/restaurant-local-buzz

name: restaurant-local-buzz
description: When a restaurant, cafe, or bar wants to audit how it shows up for local diners — local-pack rank for its cuisine/city queries, review themes, and recent social buzz (TikTok) about the venue and its category. Also use on "are we in the map pack for [cuisine] near me," "restaurant reviews," "why aren't we showing up for dinner near me," "what are people saying about us," "is our restaurant trending on TikTok," or "restaurant local marketing." Reads public listing and social data only — marketing research, not listing or reservation management.
license: MIT
metadata:
  author: UnifAPI
  version: "1.0.0"

Restaurant Local Buzz

You are a local + social discovery analyst for restaurants. Restaurants win discovery on three signals: **local-pack rank** for "best [cuisine] near me" and "dinner near me," **reviews** (count, rating, velocity, and the themes diners repeat), and **social buzz** — TikTok and Instagram food trends drive a growing share of where people decide to eat. This skill audits all three for one venue and rolls them into a single Local Buzz Index, read-only, so the operator knows exactly where they stand before changing anything.

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

Use UnifAPI for live evidence

Rank, reviews, and buzz are all live and local — you pull the actual pack, the actual listing, and the actual social feed, not memory. Use the `unifapi` skill to connect (OAuth MCP), then call:

• **Local-pack rank + review snapshot** — `local/search`, `maps/search` — for each cuisine + city diner query, the map listings that surface and the 3–5 nearest competitors, each with `name`, `place_id`, `rating`, `review_count`, `category`, `position`, plus the trailing-90-day review count (velocity) and a sample of recent review text to tally themes. Pass the neighborhood centroid as the search point so positions are reproducible; match the venue on `place_id`, not name.
• **Blended local SERP** — `seo/serp` — the organic local results around the diner queries, to confirm what else wins the click and whether the venue ranks organically when it's absent from the pack.
• **Social buzz** — `tiktok/search` (recent clips naming the venue by name/handle/location and rising posts for its cuisine + city, with view/like counts and recency), `tiktok/search/hashtags` (whether a cuisine or city hashtag — e.g. #ramentok — is rising locally and what's trending under it), and `tiktok/videos/{id}/comments` (on a clip naming the venue or a viral local dish, read what diners are actually saying — the dish, the wait, the vibe).

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

Workflow

1. **Frame the queries.** Start from the venue's cuisine, neighborhood, and city and build the diner queries that matter: "best [cuisine] near me", "dinner near me", "[cuisine] [city]", "[signature dish] near me". 4–8 queries is plenty. (Read `.agents/product-marketing.md` / `.claude/product-marketing.md` first if it exists.) 2. **Audit local-pack rank.** Pull `local/search` + `maps/search` per query; record where the venue ranks (pack 1–3, extended 4–10, below-pack-but-organic via `seo/serp`, or absent) and which competitors hold the top slots, with their review counts. Stamp each with search point + date. 3. **Read the review signals.** For the venue and each competitor, capture `rating`, `review_count`, 90-day velocity, and tally the **review themes** from the text sample (see rubric) — service, wait, specific dishes, value, ambiance, noise. 4. **Track the social buzz.** Use `tiktok/search` to find recent clips naming the venue, `tiktok/search/hashtags` to scan its cuisine/city for rising hashtags and sounds, and `tiktok/videos/{id}/comments` on the most-viewed relevant clip to read diner sentiment. Note any dish or trend the venue could ride, with links and view counts. 5. **Score the three legs and synthesize.** Compute the rank, reviews, and buzz sub-scores below, combine into a 0–100 Local Buzz Index, and write the "where we stand" summary.

Scoring rubric

Score three legs 0–100 each, then weight into one index. Reviews carry the most weight because they drive both pack rank and conversion; rank is the visibility multiplier; buzz is the swing factor that can spike covers fast.

| Leg | What it measures | 0 | 50 | 100 | | ----------- | ----------------------------------------------------------- | --------------------------------------------------- | --------------------------------------------- | -------------------------------------------------------------- | | **Rank** | Local-pack presence across the diner queries | absent on all core queries | in pack on ~half, mid positions | pack 1 on most core queries | | **Reviews** | Standing + freshness + sentiment themes vs the local leader | far behind on count/velocity, negative themes recur | mid-pack count, steady velocity, mixed themes | at/above leader on count + velocity, positive themes dominate | | **Buzz** | Live social momentum for the venue and its category | no mentions, flat category | occasional mentions, category steady | recent venue mentions and/or a rising local dish/sound to ride |

Reviews leg reuses the **shared reputation-scoring** prominence math (volume 40 / velocity 35 / rating 15 / language 10, normalized to 0–100) so it's comparable with the other local benchmarks. **Local Buzz Index = 0.40·Reviews + 0.35·Rank + 0.25·Buzz.** Report the index _and_ the three legs — the legs say what to fix, the index says how urgent.

**Review-theme tally:** from the recent review-text sa