Review Responder

Use this skill when an operator is actively running the Google Business Profile review-response workflow for one of their configured client accounts. Specific triggers: 'check for new reviews,' 'run the review check for [client],' 'new review came in for [client],' 'draft a reply to the [reviewer] r

Automatically monitors Google Business Profile reviews across one or more client accounts, drafts professional responses tuned to star rating and industry, and routes drafts to a configurable approval channel before posting. Designed for agencies and consultants managing reviews on behalf of clients.

Trigger

This skill activates during scheduled review checks (heartbeat) and when an operator responds to a pending review approval message.

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Configuration

All paths and channels are read from a single config file: review-responder.config.json in the skill's data directory. If it doesn't exist, create one from this template on first run:

json

{
  "script_path": "~/review-responder/gbp_reviews.py",
  "clients_dir": "~/review-responder/clients/",
  "approval_channel": "telegram",
  "telegram_chat_id": "",
  "email_recipient": "",
  "webhook_url": "",
  "default_industry": "general",
  "memory_file": "approval-patterns.json"
}

Configuration fields

• script_path: Absolute path to the gbp_reviews.py CLI. Defaults to ~/review-responder/gbp_reviews.py but can live anywhere.
• clients_dir: Directory containing per-client config files. Each client gets its own subdirectory or JSON entry.
• approval_channel: One of telegram, email, webhook, or chat. Determines where draft replies are sent for approval. See Approval Channels below.
• default_industry: Industry profile applied when a client doesn't specify one. See Industry Compliance Profiles below.
• memory_file: Where to log approval patterns for the learning layer.

Per-client overrides

Each client in clients_dir can override industry, approval_channel, and tone_notes (free-text guidance specific to that business). Example client config:

json

{
  "client_id": "smithdental",
  "business_name": "Smith Family Dental",
  "industry": "medical",
  "approval_channel": "email",
  "email_recipient": "[e-mail]",
  "tone_notes": "Dr. Smith is warm but understated. Avoid exclamation points."
}

---

Review Check Flow (Heartbeat)

1. Load review-responder.config.json and enumerate all clients in clients_dir.
2. For each client, run:

   text

   python3 {script_path} check --client {client_id}
   

3. For each new unanswered review:
   • Apply the client's industry profile (or default_industry if none specified)
   • Draft a response following the Response Guidelines and the industry profile's constraints
   • Cross-reference against the approval-patterns memory file for operator-specific adjustments (e.g., if the operator consistently shortens 5-star replies for this client, default to shorter)
4. Route the draft to the operator via the configured approval_channel (see below).
5. Do NOT post the reply automatically. Wait for operator approval.

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Approval Channels

The approval message format stays consistent across channels; only the delivery method changes.

Standard approval message

text

📝 New Review for [Business Name]

⭐ [star_rating] from [reviewer_name]
💬 "[review comment]"

My draft reply:
"[your drafted response]"

Reply OK to post, or send your edits.
(Review ID: [review_id] | Client: [client_id])

Telegram (approval_channel: telegram)

Send the message to the configured telegram_chat_id. The operator replies in the Telegram thread.

Email (approval_channel: email)

Send the message as a plain-text email to email_recipient. Subject line: Review approval needed — [Business Name]. The operator replies to the email; treat the reply body as the approval response.

Webhook (approval_channel: webhook)

POST a JSON payload to webhook_url containing the review draft and metadata. Useful for custom dashboards or Slack relays. Expected response: { "decision": "approve" | "edit" | "skip", "edited_text": "..." }.

Chat (approval_channel: chat)

Surface the draft directly in the current chat session. Use this mode when the operator is actively interacting with the skill rather than receiving async notifications.

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Approval Flow

When the operator responds to a draft (via any channel):

• "OK", "post it", "send it", "approved": Post the draft as-is:

  text

  python3 {script_path} reply --client {client_id} --review {review_id} --reply "{approved response}"
  

  Confirm once posted: "Done — reply posted for [reviewer_name]'s review." Log to the memory file as approved_as_is.

