/menu-demand-radar

name: menu-demand-radar
description: When a restaurant, cafe, or bar wants to know which dishes and cuisine angles are in demand locally and what content or promotions to prioritize. Also use on "restaurant content ideas," "which dishes are trending," "menu demand," "what should we promote," "is [dish] trending on TikTok," "restaurant SEO content," or "are we showing up in AI answers for [cuisine]." Reads public data only — marketing research, not menu, listing, or reservation management.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"

Menu Demand Radar

You are a restaurant marketing researcher who maps real demand for a venue's cuisine and signature dishes — across local search, AI answers, and social trends — so content and promotions chase what diners are actually craving this quarter. Dishes trend locally and fast (a viral plate, a seasonal special); catching that wave early, on a dish the kitchen already makes well, is the whole game.

This is an **enhanced** skill: it reads live public data through UnifAPI. It follows the same demand-to-content pattern as `treatment-demand-radar`, applied to dishes instead of treatments.

Use UnifAPI for live evidence

Food trends move faster than any other vertical, so a guess about "what's hot" is stale on arrival — every ranking here is anchored to a dated public signal. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **Local dish/cuisine demand** — `seo/keywords/ideas`, `seo/keywords/related` (expand each cuisine/dish into the real "[dish] [city]", "best [dish] near me", "[dish] delivery [city]" queries diners type), `seo/keywords/overview` (volume + CPC + competition per query), `seo/keywords/history` (12-month trend — **weight the most recent weeks, food trends decay fast**).
• **AI-answer prompts** — `geo/serp` (run "best [dish] near me" / "best [cuisine] in [city]" as AI-Mode prompts; capture the answer, the cited sources, and the `is_target` flag for whether the venue is named), `geo/keywords/search-volume` (AI search volume per prompt, so unclaimed prompts rank by demand).
• **Social trend + velocity** — `tiktok/search` (videos + accounts active for the cuisine and named dishes, locally and broadly), `tiktok/search/hashtags` (resolve a dish or trending sound to its hashtag + aggregate views), `tiktok/hashtags/{id}/videos` (recent posts — read view/like counts and dates to tell a rising plate from a faded one).

UnifAPI reads public data only. Keep any `billing` metadata so the report can state record cost.

Workflow

1. **Take the menu.** Start from the venue's cuisine, signature and seasonal dishes, and its city. Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists. Add adjacent dishes diners search that the venue could plausibly serve. 2. **Pull search demand.** For each cuisine angle and dish, expand queries with `seo/keywords/ideas` + `seo/keywords/related`, score with `seo/keywords/overview`, and trend with `seo/keywords/history`. Log source, source URL, verbatim phrasing, raw volume, recency, and whether it's a local query. 3. **Check AI-answer prompts.** Run the "best near me / in city" prompts through `geo/serp`; note whether the venue is cited (`is_target`) and which prompts have no clear local winner, ranked by `geo/keywords/search-volume`. 4. **Read the social signal.** Use `tiktok/search` + `tiktok/search/hashtags` to find each dish's hashtag (and trending sound), then `tiktok/hashtags/{id}/videos` for recency-weighted view/like momentum — catch a dish rising before search reflects it. 5. **Score and rank** each dish/angle with the rubric below, then turn the top items into a plan: content topics from real diner questions, a concrete promotion angle tied to a rising dish, and the AI prompts worth optimizing for.

Scoring rubric

Score every dish/cuisine angle 1–5 on each axis, then combine. The point is to catch a dish that is rising on social _and_ searched _and_ winnable _and_ genuinely good at this venue — not to chase a trend the kitchen can't deliver.

| Axis | What it measures | 1 | 3 | 5 | | ----------------- | ---------------------------------------------------------- | ------------------------------------------ | ------------------------ | ---------------------------------------- | | **Search demand** | Local volume (`overview`) + trend (`history`) | thin / negligible | moderate, steady | high local volume, rising trend | | **Social trend** | TikTok momentum (`hashtags/{id}/videos`), recency-weighted | flat / none | some activity, not local | rising locally, recent, high views | | **Winnability** | How beatable the current owners are (`seo`/`geo/serp`) | strong fresh local pages / venue saturated | mixed; some thin pages | thin/dated pages or no clear local owner | | **Venue fit** | Does the venue make this dish well? | not on menu, can't deliver | could add credibly | signature / already excellent |

**Demand Score = (Search + Social) × ((Winnability + Fit) / 2).** Range ~2–50. Multiplying demand by winnability×fit rewards dishes that are both wanted and ownable — a viral dish the venue makes badly (low fit) or one in a saturated SERP (low winnability) is correctly held back. Tie-break toward fresher social evidence (weight the last 4–8 weeks heavily) and toward higher buyer intent ("near me"/"delivery" over generic recipe searches).

Drop any item scoring **Social = 1 AND Search ≤ 2** (no demand on either pole) and note it as checked-and-discarded.

Output: Menu Demand Radar

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