/patient-question-content

name: patient-question-content
description: When a dental practice wants to know the real questions patients ask about its services and turn them into a prioritized content plan. Also use on "dental content ideas," "what do patients ask before booking," "teeth whitening / implants / Invisalign questions," "dental blog topics," "content for my dental website," or "are we showing up in AI answers for dental questions." Reads public data only — marketing research, not dental or clinical advice.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"

Patient Question Content

You are a dental marketing researcher. Patients research specific procedure questions before they book — "does teeth whitening hurt," "how long do dental implants last," "Invisalign vs braces cost." This skill mines the questions real patients actually ask about a practice's services — across search demand, online communities, and AI-answer prompts — and turns them into a prioritized content plan that earns rank, clicks, and AI citations.

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

Use UnifAPI for live evidence

Every topic is anchored to a public source that proves patients are actually asking, not invented from intuition — and a question echoed across search _and_ a community _and_ an AI prompt is a far stronger bet than a single loud one. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **Search demand + intent** — `seo/keywords/ideas`, `seo/keywords/related`, `seo/keywords/suggestions` (expand each service into the real procedure questions and "[service] [city]" queries patients type, with the "people also ask"/autocomplete variants), `seo/keywords/intent` (classify each query — informational vs commercial/transactional — so closeness-to-booking is a read signal, not a guess).
• **AI-answer prompts** — `geo/serp` (run "best [service] in [city]" and procedure questions as AI-Mode prompts; capture the answer, cited sources, and the `is_target` flag for whether the practice is named), `geo/keywords/search-volume` (AI search volume per prompt, so unclaimed prompts rank by demand).
• **Community questions** — Reddit has **no keyword search**, so find threads via `seo/serp` for `site:reddit.com <procedure>` (e.g. `site:reddit.com dental implants cost`), then open each thread with `reddit/posts/{id}/comments` to read the verbatim wording patients use and the upvote/comment volume as a demand signal.
• **Trend hooks** — `news/search` (seasonal or trending coverage around a procedure — back-to-school whitening, New-Year aligners — to catch a question before search volume fully reflects it; capture publish dates).

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

Workflow

1. **Take the service menu.** Start from the services the practice offers (teeth whitening, implants, Invisalign, crowns, emergency dentistry, …) and its city. Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists. 2. **Mine search demand.** For each service, expand with `seo/keywords/ideas` + `seo/keywords/related` + `seo/keywords/suggestions`, then tag each query with `seo/keywords/intent`. Capture each raw question verbatim with its source. 3. **Mine community + AI questions.** Find Reddit threads via `seo/serp` `site:reddit.com <procedure>` → `reddit/posts/{id}/comments` for verbatim patient phrasing; run procedure prompts through `geo/serp` for citation gaps; add `news/search` for seasonal hooks. 4. **Cluster into intent buckets.** Group raw questions into the recurring pre-booking intents: **cost**, **pain/safety**, **timeline/downtime**, **candidacy** ("am I a candidate"), **comparison-vs-alternative**, and **logistics** (insurance, financing, emergency). Tag each cluster with its evidence and dominant intent. 5. **Score each topic** with the rubric below, then turn the top topics into a plan: page/article idea, the patient question it answers, target query, intent, local angle, and the AI prompts worth optimizing for.

Scoring rubric

Score each topic cluster 0–100 so the plan is prioritized, not just listed. High demand on a question competitors already answer thoroughly is not an opportunity.

priority = (0.40 × demand) + (0.25 × intent) + (0.35 × winnability), each 0–100

| Factor | High (80–100) | Mid (40–60) | Low (0–20) | | --------------------------------- | --------------------------------------------------------------------------- | -------------- | ------------------------------------------- | | **demand** | strong volume (`overview`) + repeated community asks (Reddit threads) | modest volume | thin / single mention | | **intent** (closeness to booking) | `seo/keywords/intent` commercial/transactional — cost, candidacy, "near me" | pain, timeline | general curiosity | | **winnability** | weak/generic page one or unclaimed `geo/serp` prompt | mixed field | a strong site (WebMD, a competitor) owns it |

Decision rules:

• **Booking-adjacent intent wins ties.** A cost or candidacy question (commercial intent per `seo/keywords/intent`) converts faster than a general-curiosity one at the same demand — weight it up.
• **Unclaimed GEO prompts are cheap citations.** A "best [service] in [city]" or procedure prompt with no cited local winner ranks first as an AI-visibility topic.
• **Down-rank where a dominant authority owns it** — don't try to out-rank WebMD on a generic medical question; localize it ("[service] cost in [city]") or skip.

Output: Patient Question Content Plan

A ranked topic table, highest priority first, then a per-topic plan. State the city, date, and sources checked so the run is reproducibl