Draft a reply to one customer review, voice-scored, with escalation flags.

MITAuto-check passed

Install Review Response

skills CLI
$ npx skills add indranilbanerjee/digital-marketing-pro --skill review-response -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install indranilbanerjee/digital-marketing-pro review-response --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/review-response .claude/skills/review-response && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-response
GitHub stars
862
Used in
1 other repo
Token cost
~1.6k tokens
SKILL.md length
766 words
Files
1
Skills in repo
162
Repo updated
First seen
Licence
MIT

At a glance

Draft a reply to one customer review, voice-scored, with escalation flags.

  • Works in 12 steps: Load brand context: Read… → Apply brand voice settings: Load… → Classify review sentiment and severity:… → …
  • SKILL.md covers Purpose, Input Required, Process and Output, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Response is an agent skill from indranilbanerjee/digital-marketing-pro. Draft a reply to one customer review, voice-scored, with escalation flags. "reply to this 1-star review"

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: An open-source AI marketing operating system for strategy, SEO, AEO/GEO, paid media, content, CRM, and analytics - grounded in brand context, human approval, and verifiable… The licence is MIT.

Example prompts

  • “reply to this 1-star review”
  • “/review-response”

Workflow steps

12 steps, taken from the first numbered list in SKILL.md.

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load…
  2. Apply brand voice settings: Load voice-and-tone guidelines and any channel-specific style rules for the review platform — review responses…
  3. Classify review sentiment and severity: Categorize as positive (4-5 stars), neutral (3 stars), or negative (1-2 stars) — further classify…
  4. For negative reviews: Acknowledge the specific concern by name, express genuine empathy without generic platitudes, take responsibility…
  5. For positive reviews: Express sincere gratitude, reinforce the specific aspect the reviewer praised, add a personal or humanizing touch…
  6. For neutral reviews: Acknowledge the balanced feedback, address any specific concerns raised with actionable detail, highlight relevant…
  7. Check brand guidelines for approved response language: Verify the response against any restricted terms, required disclosures, legal…
  8. Apply platform conventions: Adjust response length, formatting, and tone for platform norms — Google (concise), Yelp (conversational), G2…
  9. Score response for brand voice alignment: Evaluate the drafted response against brand voice parameters — tone, formality, warmth, and…
  10. Check for common pitfalls: Ensure the response avoids defensiveness, blame-shifting, over-promising, disclosing private information, or…
  11. Optimize for SEO where applicable: On platforms where responses are indexed (Google, Yelp), naturally incorporate relevant keywords and…
  12. Generate batch variations: If responding to multiple similar reviews, vary the language, structure, and opening to avoid…

What it can do on your machine

Read from SKILL.md and the folder at commit 9e949f3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Response loads about 1.6k tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 766 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from indranilbanerjee/digital-marketing-pro at commit 9e949f3, republished under its MIT licence (© indranilbanerjee). 766 words, ~1,600 tokens.

Download SKILL.mdSave it as .claude/skills/review-response/SKILL.md (or your agent's skills folder).
name
review-response
description
Draft a reply to one customer review, voice-scored, with escalation flags. "reply to this 1-star review"

/digital-marketing-pro:review-response

Purpose

Generate professional, brand-aligned review responses for positive, neutral, and negative reviews across any platform. Ensures every response maintains brand voice, addresses the reviewer's specific points, and follows best practices for reputation management and customer recovery.

Input Required

The user must provide (or will be prompted for):

  • Review text: The full text of the review to respond to
  • Rating: Star rating (1-5 stars)
  • Platform: Where the review was posted (Google, Yelp, G2, Capterra, Trustpilot, Amazon, TripAdvisor, App Store, etc.)
  • Reviewer name: Display name of the reviewer (optional — for personalization)
  • Specific issue mentioned: Key complaint, praise, or topic raised in the review (optional — for targeted response)
  • Business context: Any internal context about the situation — was the issue resolved, is there a known product bug, was there a service failure (optional — helps craft an accurate response)
  • Batch mode: If responding to multiple reviews, provide them as a set for consistent tone and varied language
  • Response speed requirement: Whether the review needs an urgent response (crisis situation) or standard turnaround
  • Internal resolution status: Whether the issue has been fixed, is in progress, or is unresolved (for negative reviews — helps determine what to promise)