• Edited text: Treat any reply that isn't a recognized approval/skip keyword as replacement text. Confirm before posting: "Got it — posting your version now." Log the edit to the memory file with a diff summary (length delta, key word changes) so the learning layer can pick up patterns.

• "Skip", "ignore", "don't reply": Do not reply to that review. Remove it from pending. Log as skipped.

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Response Guidelines

Tone principles

• Warm, professional, and human — not corporate or robotic
• Specific to what the reviewer said (never generic "thanks for your review!")
• Concise: 2-4 sentences max
• Match the energy of the review without being over the top
• Layer in any tone_notes from the client config

By star rating

5 stars

• Thank them warmly and reference something specific they mentioned
• Reinforce what they loved ("We're glad [specific thing] made a difference")
• End with a light invitation to return or share with others
• Keep it brief; don't overdo it on a great review

4 stars

• Thank them and acknowledge specific positives
• If they mentioned something that could improve, acknowledge it gracefully without being defensive
• Show you're listening: "We appreciate the feedback on [topic] and are always looking to improve"

3 stars

• Thank them for taking the time
• Acknowledge both the positives and the concern
• Show genuine interest in making it right: "We'd love the chance to do better next time"
• Optionally invite them to reach out directly

1-2 stars

• Lead with empathy, not defensiveness: "We're sorry to hear this wasn't the experience you deserved"
• Acknowledge the specific issue without making excuses
• Offer a path forward: invite them to contact the business directly
• Keep it short and dignified; do not argue or over-explain
• Never blame the reviewer or question their experience

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Industry Compliance Profiles

Industry profiles enforce constraints and tone defaults appropriate to specific business types. Apply the profile from the client config (or default_industry) on every draft.

medical (HIPAA-aware drafting)

Note on terminology: this profile applies HIPAA-aware drafting constraints — it instructs the assistant to avoid referencing PHI in public review replies. It does not certify the operator's overall workflow as HIPAA-compliant. Covered entities are responsible for their own compliance program; this skill is one input.

Hard rules (never violate, regardless of star rating):

• NEVER reference or confirm any medical conditions, diagnoses, treatments, medications, procedures, or health details, even if the reviewer mentioned them publicly
• NEVER confirm or deny that someone is or was a patient
• Keep responses general: "your experience," "your visit," "your care" — not "your diagnosis" or "your treatment"
• If the reviewer shared health details, respond to the sentiment and experience only
• When inviting follow-up, use "please contact our office" — never suggest discussing their "case" or "medical records"

Tone defaults: professional, reassuring, brief.

legal

Hard rules:

• Never confirm or discuss case details, legal advice, or attorney-client relationships
• Never speculate about outcomes or imply guarantees
• Avoid language that could be interpreted as a new attorney-client communication
• For dissatisfied reviewers, direct them to the firm's office line rather than offering legal commentary

Tone defaults: measured, professional, no flourishes.

restaurant

Hard rules: none specific, but stay grounded.

Tone defaults: warmer and more conversational than medical/legal. Food-specific callouts welcome ("glad the carbonara hit"). For complaints, offer a direct contact for the manager.

retail

Hard rules:

• Don't speculate about specific products or stock issues you can't verify
• For return/refund disputes, direct to customer service, not public dialogue

Tone defaults: friendly, helpful, solution-oriented.

general

No industry-specific constraints. Fall back to base Tone Principles and By Star Rating guidance.

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Approval Pattern Learning

Log each approval interaction to the memory file (memory_file in config). Use the log to surface patterns and adjust future drafts.

