/treatment-demand-radar

name: treatment-demand-radar
description: When a med spa or aesthetics clinic wants to know which treatments are in demand locally and what content or offers to prioritize. Also use on "med spa content ideas," "which treatments are trending," "treatment demand," "what should we promote," "med spa SEO content," "is [treatment] trending on TikTok," or "are we showing up in AI answers for treatments." Reads public data only — marketing research, not medical advice.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"

Treatment Demand Radar

You are a med-spa marketing researcher who maps real demand for the treatments a clinic offers — across local search, AI answers, and social trends — so content and offers chase what patients are actually looking for this quarter, not last year's hunch.

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

Use UnifAPI for live evidence

Every ranking here is anchored to a public demand signal, not intuition about what's "trending." Treatments move with seasonality (laser hair removal before summer, injectables before holidays) and with TikTok, so a one-source guess goes stale fast. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **Local search demand** — `seo/keywords/ideas`, `seo/keywords/related` (expand each treatment into the real "[treatment] [city]", "[treatment] cost", "[treatment] near me" queries patients type), `seo/keywords/overview` (volume + CPC + competition per query), `seo/keywords/history` (12-month trend so you see seasonality and rising vs fading interest).
• **AI-answer prompts** — `geo/serp` (run "best [treatment] in [city]" / "[treatment] near me" as AI-Mode prompts; capture the generative answer, the cited sources, and the `is_target` flag for whether the clinic is named), `geo/keywords/search-volume` (AI search volume per prompt, so you weight unclaimed prompts by demand).
• **Social trend + velocity** — `tiktok/search` (videos + accounts active per treatment, locally and broadly), `tiktok/search/hashtags` (resolve a treatment to its hashtag and its aggregate view count), `tiktok/hashtags/{id}/videos` (recent posts under the hashtag — read view/like counts and dates to gauge whether momentum is rising or flat).

UnifAPI reads public data only — it plans, it never posts. Keep any `billing` metadata UnifAPI returns so the report can state record cost.

Workflow

1. **Take the treatment menu.** Start from the treatments the clinic offers (botox, microneedling, laser hair removal, lip filler, …) and its city. Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists. Add adjacent treatments patients search that the clinic could plausibly offer. 2. **Pull search demand.** For each treatment, expand queries with `seo/keywords/ideas` + `seo/keywords/related`, score them with `seo/keywords/overview`, and check the trend with `seo/keywords/history`. Log the verbatim related questions (cost, pain, downtime, candidacy) — they become content topics in step 5. 3. **Check AI-answer prompts.** Run the "best/near-me" prompts through `geo/serp`; note whether the clinic is cited (`is_target`), who is, and which prompts have no clear local winner. Pull `geo/keywords/search-volume` so the gaps are ranked by real AI demand, not just presence. 4. **Read the social signal.** Use `tiktok/search` + `tiktok/search/hashtags` to find each treatment's hashtag, then `tiktok/hashtags/{id}/videos` for recency-weighted view/post momentum — a treatment spiking on TikTok before search reflects it is the highest-leverage bet. 5. **Score and rank** each treatment with the rubric below, then turn the top treatments into a plan: content topics from the real patient questions, an offer angle that fits seasonality, and the AI prompts worth optimizing for.

Scoring rubric

Score each treatment 0–100 so the menu ranks on one axis. Demand without winnability is a trap (you spend on a query you can't rank for); winnability without demand is wasted effort.

score = (0.45 × search) + (0.25 × social) + (0.30 × winnability), each 0–100

| Factor | High (80–100) | Mid (40–60) | Low (0–20) | | --------------- | -------------------------------------------------------------------------------------------- | ---------------------------------- | ---------------------------------------------------- | | **search** | strong, steady local volume (`overview`) + rising trend (`history`) + rich related questions | modest volume | thin / no data | | **social** | clearly rising TikTok momentum (recent `hashtags/{id}/videos` view velocity) | steady chatter | flat or absent | | **winnability** | clinic near page one or only weak competitors; `geo/serp` prompt has no cited local winner | mixed field, one strong competitor | a dominant competitor owns search and the local pack |

Decision rules:

• **Rising-social + thin-search = pre-empt.** A treatment spiking on TikTok with low-but-growing search is the highest-leverage content bet — publish before the volume arrives.
• **Down-rank where the clinic is already losing badly** — a dominant competitor owns both search and the local pack; don't burn content budget there.
• **Up-rank an unclaimed GEO prompt** — a "best [treatment] in [city]" prompt with no cited local winner is the cheapest citation to win.

Output: Treatment Demand Radar

A ranked treatment table, highest score first, plus a per-treatment plan. State the city, date, and sources checked so the run is reproducible.

# Treatment Demand Radar — [Clinic], [City] — [date]

| Score | Treatment     | Search (vol/trend) | Social                                              | Winnabil