitation**: If the user asks about multiple ASINs, make separate requests for each.

## Important Limitations

- **One ASIN per request**: Only a single ASIN can be queried at a time.
- **Per-star cap**: Each star rating returns max 100 reviews per request.
- **Parameter scope**: `filterByKeyword`, `reviewerType`, `mediaType` are available on `/amazon/reviews/list`, including `domainCode: "com"`.
- **No historical snapshots**: Reviews are fetched in real-time.
- **Review text language**: Reviews are returned in their original language as posted.

## User Expression & Scenario Quick Reference

**Applicable** — Tasks involving Amazon product reviews:

| User Says | Scenario |
|-----------|----------|
| "Show me the reviews for this ASIN" | Direct review lookup |
| "Get US reviews for B08N5WRWNW" | Marketplace-specific lookup |
| "What are customers complaining about" | Negative review analysis |
| "Get me all the 1-star reviews" | Star-filtered retrieval |
| "Any common issues in the bad reviews" | Pain point mining |
| "What do people like about this product" | Positive review analysis |
| "Find reviews mentioning 'battery'" | Keyword-filtered reviews |
| "Show me reviews with photos" | Media-filtered reviews |
| "Verified purchase reviews only" | Reviewer-type filtering |
| "Help me analyze competitor reviews" | Competitor review research |
| "Product improvement suggestions from reviews" | Actionable insight extraction |

**Not applicable** — Needs beyond product review data:

- ABA search term data / keyword research (use ABA Data Explorer instead)
- Sales estimation or revenue analysis
- Listing copywriting or A+ content creation
- Advertising / PPC strategy
- Pricing strategy or profit margin calculations

**Boundary judgment**: If "product research" or "competitor analysis" boils down to reading customer reviews for specific ASINs, this skill applies. If it involves search volume, keyword rankings, sales estimates, or market sizing, it does not.

**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 the references. Do not interrupt the user's flow.

<!-- LF_LARGE_RESPONSE_BLOCK -->
## 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/amazon_reviews.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.

> This skill exposes one entry script: `amazon_reviews.py`.

`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 don't 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.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via `read`.
<!-- /LF_LARGE_RESPONSE_BLOCK -->

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