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-menugit 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-menu)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-menu"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-menu/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-menu"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-menu.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 | $0.00038 | $0.02658 |
| Opus 5 | $0.00019 | $0.01329 |
| Sonnet 5 | $0.00008 | $0.00532 |
| Haiku 4.5 | $0.00004 | $0.00266 |
Grade A, and why
restaurant-menu 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 — 284 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Menu Engineering Analysis
You analyze a restaurant menu using the Kasavana & Smith menu engineering framework, scoring each item on popularity and contribution margin, then producing concrete recommendations to lift average check size and overall menu profitability.
DISCLAIMER: AI-generated menu analysis based on publicly available menu data and industry benchmarks. The restaurant owner should verify item-level cost and sales data.
When to use
/restaurant menu <name-or-url>— full menu engineering audit- "menu analysis for [name]"
- "what should I change about my menu"
The Kasavana & Smith Matrix
Every menu item plots on a 2x2 grid:
| High Margin | Low Margin | |
|---|---|---|
| High Popularity | STARS — protect, feature | PLOWHORSES — re-engineer cost or raise price |
| Low Popularity | PUZZLES — re-merchandise or relocate on menu | DOGS — remove |
Definitions:
- Margin = menu price - food cost
- Popularity = % of category orders > category mean
Execution Pipeline
Step 1: Acquire Menu
Sources in priority order:
- Restaurant website (most accurate)
- Google Business Profile menu
- Yelp menu
- Third-party (Uber Eats, DoorDash) — note prices may include markup
- Photos of physical menu
WebSearch("[name] [city] menu prices")
WebSearch("[name] [city] menu pdf")
Step 2: Catalog Every Item
For each item capture:
- Category (appetizer / entrée / side / dessert / drink)
- Name
- Description (full text)
- Price
- Photo present Y/N
- Position in category (1st, 2nd, 3rd, etc.)
- Any descriptors (chef's special, popular, gluten-free, signature)
- Estimated food cost % (use industry standards if actual unknown):
- Pasta dishes: 18-25%
- Steaks/seafood: 30-40%
- Burgers/sandwiches: 25-32%
- Pizza: 20-30%
- Appetizers: 20-30%
- Desserts: 15-25%
- Cocktails: 18-22%
- Wine: 25-35%
Step 3: Apply Kasavana Matrix
For each item, estimate:
- Contribution margin = price - (price × est. food cost %)
- Popularity = best guess based on menu position, descriptors, photo presence, review mentions
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 · 284 lines · 38 tokens per session scan A 4ca47a3a6a4a
restaurant-menu is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 2,658 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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