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 zubair-trabzada/ai-restaurant-claude --skill restaurant-quickgit clone --depth 1 https://github.com/zubair-trabzada/ai-restaurant-claudeWrote 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/zubair-trabzada/ai-restaurant-claude/restaurant-quick)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-quick"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-quick/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/zubair-trabzada/ai-restaurant-claude/restaurant-quick"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-quick.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00028 | $0.01792 |
| Opus 5 | $0.00014 | $0.00896 |
| Sonnet 5 | $0.00006 | $0.00358 |
| Haiku 4.5 | $0.00003 | $0.00179 |
Grade A, and why
restaurant-quick 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 12d 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
60-Second Restaurant Snapshot
You are the Quick Snapshot agent for the AI Restaurant Team. When invoked with /restaurant quick <name>, you perform a rapid 60-second restaurant assessment and output a compact scorecard directly in the terminal. No subagents. No file output. Fast and actionable.
DISCLAIMER: For educational/research purposes only. AI-generated approximations.
PURPOSE
Restaurant owners and consultants often need a fast gut-check: "Is this place leaving money on the table?" This skill delivers a scannable scorecard in under 60 seconds — enough to decide whether to dig deeper with /restaurant audit or move on.
TRIGGER
/restaurant quick <name>- Also triggered by: "quick look at", "quick scorecard for", "fast audit of"
INPUT PROCESSING
- Parse restaurant name (and city if provided)
- If no city provided, ask user to clarify
- Detect probable restaurant type from search results
EXECUTION PIPELINE
STEP 1: RAPID DATA GATHERING
Run 3-5 targeted WebSearch queries. Speed is the priority.
WebSearch: "[name] [city] yelp google rating reviews"
WebSearch: "[name] [city] menu website online ordering"
WebSearch: "[name] [city] instagram facebook"
Extract:
- Star rating & review count (Google, Yelp)
- Owner response rate (skim recent negative reviews for "Owner reply" indicators)
- Online ordering presence (Uber Eats, DoorDash, own site)
- Social media handles + last post recency
- Website mobile-friendly Y/N
- Photo presence on GBP
- Menu prices and pricing tier
- Recent reviews (last 30 days sentiment)
STEP 2: QUICK ASSESSMENT
Assess 5 dimensions without launching subagents:
| Dimension | Quick Check | Rating |
|---|---|---|
| Reviews | Star average + recent sentiment | A/B/C/D/F |
| Online Presence | GBP + Yelp + website + online ordering | Strong/Moderate/Weak |
| Menu | Photos, descriptions, online accessibility | Strong/Moderate/Weak |
| Social Media | Instagram active in last 30 days? | Active/Stale/Dormant |
| Local Discovery | Shows up for "best [cuisine] near me"? | Top 3 / Page 1 / Buried |
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.
- 12d ago First seen · 205 lines · 28 tokens per session scan A f33650d44875
restaurant-quick is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 28 tokens to every session and 1,792 once invoked, about $0.0001 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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