What to log per review

json

{
  "client_id": "smithdental",
  "review_id": "abc123",
  "stars": 5,
  "draft": "Thank you, Maria...",
  "decision": "edited",
  "final": "Thanks Maria...",
  "length_delta_words": -8,
  "timestamp": "2026-03-20T14:22:00Z"
}

How to apply patterns

Before drafting any new reply, scan the log for the same client and look for trends across the last 10-20 interactions:

• If length_delta_words is consistently negative for a given star rating, default to shorter drafts for that client at that rating
• If certain words/phrases are routinely stripped (e.g., "incredibly", "truly"), avoid them on future drafts for that client
• If the operator consistently skips 1-star reviews from anonymous reviewers, surface that as a default rather than drafting one

Surface insights to the operator periodically (e.g., once a week or on the 20th interaction): "I've noticed you usually shorten 5-star replies for Smith Dental by about 10 words. Want me to default to shorter going forward?"

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Checking Pending Reviews

To see what's waiting for approval:

text

python3 {script_path} pending

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Things to Avoid

• Generic filler: "We value all our customers," "Your feedback is important to us"
• Mentioning the star rating directly: "Thanks for the 5 stars!"
• Being defensive about negative reviews
• Making promises the business can't keep
• Using the reviewer's full name unless they used it in their review
• Emojis (unless the business brand is very casual and the operator approves it)
• Violating the active industry profile's hard rules under any circumstance

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Dependencies

• Python 3 with: google-auth, google-auth-oauthlib, requests
• Client config files in the directory specified by clients_dir
• For Telegram: a Telegram channel/chat configured and a working bot token
• For email: SMTP credentials or a relay
• For webhook: an HTTPS endpoint that accepts POST and returns the decision JSON

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Privacy and Data Handling

Unlike most skills in this catalog, this one ships executable Python code (gbp_reviews.py, get_client_token.py, oauth_server.py) that makes real network calls and posts content publicly to Google Business Profile. Be honest with the user about that scope.

What the skill does over the network

• Calls Google's My Business API v4 (mybusiness.googleapis.com) to fetch unanswered reviews and to post replies on behalf of the operator's configured clients. These calls use the client's own OAuth credentials and refresh tokens, which the operator obtains and stores locally.
• Sends draft approval messages through whichever approval_channel the operator configured (Telegram, email, webhook, or in-chat). Each of those uses the operator's own credentials and infrastructure; the skill does not bundle credentials or route through any author-controlled service.
• Posts the approved reply text to the corresponding Google review only after explicit operator approval. Drafts are never auto-posted.

Credentials and local data

• Per-client OAuth credentials (oauth_client_id, oauth_client_secret, refresh_token) live in JSON files under clients_dir. These are the operator's credentials for the operator's own clients. The skill does not transmit them anywhere except to Google's token endpoint (https://oauth2.googleapis.com/token) for the standard OAuth refresh flow.
• Review polling state (review_log.json) and pending drafts (pending/) are stored locally under the skill's directory.
• Approval-pattern learning state (memory_file, default approval-patterns.json) is stored locally.

Hard guardrails

• No auto-posting. Every reply requires an explicit operator approval through one of the configured channels. The skill must not post a reply without that approval.
• No PHI in public replies. When the active client's industry profile is medical, the assistant must never reference health conditions, treatments, diagnoses, or patient status in the public reply — even if the reviewer disclosed those details themselves.
• No exfiltration of credentials. The assistant must never quote, log, summarize, or transmit oauth_client_secret, refresh_token, or any other credential field into approval messages, drafts, logs, or chat outputs.
• No bulk export of client data. The skill is for the operator's own ongoing review workflow. It must not dump consolidated client lists, credentials, or review histories into external destinations without explicit operator instruction for that specific export.

No telemetry

The skill does not collect or transmit usage data, client identifiers, review content, or any other information back to its author, ClawHub, or any third party. (The Google API, Telegram, your SMTP relay, and any webhook target will each have their own logs — consult those services' policies.)

Compliance scope

The medical industry profile applies HIPAA-aware drafting constraints to public review replies. It does not certify the operator's overall workflow as HIPAA-compliant, and it does not turn this skill into a HIPAA-covered service. Operators in regulated industries (medical, legal, financial) remain responsible for their own compliance programs and should review the constraints in this skill against their own policies before using it in production.