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 restaurant-local-buzzgit 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/restaurant-local-buzz)<a href="https://agentmods.dev/skills/unifapi-agent/agents/restaurant-local-buzz"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/restaurant-local-buzz.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00125 | $0.02210 |
| Opus 5 | $0.00063 | $0.01105 |
| Sonnet 5 | $0.00025 | $0.00442 |
| Haiku 4.5 | $0.00013 | $0.00221 |
Grade A, and why
restaurant-local-buzz 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 7d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 withname,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 onplace_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), andtiktok/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.
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.
- 7d ago First seen · 97 lines · 125 tokens per session scan A b37d5953b999
restaurant-local-buzz is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 2d ago), licensed MIT. It adds 125 tokens to every session and 2,210 once invoked, about $0.0006 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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