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-pricinggit 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-pricing)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-pricing"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-pricing/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-pricing"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-pricing.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 | $0.00029 | $0.02085 |
| Opus 5 | $0.00015 | $0.01043 |
| Sonnet 5 | $0.00006 | $0.00417 |
| Haiku 4.5 | $0.00003 | $0.00209 |
Grade A, and why
restaurant-pricing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Pricing Analysis
You compare a restaurant's menu prices against direct local competitors of the same cuisine and price tier, then surface specific items that are underpriced (leaving margin on the table) or overpriced (hurting volume).
DISCLAIMER: AI-generated competitive pricing analysis. The restaurant owner should verify with full P&L data before changing prices.
When to use
/restaurant pricing <name>— full competitive pricing comparison- "are my prices right"
- "how does my pricing compare"
Execution Pipeline
Step 1: Identify Direct Competitors
Find 5-7 restaurants within 3 miles that match:
- Same cuisine
- Same price tier ($, $$, $$$, $$$$)
- Similar concept (casual / fine / fast-casual)
WebSearch("best [cuisine] near [neighborhood] [city]")
WebSearch("[cuisine] restaurants similar to [subject name] [city]")
Step 2: Capture Competitor Menus
For each competitor, capture pricing on:
- 3-5 signature / most-ordered dishes
- A common reference dish present at most concepts (e.g., for Italian: spaghetti & meatballs, margherita pizza, caesar salad)
- Appetizer price range (min/avg/max)
- Entrée price range
- Dessert price range
- Glass of house wine / beer / cocktail prices
Step 3: Build Pricing Comparison Matrix
For each subject menu item, compare to competitor equivalents:
| Subject Item | Subject Price | Comp Avg | Comp Range | Position |
|---|---|---|---|---|
| Margherita Pizza | $16 | $18.50 | $15-$22 | Underpriced |
| Lasagna | $17 | $19.20 | $17-$22 | Slightly under |
| Carbonara | $19 | $18.40 | $16-$21 | At market |
| Ribeye | $42 | $48.00 | $42-$58 | Underpriced |
| Tiramisu | $11 | $9.50 | $8-$11 | Overpriced |
Step 4: Identify Underpriced Items (Leaving Money on Table)
Items more than 8% below competitor average WITHOUT a deliberate value-positioning reason. For each:
- Recommend new price
- Calculate revenue lift at current volume
- Consider price elasticity risk
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 · 214 lines · 29 tokens per session scan A 7a75f578ccb0
restaurant-pricing is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 2,085 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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