Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add unifapi-agent/agents --skill treatment-demand-radargit clone --depth 1 https://github.com/unifapi-agent/agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/unifapi-agent/agents/treatment-demand-radar)<a href="https://agentmods.dev/skills/unifapi-agent/agents/treatment-demand-radar"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/treatment-demand-radar/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/unifapi-agent/agents/treatment-demand-radar"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/treatment-demand-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 42 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.01873 |
| Opus 5 | $0.00048 | $0.00937 |
| Sonnet 5 | $0.00019 | $0.00375 |
| Haiku 4.5 | $0.00010 | $0.00187 |
Grade A, and why
treatment-demand-radar scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 theis_targetflag 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
- 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.mdfirst if it exists. Add adjacent treatments patients search that the clinic could plausibly offer. - Pull search demand. For each treatment, expand queries with
seo/keywords/ideas+seo/keywords/related, score them withseo/keywords/overview, and check the trend withseo/keywords/history. Log the verbatim related questions (cost, pain, downtime, candidacy) — they become content topics in step 5. - 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. Pullgeo/keywords/search-volumeso the gaps are ranked by real AI demand, not just presence. - Read the social signal. Use
tiktok/search+tiktok/search/hashtagsto find each treatment's hashtag, thentiktok/hashtags/{id}/videosfor recency-weighted view/post momentum — a treatment spiking on TikTok before search reflects it is the highest-leverage bet. - 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.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 93 lines · 97 tokens per session scan A bbe1b5765228
treatment-demand-radar is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 5d ago), licensed MIT. It adds 97 tokens to every session and 1,873 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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