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review-response-writer

review-response-writer

github.com/onewave-aiMarketing & growth39392 installsUpdated 1w ago
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About review-response-writer

review-response-writer is a review-handling writing skill for small businesses. It classifies reviews, drafts replies in the owner’s voice, and flags cases that need escalation, while identifying operational patterns across a backlog.

Key features

  1. 01Learn the owner’s tone from prior replies or a description
  2. 02Classify praise, complaints, mistakes, and suspected fakes
  3. 03Draft specific replies for different review types
  4. 04Flag issues involving injury, discrimination, or legal threats
  5. 05Organize backlogs and identify operational patterns

Use cases

  1. 01Draft a reply to an individual customer review
  2. 02Process a backlog of unanswered reviews
  3. 03Handle a one-star review and assess removal options
  4. 04Find recurring operational issues in customer feedback

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill review-response-writer

After installing, type/review-response-writerin Claude Code to confirm it loaded.

Details

From the project README

Review Response Writer

The response to a bad review is not for the reviewer -- it is for every prospect who reads it for the next five years. Input: the review text (pasted, exported, or fetched from the profile), and on first run a few existing responses or a note on how the owner talks. Output: responses ready to post, plus escalation flags for the ones that need more than words.

Workflow

  1. Learn the voice. From past responses or the owner's description: warmth level, formality, sign-off style, whether they use names. Small-business responses that sound like the actual owner outperform corporate-polished ones -- keep contractions, keep personality.
  2. Triage each review:
    • GLOW (4-5 stars) -- thank specifically (echo the detail they praised, never generic), reinforce the service mentioned (it is searchable text), invite them back.
    • LEGITIMATE COMPLAINT -- acknowledge the specific failure without excuses, state the fix made, take it offline with a real contact, never argue. No coupons in public (it trains complaint-for-discount).
    • UNFAIR/MISTAKEN -- correct the record factually and briefly ("Our records show we honored the quoted price of...") while staying gracious; readers can tell who is being reasonable.
    • SUSPECTED FAKE (no record of the customer, competitor patterns) -- respond once, neutrally ("We have no record of serving you -- contact us and we'll make it right"), and output the platform's removal-request steps with the policy grounds.
    • ESCALATE -- anything mentioning injury, illness, discrimination, legal threats, or an employee by name in an accusation: draft nothing final; flag for the owner and, where serious, counsel, with a holding-pattern response option.
  3. Draft. Responses under 100 words for positives, under 150 for negatives. Vary structure across a batch -- ten identical-template responses read as a bot and undo the effort.
  4. Batch report. For a backlog: respond oldest-negative first (unanswered negatives do compounding damage), then recent positives. Note patterns worth fixing upstream -- three reviews mentioning wait times is an operations finding, not a writing task.

Rules

  • Never admit legal fault, discuss health/medical specifics, or confirm someone was a customer on platforms where that itself is sensitive (medical, legal, financial services -- respond without confirming the relationship).
  • Never argue, never sarcasm, never match the reviewer's temperature. The audience is the reader, not the reviewer.
  • Specificity is the tell of sincerity: echo one concrete detail from every review; a response that could sit under any review helps nobody.
  • Public forgiveness, private resolution: the public reply shows character, the offline contact fixes the problem.
  • Fake-review accusations stay out of the public response -- neutrality publicly, evidence in the removal request.
  • Response velocity matters: recommend responding to negatives within 24-48 hours and set that as the standing cadence.

Quick Commands

  • "Respond to this review" -- single review, voice-matched
  • "Clear my backlog [export/profile]" -- triaged batch with drafts
  • "Handle this 1-star" -- deep treatment: response + offline script + removal assessment if warranted
  • "What are my reviews telling me?" -- the upstream operations patterns

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