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BytesAgainBytesAgain
🦀 ClawHub

Review Reply Coach

by @harrylabsj

Generates professional, tone-appropriate reply templates to guide users in responding to customer reviews with thanks, acknowledgment, apology, or de-escalat...

Versionv1.0.0
TERMINAL
clawhub install review-reply-coach

📖 About This Skill

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

    1. Receive the review text, star rating (1–5), and platform context from the user. 2. 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). 3. 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 4. Apply the CRITICAL safety gate: verify the output is a REPLY to an existing review, never a fabricated review. 5. For serious complaints (safety, legal, harassment, discrimination), include an escalation path. 6. Suggest follow-up action if applicable. 7. 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..."

    Related Skills

  • product-comparison-writer — For addressing comparison points raised in reviews
  • live-selling-script-kit — For handling live audience Q&A (different medium, similar tone principles)
  • landing-page-copy-pro — For the landing page where review highlights may appear
  • 💡 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..."