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Agent skill

Patient Question Content

by unifapi-agent in unifapi-agent/agents

When a dental practice wants to know the real questions patients ask about its services and turn them into a prioritized content plan.

MITAuto-check passedMarketing & SEO

Install Patient Question Content

skills CLI
$ npx skills add unifapi-agent/agents --skill patient-question-content -a claude-code

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

GitHub CLI
$ gh skill install unifapi-agent/agents patient-question-content --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/unifapi-agent/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dental-marketing/patient-question-content .claude/skills/patient-question-content && 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
patient-question-content
GitHub stars
589
Token cost
~2.2k tokens
SKILL.md length
870 words
Files
2
Skills in repo
47
Repo updated
First seen
Licence
MIT

At a glance

When a dental practice wants to know the real questions patients ask about its services and turn them into a prioritized content plan.

  • Works in 5 steps: Take the service menu. Start from the… → Mine search demand. For each service,… → Mine community + AI questions. Find… → …
  • Tasks that involve Content strategy
  • SKILL.md covers Use UnifAPI for live evidence, Workflow, Scoring rubric and Output: Patient Question…, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Patient Question Content is an agent skill from unifapi-agent/agents. 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.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `README.md`).

It sits in Marketing & SEO, covering Content strategy. It works with Reddit. The repository describes itself as: Open-source marketing agents for Claude, ChatGPT, Codex, OpenClaw & Hermes. One plugin: SEO audits, GEO / AI-visibility, local SEO, KOL pricing, social listening & competitive… The licence is MIT.

When your agent uses it

  • Tasks that involve Content strategy

Example prompts

  • “dental content ideas,”
  • “what do patients ask before booking,”
  • “teeth whitening / implants / Invisalign questions,”
  • “/patient-question-content”

Workflow steps

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

  1. Take the service menu. Start from the services the practice offers (teeth whitening, implants, Invisalign, crowns, emergency dentistry, …)…
  2. Mine search demand. For each service, expand with seo/keywords/ideas + seo/keywords/related + seo/keywords/suggestions, then tag each…
  3. Mine community + AI questions. Find Reddit threads via seo/serp site:reddit.com → reddit/posts/{id}/comments for verbatim patient…
  4. Cluster into intent buckets. Group raw questions into the recurring pre-booking intents: cost, pain/safety, timeline/downtime, candidacy…
  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…

What it can do on your machine

Read from SKILL.md and the folder at commit fb53247. 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 (its code samples are markdown).

    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

Patient Question Content loads about 2.2k tokens when it runs. Until then it costs about 119 tokens; SKILL.md has 870 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~119
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

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 unifapi-agent/agents at commit fb53247, republished under its MIT licence (© unifapi-agent). 870 words, ~2,178 tokens.

Download SKILL.mdSave it as .claude/skills/patient-question-content/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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
metadata.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.
Show full SKILL.md (406 more words)Show less

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.

text
priority = (0.40 × demand) + (0.25 × intent) + (0.35 × winnability), each 0–100
FactorHigh (80–100)Mid (40–60)Low (0–20)
demandstrong volume (overview) + repeated community asks (Reddit threads)modest volumethin / single mention
intent (closeness to booking)seo/keywords/intent commercial/transactional — cost, candidacy, "near me"pain, timelinegeneral curiosity
winnabilityweak/generic page one or unclaimed geo/serp promptmixed fielda 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.

markdown
# 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.
  • 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.

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

Files

SKILL.md and 1 other file in skills/dental-marketing/patient-question-content of unifapi-agent/agents.

  • SKILL.md
  • README.md

Open the folder on GitHubat commit fb53247

Compare with similar skills

Patient Question Content 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.

Patient Question Content compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Patient Question Content this skillunifapi-agent/agents589—~2.2kAutomated safety check: PassMIT
Blog StrategyAgriciDaniel/claude-blog2.3k—~4.4kAutomated safety check: PassMIT
Afa Socialafadtc/afa-dtc-skills168—~2.3kAutomated safety check: PassCustom licence
Blog StrategyInfrasity-Labs/dev-gtm-claude-skills136—~3.8kAutomated safety check: PassMIT
Contentnotque/vexjoy-agent441—~2.9kAutomated safety check: NotesMIT
Audience Researchsocial-media-skills/skills134—~1.8kAutomated safety check: PassMIT

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Works with

Categories

Questions about Patient Question Content

What does Patient Question Content do?

When a dental practice wants to know the real questions patients ask about its services and turn them into a prioritized content plan. Patient Question Content is an agent skill from unifapi-agent/agents. When a dental practice wants to know the real questions patients ask about its services and turn them into a prioritized content plan.

When should I use Patient Question Content?

Patient Question Content fits situations like: tasks that involve Content strategy.

How do I install Patient Question Content in Claude Code?

Run `npx skills add unifapi-agent/agents --skill patient-question-content -a claude-code`. Or copy the skill folder (skills/dental-marketing/patient-question-content in unifapi-agent/agents) into .claude/skills/patient-question-content in your project. Claude Code loads it when a task matches its description.

How do I install Patient Question Content in Codex?

Run `npx skills add unifapi-agent/agents --skill patient-question-content -a codex`. Or copy the skill folder (skills/dental-marketing/patient-question-content in unifapi-agent/agents) into .agents/skills/patient-question-content in your project. Codex loads it when a task matches its description.

Can I use Patient Question Content 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 unifapi-agent/agents --skill patient-question-content -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/patient-question-content, .gemini/skills/patient-question-content, .github/skills/patient-question-content and .opencode/skills/patient-question-content in your project.

What does Patient Question Content need to run?

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

Does Patient Question Content 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 Patient Question Content 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 Patient Question Content use?

Patient Question Content is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Patient Question Content use?

About 2.2k tokens (SKILL.md is roughly 8.7k 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 Patient Question Content?

Skills that share tags, products or a category with Patient Question Content: Blog Strategy (AgriciDaniel/claude-blog, 2.3k stars), Afa Social (afadtc/afa-dtc-skills, 168 stars), Blog Strategy (Infrasity-Labs/dev-gtm-claude-skills, 136 stars) and Content (notque/vexjoy-agent, 441 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Patient Question Content?

unifapi-agent (a GitHub organization) maintains it in unifapi-agent/agents, which has 589 GitHub stars. The repository holds 47 skills in this directory. The repository was last updated on September 5, 2026.

Source: unifapi-agent/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.