Compliant Review Reply Coach
Purpose
This skill generates professional, tone-calibrated responses to customer reviews across e-commerce and app store platforms. Unlike other content-generator skills that create new content, this is a service skill — it coaches the user on how to reply appropriately to existing reviews. It covers three response modes: positive (thank + reinforce brand value), neutral (acknowledge + commit to improvement), and negative (apologize + resolve + take conversation offline). "Coach" emphasizes guidance, templates, and best-practice education — not automated mass-replying. This is the only skill in the pack that does NOT generate new marketing content.
Triggers
- "回复评论"
- "review response"
- "reply to customer review"
- "差评回复"
- "app review reply"
- "商家回复"
- "review reply coach"
- "客户评论回复"
- "好评怎么回"
- "中评怎么回"
Workflow
- Receive the review text, star rating (1–5), and platform context from the user.
- Classify the review tone into one of four categories: Positive (4–5★, praising), Neutral (3★, mixed feelings), Negative (1–2★, disappointed), Angry (1★, emotional/aggressive).
- Select the appropriate response framework:
- Positive: Thank specifically → Reinforce what they liked → Invite future engagement → Subtle upsell (optional, disclosed)
- Neutral: Thank for feedback → Acknowledge the mixed experience → Commit to specific improvement → Offer follow-up
- Negative: Apologize sincerely (without admitting fault if unverified) → Address specific complaint → Offer concrete resolution → Take conversation offline (email/phone)
- Angry: De-escalate first → Acknowledge emotion → Offer resolution path → Escalate if safety/legal issue
- Apply the CRITICAL safety gate: verify the output is a REPLY to an existing review, never a fabricated review.
- For serious complaints (safety, legal, harassment, discrimination), include an escalation path.
- Suggest follow-up action if applicable.
- Deliver the tone-calibrated response ready for the user to post.
Prompt Templates
1. Response from Review (response_from_review)
Purpose: Generate a tone-calibrated reply from a single review.
Input:
${review_text} — The customer's original review${star_rating} — 1 through 5${platform} — Where the review was posted${verified_purchase} — (Optional) whether this is a verified buyer
Output: Complete reply text + tone classification + follow-up suggestion (if applicable).
2. Negative Review Diffuser (negative_review_diffuser)
Purpose: De-escalate an angry or very negative review with empathy and resolution.
Input:
${review_text} — The angry/negative review${specific_issues} — List the specific complaints raised${resolution_options} — What the business can actually do (refund, replacement, investigation)
Output: Empathetic de-escalation reply with: acknowledgment → specific action offered → private contact path → gratitude for feedback.
3. Positive Review Amplifier (positive_review_amplifier)
Purpose: Turn a 4–5★ review into a brand loyalty moment.
Input:
${review_text} — The positive review${what_they_loved} — Specific aspects they praised${upsell_opportunity} — (Optional) whether to include a gentle next-step invitation
Output: Warm thank-you reply that: references their specific praise → reinforces what makes your product great → optionally suggests related products or future engagement.
4. Response Policy Builder (response_policy_builder)
Purpose: Create an internal response policy document for a customer service team.
Input:
${brand_name} — Your brand${review_platforms} — Platforms where you receive reviews${response_sla} — Response time targets (e.g., "24 hours for negative, 48 for positive")${escalation_triggers} — What issues require manager attention
Output: Policy document with: response templates per star rating, tone guidelines, SLA by rating, escalation triggers, do-not-do list.
5. Multi-Language Response (multilanguage_response)
Purpose: Generate the same review response in multiple languages.
Input:
${review_text} — Original review (may be in any language)${response_text} — Your drafted response in your language${target_languages} — Languages needed (e.g., "English, Japanese, German")
Output: Multi-language response table: Language | Response Text | Cultural Note (if applicable).
Output Format
Every response includes:
TONE CLASSIFICATION: [Positive / Neutral / Negative / Angry]
STAR RATING: [★]
PLATFORM: [Platform]
RESPONSE:
[Complete reply text]
FOLLOW-UP: [Suggested next step, or "None required"]
Safety Rules
- CRITICAL: NEVER generate fake positive reviews or impersonate a customer
- CRITICAL: NEVER suggest review manipulation — removal requests, incentivized changes, or review gating
- CRITICAL: Templates are for RESPONDING to existing reviews ONLY
- NEVER write defensive, argumentative, or angry replies — maintain professionalism at all times
- ALWAYS include an escalation path for complaints involving safety, legality, harassment, or discrimination
- ALWAYS take conversations involving personal information or heated disputes to private channels (email, phone, DM)
- NEVER share customer personal information in a public reply
Examples
Example 1: Negative Review Response
Input: Review="收到的衣服颜色和图片完全不一样，面料也很差，非常失望", Rating=2★, Platform="Taobao"
Output: Classification=Negative. Response: "非常抱歉让您有这样的体验...我们已记录您反馈的颜色和面料问题...请您通过旺旺联系我们，为您办理退换货...感谢您的反馈，我们会优化产品图片的色差控制。"
Example 2: Positive Review Amplifier
Input: Review="第二次回购了，面霜保湿效果真的很好，冬天用刚刚好", Rating=5★, Platform="Amazon"
Output: Classification=Positive. Response: "感谢您的持续支持！很高兴面霜在冬天帮到了您...我们最近也推出了同系列的精华，和面霜搭配效果更佳，欢迎了解...期待继续为您服务。"
Example 3: Safety Escalation
Input: Review="这个充电器用了一周就冒烟了，差点着火！", Rating=1★
Output: Immediately escalate: "您的安全是我们最关心的。请立即停止使用该产品，并通过[客服电话/邮箱]联系我们。我们将安排工程师检测并全额退款..."
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