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-reviewsgit 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-reviews)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-reviews"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-reviews/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-reviews"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-reviews.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 | $0.00031 | $0.01448 |
| Opus 5 | $0.00015 | $0.00724 |
| Sonnet 5 | $0.00006 | $0.00290 |
| Haiku 4.5 | $0.00003 | $0.00145 |
Grade A, and why
restaurant-reviews 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Restaurant Reviews Analysis
You analyze a restaurant's review profile across all major platforms — Google, Yelp, TripAdvisor, OpenTable, and third-party delivery apps — and produce a structured report identifying recurring complaints, recurring praises, owner response performance, and rating trajectory over time.
DISCLAIMER: AI-generated analysis based on publicly available reviews. Always verify with the restaurant owner.
When to use
/restaurant reviews <name>— full multi-platform review breakdown- "analyze reviews for [name]"
- "what are people saying about [name]"
Execution Pipeline
Step 1: Locate Listings
WebSearch("[name] [city] google reviews")
WebSearch("[name] [city] yelp")
WebSearch("[name] [city] tripadvisor")
WebSearch("[name] [city] doordash ubereats grubhub reviews")
Capture for each platform:
- URL
- Star rating
- Review count
- Owner verified Y/N
- Last owner response date
Step 2: Read Recent Reviews
Read the 20 most recent reviews on each platform (last 90 days where possible). Categorize each as:
- Positive (4-5 stars)
- Neutral (3 stars)
- Negative (1-2 stars)
Step 3: Theme Extraction
Build two lists by tallying common phrases:
Top 5 Recurring Complaints — categories like:
- Slow service / long wait
- Cold food / quality issues
- Pricing / portion size
- Noise level / atmosphere
- Parking / location
- Staff attitude
- Cleanliness
- Online ordering / delivery problems
- Bathroom / facilities
- Reservation / wait list
Top 5 Recurring Praises:
- Specific dish names (signature items)
- Specific staff names (servers, chef, host)
- Atmosphere / ambiance
- Value
- Service speed (positive)
- Authenticity / quality
Step 4: Owner Response Analysis
For the last 50 reviews across platforms:
- % of negative reviews with owner reply
- % of positive reviews with owner reply (yes, this matters)
- Average response time (days)
- Response quality: templated vs personalized vs absent
Industry benchmark: top-decile restaurants respond to 100% of negative reviews within 48 hours.
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 · 192 lines · 31 tokens per session scan A 1e8388a9eb8d
restaurant-reviews is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 1,448 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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