Taobao Shopping

---
name: taobao-shopping
description: Help users decide how to shop on Taobao from public marketplace characteristics, then guide Taobao product search, seller comparison, store-type judgment, variant checks, and buying-risk evaluation using browser-visible evidence only. Use when the user wants to 买淘宝、看同款、逛店铺、比店、判断靠不靠谱、比较卖家、看是否值得在淘宝下单, or needs Taobao-specific shopping guidance without pretending to do hidden API access or payment automation.
---

# Taobao Shopping

Taobao station-native shopping decision skill.

This skill is for users who already know they want to evaluate Taobao itself, not compare across JD, PDD, and Vipshop. For cross-platform same-item price decisions, use `taobao-competitor-analyzer` instead.

What makes this skill useful:
- It helps users judge whether Taobao is a good fit for the purchase.
- It focuses on seller quality, store type, variant clarity, and listing risk.
- It gives a direct buy / compare more / avoid recommendation instead of generic browsing help.

## When To Use

Use this skill when the user is effectively asking:
- 这东西在淘宝上怎么买更靠谱
- 淘宝这家店能不能买
- 同款太多了怎么选
- 帮我看淘宝店铺和商品风险
- 淘宝买这个值不值

Use `taobao-competitor-analyzer` instead when the user wants:
- 京东 / 拼多多 / 唯品会 cross-platform comparison
- lowest visible comparable price across platforms
- whether to switch away from Taobao

## Commerce Matrix

This skill is the Taobao station-native node in the shopping matrix.

Prefer nearby skills when the task shifts:
- `taobao-competitor-analyzer` for cross-platform same-item comparison
- `alibaba-shopping` when the user first needs to choose between Taobao, Tmall, and 1688
- `tianmao` when official flagship and authenticity matter more than marketplace variety

## Workflow

1. Clarify the target product or listing.
2. Normalize the product name or listing title.
3. Search Taobao using browser-visible flows only.
4. Compare the strongest 2-5 candidate listings.
5. Judge seller trust, variant clarity, service promises, and visible pricing conditions.
6. End with a direct recommendation:
   - `可直接优先看`
   - `建议继续比店`
   - `高风险，暂不建议`

## Browser Rules

- Stay in browser-visible pages only.
- Do not use hidden APIs, app-only endpoints, or unofficial scrapers.
- Prefer visible search results, item detail pages, and seller/store pages.
- If results are noisy, narrow by brand, spec, quantity, version, and store type.
- Do not fabricate prices, ratings, or guarantees.

## What To Compare

For each candidate listing, collect when visible:
- title
- displayed price
- variant/specification
- store name
- store type or badges
- sales volume or recent order count
- visible rating / service / logistics signals
- return / shipping / authenticity wording
- URL
- short note on why it looks strong or risky

## Decision Rules

Prioritize these questions:

1. Is the listing clearly the right product and variant
2. Is the seller/store trustworthy enough
3. Is the price believable for the product class
4. Are shipping, returns, and after-sales acceptable
5. Is the listing better than nearby Taobao alternatives

Red flags:
- title is vague or overloaded with unrelated keywords
- variant/specification is unclear
- store signals are weak or inconsistent
- price is suspiciously low without explanation
- product page depends on confusing coupon tricks or hidden conditions

## Output Format

Return a short decision block first:

- `推荐动作`
- `最值得看的店/商品`
- `主要原因`
- `风险点`

Then give a compact comparison table:

| 候选 | 价格 | 规格 | 店铺 | 信号 | 判断 |
|---|---:|---|---|---|---|
| A | ¥... | ... | ... | ... | ... |

Then end with:
- `下一步建议`
- `如果要继续比店，优先看什么`

## Positioning

This skill is part of a shopping decision matrix:
- `taobao-shopping`: Taobao station-native buying guidance
- `taobao-competitor-analyzer`: cross-platform same-item comparison
- `tianmao`: official flagship / authenticity-first buying
- `pdd-shopping`: low-price / group-buy / subsidy-first buying
- `jd-shopping`: trust, self-operated, and fulfillment-first buying