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply brand voice, compliance rules for target markets (skills/context-engine/compliance-rules.md), and industry context. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load restrictions and relevant category files. Check for custom templates at ~/.claude-marketing/brands/{slug}/templates/. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  2. Apply brand voice settings: Load voice-and-tone guidelines and any channel-specific style rules for the review platform — review responses often require a warmer, more personal tone than other brand communications
  3. Classify review sentiment and severity: Categorize as positive (4-5 stars), neutral (3 stars), or negative (1-2 stars) — further classify negative reviews by severity level: minor complaint, service failure, product defect, or safety/legal issue
  4. For negative reviews: Acknowledge the specific concern by name, express genuine empathy without generic platitudes, take responsibility where appropriate, offer a concrete resolution path with specifics, and move the conversation offline with a direct contact method (email or phone)
  5. For positive reviews: Express sincere gratitude, reinforce the specific aspect the reviewer praised, add a personal or humanizing touch, and encourage continued engagement — mention related products, services, or referral programs where natural
  6. For neutral reviews: Acknowledge the balanced feedback, address any specific concerns raised with actionable detail, highlight relevant brand strengths without being defensive or dismissive, and invite further dialogue to improve their experience
  7. Check brand guidelines for approved response language: Verify the response against any restricted terms, required disclosures, legal disclaimers, or mandated response elements in the brand guidelines
  8. Apply platform conventions: Adjust response length, formatting, and tone for platform norms — Google (concise), Yelp (conversational), G2 (professional), TripAdvisor (hospitality-focused), etc.
  9. Score response for brand voice alignment: Evaluate the drafted response against brand voice parameters — tone, formality, warmth, and personality — and adjust until the response sounds authentically on-brand
  10. Check for common pitfalls: Ensure the response avoids defensiveness, blame-shifting, over-promising, disclosing private information, or using repetitive language across multiple review responses
  11. Optimize for SEO where applicable: On platforms where responses are indexed (Google, Yelp), naturally incorporate relevant keywords and business name without sounding forced
  12. Generate batch variations: If responding to multiple similar reviews, vary the language, structure, and opening to avoid templated-sounding responses that damage authenticity
Show full SKILL.md (191 more words)Show less

Output

A structured review response package containing:

  • Ready-to-post review response: Primary response tailored to the platform's character limits, conventions, and audience expectations
  • Alternative versions: Formal and casual variants for flexibility, plus a shorter version if the primary response exceeds platform norms
  • Response guidelines: Platform-specific best practices applied — recommended length, optimal tone, ideal response timing, and SEO considerations
  • Escalation recommendation: For negative reviews — whether this requires manager involvement, legal review, product team notification, or immediate offline outreach
  • Response quality score: Brand voice alignment rating and checklist of best practices applied
  • Follow-up note: Suggested internal action items if the review reveals a systemic issue worth addressing
  • SEO keywords applied: For indexed platforms, keywords naturally incorporated into the response
  • Tone analysis: Breakdown of the response tone (empathetic, grateful, professional, warm) matched against brand voice settings
  • Response timing recommendation: Optimal window for posting the response based on platform algorithms and customer expectations
  • Template extraction: If the response is strong, a generalized template version saved for future similar reviews

Agents Used

  • content-creator — Response copywriting, tone calibration, personalization, platform-appropriate language, alternative version drafting
  • brand-guardian — Voice consistency enforcement, guideline compliance, restricted language checks, escalation assessment, legal sensitivity review

© indranilbanerjee, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/review-response of indranilbanerjee/digital-marketing-pro.

Open the folder on GitHubat commit 9e949f3

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in indranilbanerjee/digital-marketing-pro, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Review Response next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Response compared with similar skills
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Review Response this skillindranilbanerjee/digital-marketing-pro8621 repos~1.6kAutomated safety check: PassMIT
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Responsive Unitsthedaviddias/Front-End-Checklist74k—~472Automated safety check: PassMIT
CSS Custom Propertiesthedaviddias/Front-End-Checklist74k—~492Automated safety check: PassMIT
Customs and Trade Complianceaffaan-m/ECC277k5 repos~7.2kAutomated safety check: PassApache-2.0
Persona Customer Supportgoogleworkspace/cli31k—~303Automated safety check: PassApache-2.0

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Questions about Review Response

What does Review Response do?

Draft a reply to one customer review, voice-scored, with escalation flags. Review Response is an agent skill from indranilbanerjee/digital-marketing-pro. Draft a reply to one customer review, voice-scored, with escalation flags.

How do I install Review Response in Claude Code?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill review-response -a claude-code`. Or copy the skill folder (skills/review-response in indranilbanerjee/digital-marketing-pro) into .claude/skills/review-response in your project. Claude Code loads it when a task matches its description.

How do I install Review Response in Codex?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill review-response -a codex`. Or copy the skill folder (skills/review-response in indranilbanerjee/digital-marketing-pro) into .agents/skills/review-response in your project. Codex loads it when a task matches its description.

Can I use Review Response in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add indranilbanerjee/digital-marketing-pro --skill review-response -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-response, .gemini/skills/review-response, .github/skills/review-response and .opencode/skills/review-response in your project.

What does Review Response need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Response is instructions for the agent only.

Does Review Response access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Response safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Response use?

Review Response is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Response use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Response?

Skills that share tags, products or a category with Review Response: Escalation And Reply Tone (Kiln-AI/Kiln, 5.2k stars), Responsive Units (thedaviddias/Front-End-Checklist, 74k stars), CSS Custom Properties (thedaviddias/Front-End-Checklist, 74k stars) and Customs and Trade Compliance (affaan-m/ECC, 277k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Response?

indranilbanerjee (a GitHub user) maintains it in indranilbanerjee/digital-marketing-pro, which has 862 GitHub stars. The repository holds 162 skills in this directory. The repository was last updated on October 9, 2026.

Source: indranilbanerjee/digital-marketing-pro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.