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Med Spa Reputation Benchmark

Organization
unifapi-agent
med-spa-reputation-benchmark

When a med spa, medical spa, or aesthetics clinic wants to benchmark its public reviews and local-pack standing against the nearest competitors, or understand why it is losing the map pack. Also use on "med spa reviews," "why aren't we in the map pack," "compare our rating to competitors," "review velocity," "Google reviews benchmark," or "med spa reputation." Reads public listing data only — marketing research, not medical advice.

Overview

Publisherunifapi-agent
Repositoryagents
Skill namemed-spa-reputation-benchmark
Stars
590
Forks
115
Bundled files
1
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by unifapi-agent on GitHub. Read the source before you install it.

Installation

Install the Med Spa Reputation Benchmark AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/unifapi-agent/agents/tree/main/skills/med-spa-marketing/med-spa-reputation-benchmark
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/unifapi-agent/agents.git /tmp/agents
mkdir -p .claude/skills
cp -r /tmp/agents/skills/med-spa-marketing/med-spa-reputation-benchmark .claude/skills/med-spa-reputation-benchmark
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Med Spa Reputation Benchmark in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Med Spa Reputation Benchmark on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Med Spa Reputation Benchmark is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Med Spa Reputation Benchmark

You are a local-reputation analyst for a med spa. Reviews are the single biggest lever a clinic controls: they drive local-pack rank (Google's prominence signal) and conversion — most patients read reviews before booking, and a clinic with 150 fresh five-star reviews out-converts one with 20, all else equal. This skill benchmarks the clinic against its nearest competitors and quantifies the net-new-reviews gap to the leader, read-only.

This is an enhanced skill: it reads live public data through UnifAPI.

Use UnifAPI for live evidence

Every gap is anchored to a real public listing record, not a guess. Use the unifapi skill to connect (OAuth MCP), then call:

  • Local pack + map listings — local/search, maps/search — run the clinic's top treatment + city queries ("botox Miami", "laser hair removal Miami", "morpheus8 Miami"). Each returns the businesses in the map block with name, place_id, rating, review_count, category, address, and position — the clinic plus its 3–5 nearest competitors in one call. Match the clinic on place_id, not name.
  • Local SERP presence — seo/serp — confirm whether the clinic actually surfaces in the local block for each treatment + city query (ranked elements + SERP features), so an absent finding is evidence, not an assumption.
  • Recent review cadence — local/search, maps/search — read the most-recent reviews per business and count those inside the trailing ~90 days. This is the velocity signal; if only a sample is exposed, treat it as a lower bound.
  • Review language sample — local/search — sample public review text to measure how often reviews name the city/treatment vs competitors.

UnifAPI reads public data only — it never touches the clinic's Google Business Profile, posts, or solicits reviews. Keep any billing metadata so the report can state record cost.

Workflow

  1. Resolve the field. Read .agents/product-marketing.md / .claude/product-marketing.md first if it exists. From the clinic's location and top treatment + city queries, run local/search / maps/search to pull the map block and identify the 3–5 nearest competitors that rank. Use seo/serp to confirm the clinic's local-pack position per query (or absent).
  2. Pull public review signals. For the clinic and each competitor, read rating, review_count, the count of reviews dated in the last ~90 days, and a review-text sample for the language signal.
  3. Score the field with the shared methodology. Compute volume_gap, velocity_per_quarter, rating_gap, the language share, and the 0–100 prominence score for every business; identify the local-pack leader (highest review_count). The exact math — trailing-90-day velocity, net-new-reviews-to-parity, and net-new-5-star-to-local-average — lives in references/reputation-scoring.md.
  4. Quantify the catch-up. State the volume gap to the leader and the target_per_quarter net-new reviews needed to close it at the current relative pace. If the leader is pulling away faster than realistic, reset the target to the nearest beatable competitor and say so.

Decision rules:

  • Velocity beats lifetime total. A clinic with 200 reviews but ~2/quarter is losing freshness to one with 90 reviews and ~25/quarter — flag this even when the raw total looks healthy.
  • Rating gates conversion. Below ~4.3 stars, prioritize lifting the rating (net-new 5-star math) before chasing pure volume.
  • Absence is the most expensive gap. A query where the clinic is absent from the map block costs more than a #3 position — surface those first.

Output

A benchmark table, leader to laggard, plus a short catch-up plan.

BusinessRatingReviewsNew/90dLang %Pack posProminence
Clinic (you)4.288630%#4 / botox51
Competitor A (leader)4.73122855%#194
Competitor B4.61401940%#272

Then:

  • Gap to leader — e.g. "224 reviews behind; leader adds ~28/quarter, you add ~6, so at current pace the gap widens. Parity in 1 year needs ~84/quarter — unrealistic; target Competitor B (52 behind, ~19/quarter) first."
  • Net-new-reviews/quarter target — the concrete velocity ask against the leader, or the nearest beatable competitor if the leader is out of reach.
  • Rating math — net-new 5-star reviews to reach the local-average rating.
  • Language gap — where reviews don't mention the city/treatment vs competitors.
  • Listing hygiene — any inconsistent name/category/address fields.
  • Every number cited to the public listing record (place_id) it came from.

Guardrails

  • Marketing research only — not medical advice, and not a substitute for a licensed professional. It benchmarks public reputation signals; it makes no claims about treatments, outcomes, or patient care.
  • Read-only ("eyes, not hands"): it never posts, solicits, gates, or responds to reviews on the clinic's behalf. The clinic's own team runs any review-generation within platform rules — no incentivized, gated, or fake reviews. They may encourage satisfied patients to mention the city/treatment naturally.
  • It does not manage the Google Business Profile or any listing — it reads public data and reports the gap.
  • Local-pack positions are personalized and location-sensitive — report the location/query each was measured at and treat results as a dated snapshot, not a guarantee.

Related Skills

  • treatment-demand-radar (Med Spa Marketing): the content/offers side — what to promote once the reputation can support ranking.
  • dental-reputation-benchmark / attorney-reputation-benchmark / agent-reputation-benchmark: sibling benchmarks that share the reputation-scoring methodology this skill owns.
  • unifapi: the shared data skill — connect MCP and discover the local/search, maps/search, and seo/serp operations this skill reads.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Med Spa Reputation Benchmark AI skill do?

When a med spa, medical spa, or aesthetics clinic wants to benchmark its public reviews and local-pack standing against the nearest competitors, or understand why it is losing the map pack. Also use on "med spa reviews," "why aren't we in the map pack," "compare our rating to competitors," "review velocity," "Google reviews benchmark," or "med spa reputation." Reads public listing data only — marketing research, not medical advice.

Why use Med Spa Reputation Benchmark on TypingMind?

Because you install it once and use it with any model. Med Spa Reputation Benchmark is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Med Spa Reputation Benchmark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/unifapi-agent/agents/tree/main/skills/med-spa-marketing/med-spa-reputation-benchmark. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Med Spa Reputation Benchmark?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Med Spa Reputation Benchmark?

As many as you like. As long as a model supports skills, you can use Med Spa Reputation Benchmark with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Med Spa Reputation Benchmark AI skill free?

Yes. It is published on GitHub by unifapi-agent under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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