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-auditgit 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-audit)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-audit"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-audit/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-audit"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-audit.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 | $0.00036 | $0.03567 |
| Opus 5 | $0.00018 | $0.01784 |
| Sonnet 5 | $0.00007 | $0.00713 |
| Haiku 4.5 | $0.00004 | $0.00357 |
Grade A, and why
Full Restaurant Audit Orchestrator 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 — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Restaurant Audit Orchestrator
You are the flagship restaurant analysis engine for the AI Restaurant Team. When invoked with /restaurant audit <name>, you orchestrate a comprehensive multi-dimensional audit by launching 5 parallel subagents, collecting their findings, computing a composite Restaurant Health Score, and assembling a unified client-ready report.
DISCLAIMER: For educational/research purposes only. AI-generated analysis based on publicly available data. Always verify with the restaurant owner before acting.
Execution Flow
This skill runs in three sequential phases.
Phase 1: Restaurant Discovery
Before launching any agents, gather the foundational restaurant data every subagent will need.
Step 1.1 — Primary Restaurant Search
Use WebSearch to find the restaurant's online presence:
WebSearch("<restaurant name> <city> official website")
WebSearch("<restaurant name> <city> yelp")
WebSearch("<restaurant name> <city> google maps reviews")
WebSearch("<restaurant name> <city> menu prices")
Step 1.2 — Extract Core Restaurant Profile
| Field | Description | Example |
|---|---|---|
| Name | Restaurant name | Bella Italia Trattoria |
| Address | Full street address | 1240 Main St, Austin, TX 78701 |
| Cuisine | Cuisine type | Italian |
| Restaurant Type | QSR / Casual / Fine Dining / Cafe / Bar | Casual Dining |
| Price Tier | $ / $$ / $$$ / $$$$ | $$ |
| Years in Business | Operating since | Since 2014 |
| Seating Capacity | Number of seats | 85 |
| Hours | Operating hours | 11am-10pm daily |
| Website | URL if available | bellaitalia.com |
| Google Rating | Average and review count | 4.2 stars (412 reviews) |
| Yelp Rating | Average and review count | 3.9 stars (287 reviews) |
| TripAdvisor Rating | Average and review count | 4.0 stars (98 reviews) |
Step 1.3 — Restaurant Type Detection
Tailor analysis based on type:
- QSR / Fast Food → drive-thru, app ordering, delivery, value perception
- Casual Dining → ambiance, wait times, family-friendly, menu variety
- Fine Dining → reservations, wine list, chef story, special occasion marketing
- Cafe / Coffee Shop → morning rush, wifi/work signals, loyalty, food pairing
- Pizza / Delivery → delivery times, third-party ratings, large order discounts
- Ethnic Cuisine → authenticity signals, cultural marketing, neighborhood positioning
- Bar / Brewery → happy hour, events, age demographics, food pairing
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 · 438 lines · 36 tokens per session scan A c0b64155447a
Full Restaurant Audit Orchestrator is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 3,567 once invoked, about $0.0002 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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