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 reproducible.

# Patient Question Content Plan — [Practice], [City] — [date]

| Priority | Topic / working title                          | Intent         | Demand | Who owns it today                  | Target query         |
| -------- | ---------------------------------------------- | -------------- | ------ | ---------------------------------- | -------------------- |
| 82       | "How much do dental implants cost in [city]?"  | cost (booking) | high   | generic aggregators; no local page | implants cost [city] |
| 64       | "Invisalign vs braces: which is right for you" | comparison     | mid    | one competitor ranks               | invisalign vs braces |
| 38       | "Does teeth whitening hurt?"                   | pain           | high   | WebMD owns it                      | teeth whitening pain |

## Per top topic

Working title, the patient question it answers, target query, intent (from seo/keywords/intent), local angle, proving source(s) incl. Reddit thread URLs.

## AI-answer prompts

Prompts (from geo/serp) the practice should be cited for but isn't.

## Discarded

One line per cluster checked and set aside, with why.

Guardrails

• Marketing research only — not dental or clinical advice. This is a marketing agent, not a dentist; it surfaces what patients ask and content angles, and makes no clinical claims about procedures or outcomes. Any clinical content the practice publishes should be reviewed by a licensed professional.
• Read-only ("eyes, not hands"): it plans; the practice's own team (and assistant) writes and publishes. It never posts anywhere on the practice's behalf, and it does not manage the Google Business Profile or any listing.
• Confirmed vs inferred: label what's read off a source (volume, intent class, citation, a verbatim Reddit question) versus what's deduced (winnability, the local call).
• Demand and trend signals are public-data estimates — present ranges and dates and treat them as a dated snapshot, not a guarantee. Reddit skews toward strong opinions; weight by recurrence across threads, not a single loud one.
• Every recommended topic must cite the public source that proves patients are asking. No source, no recommendation — and no fabricated volumes or quotes.

Related Skills

• dental-reputation-benchmark (Dental Marketing): the reviews / local-pack side for this practice — the prominence needed to rank for the topics this skill surfaces.
• content-opportunity-brief (Content Strategy Agent): the general-purpose demand-to-ranked-topics workflow this plan is built on.
• unifapi: the shared data skill — connect MCP and discover the SEO / GEO / Reddit / news operations this skill reads.