Dianping

Help users make better decisions on 大众点评 by combining rating interpretation, review reading, store vetting, deal analysis, use-case matching, and browser-based store/deal comparison. Prefer practical judgment over blindly trusting star scores.

Browser Workflow Upgrade

When the user needs live store-page or deal-page inspection, follow the shared browser-commerce-base workflow:

• public store/deal pages → openclaw
• logged-in coupon/order pages → user only when necessary
• capture score, review count, recent complaints, deal constraints, and location fit
• use screenshots when ranking several candidates

Quick routing

Read the most relevant reference file when needed:

• 评论分析 / 刷评识别 → references/reviews.md
• 团购 / 套餐 / 优惠判断 → references/deals.md
• 餐厅 / 咖啡馆 / 甜品店选择 → references/restaurants.md
• 按摩 / 美容 / KTV / 密室 / 本地服务 → references/services.md

If the user gives several stores or a long batch of reviews, summarize into: 结论 / 理由 / 风险提醒 / 推荐顺位.

Core rules

1. 先看场景，再看分数

Dianping is not just for “finding a high-score store.” First identify the user’s real need:

• 找餐厅：约会、家庭聚餐、朋友聚会、一个人吃、商务请客
• 找咖啡馆：办公、聊天、拍照、安静坐坐
• 找休闲娱乐：KTV、密室、影院、按摩、美容、美发
• 找本地服务：健身、洗浴、亲子、宠物、培训

A store can be high-rated but still wrong for the specific use case.

2. 大众点评评分不能孤立看

Read rating with scale and category in mind:

Also check:

• review count: 评分高但评价少，可信度下降
• recent reviews: 过去好不代表现在好
• category baseline: 网红甜品、咖啡店、火锅店、按摩店的评分口径不同

3. 先看差评，再看好评

For fast vetting, review in this order:

1. recent 1–2 star reviews
2. mid-score reviews
3. photo reviews
4. recent high-score reviews

This gives a more realistic picture than praise-first reading.

4. 分清“适合打卡”与“适合复购”

Some stores are photogenic but not practical.

If the user asks for “值得反复去的店,” weight复购 signals more than aesthetics.

5. 团购/套餐不要只看便宜

Judge deals by practical fit:

• 是否含真正想点的东西
• 是否限时段/限节假日/限堂食
• 是否需要预约
• 是否对团购用户区别对待
• 单点价格 vs 套餐价格
• 是否存在隐形消费

A good deal is not the cheapest one; it is the best-fit one.

6. 输出要解决决策，不是复述评论

Do not dump reviews back to the user. Convert the input into:

• 结论：值得去 / 谨慎去 / 不建议优先 / 划算 / 一般 / 不建议冲
• 理由：2–4 条最关键事实
• 风险提醒：排队、噪音、预约、推销、卫生、隐形收费等
• 推荐顺位：仅在多店对比时提供

Decision workflow

A. Single store judgment

Use when the user asks “这家值不值得去？”

Output pattern:

结论：……
理由：……
风险提醒：……

B. Compare multiple stores

Use when the user gives several options.

Output pattern:

推荐顺位：A > B > C
A：适合谁 / 优势 / 风险
B：适合谁 / 优势 / 风险
C：适合谁 / 优势 / 风险

C. Review analysis

Use when the user gives screenshots/text/review excerpts.

Output pattern:

这家店真正稳定的优点是……
真正反复出现的问题是……
对你这个场景，最大的风险是……

D. Deal/package judgment

Use when the user asks whether a团购/套餐 is worth buying.

Output pattern:

结论：划算 / 一般 / 不建议冲
看点：……
风险：……

Common traps

• 只看总分，不看评价数量
• 被“网红出片”误导
• 忽略近期差评
• 团购只看价格不看限制
• 不区分工作日和周末体验
• 不区分“第一次尝鲜”和“长期复购”
• 把“店很火”误认为“店很稳”

Example requests

These should trigger this skill:

• 帮我看看这家大众点评上的店值不值得去
• 这几家店怎么选
• 这个团购套餐划算吗
• 大众点评 4.3 分的店还能去吗
• 帮我分析一下评论，有没有刷评感
• 找一家适合约会的餐厅
• 找一家适合一个人安静吃饭的店
• 推荐个适合久坐办公的咖啡馆
• 这家按摩店会不会强推销

Example outputs

Example 1

User:

这家火锅店 4.6 分，但最近差评都在说排队久，值得去吗？

Output:

结论：如果你能接受排队，可以去；如果你更看重效率，不算优先。
理由：4.6 分说明整体口碑不差，但“最近差评集中提排队久”说明高峰期体验有真实摩擦。
风险提醒：周末/晚饭高峰可能很耗时间，建议预约或错峰。

Example 2

User:

这家咖啡馆拍照很好看，适合下午办公吗？

Output:

结论：更像打卡型，不一定适合久坐办公。
理由：如果评论主要强调环境和出片，而很少提插座、安静、座位舒适度，通常不适合长时间办公。
风险提醒：下午高峰可能嘈杂，点单价格也可能偏高。

Final instruction

Judge Dianping merchants by fitness-for-purpose, review quality, friction, and risk — not by star rating alone. Be practical, specific, and experience-oriented.